cg

annotate grant.bib @ 102:4cca7c7d91d1

.
author bshanks@bshanks.dyndns.org
date Wed Apr 22 07:09:37 2009 -0700 (16 years ago)
parents e460569c21d4
children

rev   line source
bshanks@81 1 
bshanks@81 2 @misc{_zotero_????,
bshanks@81 3 title = {Zotero - Quick Start Guide},
bshanks@81 4 url = {http://www.zotero.org/documentation/quick_start_guide}
bshanks@81 5 },
bshanks@81 6
bshanks@81 7 @article{chin_genome-scale_2007,
bshanks@81 8 title = {A genome-scale map of expression for a mouse brain section obtained using voxelation},
bshanks@81 9 volume = {30},
bshanks@81 10 url = {http://physiolgenomics.physiology.org/cgi/content/abstract/30/3/313},
bshanks@81 11 doi = {10.1152/physiolgenomics.00287.2006},
bshanks@81 12 abstract = {Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative {RT-PCR} voxelation, mass spectrometry voxelation, and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genes with unexpected patterns were identified, and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.},
bshanks@81 13 number = {3},
bshanks@81 14 journal = {Physiol. Genomics},
bshanks@81 15 author = {Mark H. Chin and Alex B. Geng and Arshad H. Khan and {Wei-Jun} Qian and Vladislav A. Petyuk and Jyl Boline and Shawn Levy and Arthur W. Toga and Richard D. Smith and Richard M. Leahy and Desmond J. Smith},
bshanks@81 16 month = aug,
bshanks@81 17 year = {2007},
bshanks@81 18 pages = {313--321}
bshanks@81 19 },
bshanks@81 20
bshanks@81 21 @article{lein_genome-wide_2007,
bshanks@81 22 title = {Genome-wide atlas of gene expression in the adult mouse brain},
bshanks@81 23 volume = {445},
bshanks@81 24 issn = {0028-0836},
bshanks@81 25 url = {http://dx.doi.org/10.1038/nature05453},
bshanks@81 26 doi = {10.1038/nature05453},
bshanks@81 27 number = {7124},
bshanks@81 28 journal = {Nature},
bshanks@81 29 author = {Ed S. Lein and Michael J. Hawrylycz and Nancy Ao and Mikael Ayres and Amy Bensinger and Amy Bernard and Andrew F. Boe and Mark S. Boguski and Kevin S. Brockway and Emi J. Byrnes and Lin Chen and Li Chen and {Tsuey-Ming} Chen and Mei Chi Chin and Jimmy Chong and Brian E. Crook and Aneta Czaplinska and Chinh N. Dang and Suvro Datta and Nick R. Dee and Aimee L. Desaki and Tsega Desta and Ellen Diep and Tim A. Dolbeare and Matthew J. Donelan and {Hong-Wei} Dong and Jennifer G. Dougherty and Ben J. Duncan and Amanda J. Ebbert and Gregor Eichele and Lili K. Estin and Casey Faber and Benjamin A. Facer and Rick Fields and Shanna R. Fischer and Tim P. Fliss and Cliff Frensley and Sabrina N. Gates and Katie J. Glattfelder and Kevin R. Halverson and Matthew R. Hart and John G. Hohmann and Maureen P. Howell and Darren P. Jeung and Rebecca A. Johnson and Patrick T. Karr and Reena Kawal and Jolene M. Kidney and Rachel H. Knapik and Chihchau L. Kuan and James H. Lake and Annabel R. Laramee and Kirk D. Larsen and Christopher Lau and Tracy A. Lemon and Agnes J. Liang and Ying Liu and Lon T. Luong and Jesse Michaels and Judith J. Morgan and Rebecca J. Morgan and Marty T. Mortrud and Nerick F. Mosqueda and Lydia L. Ng and Randy Ng and Geralyn J. Orta and Caroline C. Overly and Tu H. Pak and Sheana E. Parry and Sayan D. Pathak and Owen C. Pearson and Ralph B. Puchalski and Zackery L. Riley and Hannah R. Rockett and Stephen A. Rowland and Joshua J. Royall and Marcos J. Ruiz and Nadia R. Sarno and Katherine Schaffnit and Nadiya V. Shapovalova and Taz Sivisay and Clifford R. Slaughterbeck and Simon C. Smith and Kimberly A. Smith and Bryan I. Smith and Andy J. Sodt and Nick N. Stewart and {Kenda-Ruth} Stumpf and Susan M. Sunkin and Madhavi Sutram and Angelene Tam and Carey D. Teemer and Christina Thaller and Carol L. Thompson and Lee R. Varnam and Axel Visel and Ray M. Whitlock and Paul E. Wohnoutka and Crissa K. Wolkey and Victoria Y. Wong and Matthew Wood and Murat B. Yaylaoglu and Rob C. Young and Brian L. Youngstrom and Xu Feng Yuan and Bin Zhang and Theresa A. Zwingman and Allan R. Jones},
bshanks@81 30 year = {2007},
bshanks@81 31 pages = {168--176}
bshanks@81 32 },
bshanks@81 33
bshanks@81 34 @article{ng_anatomic_2009,
bshanks@81 35 title = {An anatomic gene expression atlas of the adult mouse brain},
bshanks@81 36 volume = {12},
bshanks@81 37 issn = {1097-6256},
bshanks@81 38 url = {http://dx.doi.org/10.1038/nn.2281},
bshanks@81 39 doi = {10.1038/nn.2281},
bshanks@81 40 number = {3},
bshanks@81 41 journal = {Nat Neurosci},
bshanks@81 42 author = {Lydia Ng and Amy Bernard and Chris Lau and Caroline C Overly and {Hong-Wei} Dong and Chihchau Kuan and Sayan Pathak and Susan M Sunkin and Chinh Dang and Jason W Bohland and Hemant Bokil and Partha P Mitra and Luis Puelles and John Hohmann and David J Anderson and Ed S Lein and Allan R Jones and Michael Hawrylycz},
bshanks@81 43 month = mar,
bshanks@81 44 year = {2009},
bshanks@81 45 pages = {356--362}
bshanks@81 46 },
bshanks@81 47
bshanks@81 48 @article{thompson_genomic_2008,
bshanks@81 49 title = {Genomic Anatomy of the Hippocampus},
bshanks@81 50 volume = {60},
bshanks@81 51 issn = {0896-6273},
bshanks@81 52 url = {http://www.sciencedirect.com/science/article/B6WSS-4V70FHY-9/2/a4de532b1bf60f0d033eacad345b935e},
bshanks@81 53 doi = {10.1016/j.neuron.2008.12.008},
bshanks@81 54 abstract = {Summary
bshanks@81 55 Availability of genome-scale in situ hybridization data allows systematic analysis of genetic neuroanatomical architecture. Within the hippocampus, electrophysiology and lesion and imaging studies demonstrate functional heterogeneity along the septotemporal axis, although precise underlying circuitry and molecular substrates remain uncharacterized. Application of unbiased statistical component analyses to genome-scale hippocampal gene expression data revealed robust septotemporal molecular heterogeneity, leading to the identification of a large cohort of genes with robust regionalized hippocampal expression. Manual mapping of heterogeneous {CA3} pyramidal neuron expression patterns demonstrates an unexpectedly complex molecular parcellation into a relatively coherent set of nine expression domains in the septal/temporal and proximal/distal axes with reciprocal, nonoverlapping boundaries. Unique combinatorial profiles of adhesion molecules within these domains suggest corresponding differential connectivity, which is demonstrated for {CA3} projections to the lateral septum using retrograde labeling. This complex, discrete molecular architecture provides a novel paradigm for predicting functional differentiation across the full septotemporal extent of the hippocampus.},
bshanks@81 56 number = {6},
bshanks@81 57 journal = {Neuron},
bshanks@81 58 author = {Carol L. Thompson and Sayan D. Pathak and Andreas Jeromin and Lydia L. Ng and Cameron R. {MacPherson} and Marty T. Mortrud and Allison Cusick and Zackery L. Riley and Susan M. Sunkin and Amy Bernard and Ralph B. Puchalski and Fred H. Gage and Allan R. Jones and Vladimir B. Bajic and Michael J. Hawrylycz and Ed S. Lein},
bshanks@81 59 month = dec,
bshanks@81 60 year = {2008},
bshanks@81 61 keywords = {{MOLNEURO,SYSBIO,SYSNEURO}},
bshanks@81 62 pages = {1010--1021}
bshanks@81 63 },
bshanks@81 64
bshanks@81 65 @inproceedings{shanks_wikigateway:library_2005,
bshanks@81 66 address = {San Diego, California},
bshanks@81 67 title = {{WikiGateway:} a library for interoperability and accelerated wiki development},
bshanks@81 68 isbn = {1-59593-111-2},
bshanks@81 69 url = {http://portal.acm.org/citation.cfm?id=1104973.1104979},
bshanks@81 70 doi = {10.1145/1104973.1104979},
bshanks@81 71 abstract = {{WikiGateway} is an open-source suite of tools for automated interaction with wikis:• Python and Perl modules with functions like {getPage,} {putPage,} {getRecentChanges,} and more.• A mechanism to add {DAV,} Atom, or {XMLRPC} capabilities to any supported wiki server.• A command-line tool with functionality similar to the Perl and Python modules.• Demo applications built on top of these tools include a wiki copy command, a spam-cleaning bot, and a tool to recursively upload text files inside a directory structure as wiki {pages.All} {WikiGateway} tools are compatible with a number of different wiki engines. Developers can use {WikiGateway} to hide the differences between wiki engines and build applications which interoperate with many different wiki engines.},
bshanks@81 72 booktitle = {Proceedings of the 2005 international symposium on Wikis},
bshanks@81 73 publisher = {{ACM}},
bshanks@81 74 author = {Bayle Shanks},
bshanks@81 75 year = {2005},
bshanks@96 76 keywords = {atom,client-side wiki,interoperability,interwiki,middleware,webdav,wiki,wikiclient,wikigateway,wikirpcinterface,wiki xmlrpc},
bshanks@81 77 pages = {53--66}
bshanks@81 78 },
bshanks@81 79
bshanks@81 80 @article{zhang_v3_2008,
bshanks@81 81 title = {V3 Spinal Neurons Establish a Robust and Balanced Locomotor Rhythm during Walking},
bshanks@81 82 volume = {60},
bshanks@81 83 issn = {0896-6273},
bshanks@81 84 url = {http://www.sciencedirect.com/science/article/B6WSS-4TMK33J-C/2/9281fbcc359d1ad037a368824abbd871},
bshanks@81 85 doi = {10.1016/j.neuron.2008.09.027},
bshanks@81 86 abstract = {Summary
bshanks@81 87 A robust and well-organized rhythm is a key feature of many neuronal networks, including those that regulate essential behaviors such as circadian rhythmogenesis, breathing, and locomotion. Here we show that excitatory V3-derived neurons are necessary for a robust and organized locomotor rhythm during walking. When V3-mediated neurotransmission is selectively blocked by the expression of the tetanus toxin light chain subunit {(TeNT),} the regularity and robustness of the locomotor rhythm is severely perturbed. A similar degeneration in the locomotor rhythm occurs when the excitability of V3-derived neurons is reduced acutely by ligand-induced activation of the allatostatin receptor. The V3-derived neurons additionally function to balance the locomotor output between both halves of the spinal cord, thereby ensuring a symmetrical pattern of locomotor activity during walking. We propose that the V3 neurons establish a regular and balanced motor rhythm by distributing excitatory drive between both halves of the spinal cord.},
bshanks@81 88 number = {1},
bshanks@81 89 journal = {Neuron},
bshanks@81 90 author = {Ying Zhang and Sujatha Narayan and Eric Geiman and Guillermo M. Lanuza and Tomoko Velasquez and Bayle Shanks and Turgay Akay and Jason Dyck and Keir Pearson and Simon Gosgnach and {Chen-Ming} Fan and Martyn Goulding},
bshanks@81 91 month = oct,
bshanks@81 92 year = {2008},
bshanks@81 93 keywords = {{MOLNEURO}},
bshanks@81 94 pages = {84--96}
bshanks@81 95 },
bshanks@81 96
bshanks@81 97 @article{van_essen_integrated_2001,
bshanks@81 98 title = {An integrated software suite for surface-based analyses of cerebral cortex},
bshanks@81 99 volume = {8},
bshanks@81 100 issn = {1067-5027},
bshanks@81 101 url = {http://www.ncbi.nlm.nih.gov/pubmed/11522765},
bshanks@81 102 abstract = {The authors describe and illustrate an integrated trio of software programs for carrying out surface-based analyses of cerebral cortex. The first component of this trio, {SureFit} {(Surface} Reconstruction by Filtering and Intensity Transformations), is used primarily for cortical segmentation, volume visualization, surface generation, and the mapping of functional neuroimaging data onto surfaces. The second component, Caret {(Computerized} Anatomical Reconstruction and Editing Tool Kit), provides a wide range of surface visualization and analysis options as well as capabilities for surface flattening, surface-based deformation, and other surface manipulations. The third component, {SuMS} {(Surface} Management System), is a database and associated user interface for surface-related data. It provides for efficient insertion, searching, and extraction of surface and volume data from the database.},
bshanks@81 103 number = {5},
bshanks@81 104 journal = {Journal of the American Medical Informatics Association: {JAMIA}},
bshanks@81 105 author = {D C Van Essen and H A Drury and J Dickson and J Harwell and D Hanlon and C H Anderson},
bshanks@81 106 year = {2001},
bshanks@81 107 note = {{PMID:} 11522765},
bshanks@81 108 keywords = {Anatomy, {Artistic,Anatomy,} {Cross-Sectional,Brain,Cerebral} {Cortex,Databases,} {Factual,Humans,Image} Processing, {Computer-Assisted,Magnetic} Resonance {Imaging,Medical} {Illustration,Neuroanatomy,Software,Systems} Integration},
bshanks@81 109 pages = {443--59}
bshanks@81 110 },
bshanks@81 111
bshanks@81 112 @misc{sonnenburg_large_2006,
bshanks@81 113 type = {Article},
bshanks@81 114 title = {Large scale multiple kernel learning},
bshanks@81 115 url = {http://eprints.pascal-network.org/archive/00003035/},
bshanks@81 116 author = {Sören Sonnenburg and Gunnar Raetsch and Christin Schaefer and Bernhard Schölkopf},
bshanks@81 117 year = {2006},
bshanks@81 118 note = {While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard {SVM} implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm works for hundred thousands of examples or hundreds of kernels to be combined, and helps for automatic model selection, improving the interpretability of the learning result. In a second part we discuss general speed up mechanism for {SVMs,} especially when used with sparse feature maps as appear for string kernels, allowing us to train a string kernel {SVM} on a 10 million real-world splice data set from computational biology. We integrated multiple kernel learning in our machine learning toolbox {SHOGUN} for which the source code is publicly available at http://www.fml.tuebingen.mpg.de/raetsch/projects/shogun.},
bshanks@81 119 keywords = {{Learning/Statistics} \& {Optimisation,Multimodal} {Integration,Theory} \& Algorithms},
bshanks@81 120 howpublished = {http://eprints.pascal-network.org/archive/00003035/}
bshanks@81 121 },
bshanks@81 122
bshanks@81 123 @book{swanson_brain_2003,
bshanks@81 124 edition = {3},
bshanks@81 125 title = {Brain Maps: Structure of the Rat Brain},
bshanks@81 126 isbn = {0126105820},
bshanks@81 127 publisher = {Academic Press},
bshanks@81 128 author = {Larry Swanson},
bshanks@81 129 month = nov,
bshanks@81 130 year = {2003}
bshanks@81 131 },
bshanks@81 132
bshanks@81 133 @book{paxinos_mouse_2001,
bshanks@81 134 edition = {2},
bshanks@81 135 title = {The Mouse Brain in Stereotaxic Coordinates},
bshanks@81 136 isbn = {{012547637X}},
bshanks@81 137 publisher = {Academic Press},
bshanks@81 138 author = {George Paxinos and Keith {B.J.} Franklin},
bshanks@81 139 month = jul,
bshanks@81 140 year = {2001}
bshanks@81 141 },
bshanks@81 142
bshanks@81 143 @article{waterston_initial_2002,
bshanks@81 144 title = {Initial sequencing and comparative analysis of the mouse genome},
bshanks@81 145 volume = {420},
bshanks@81 146 issn = {0028-0836},
bshanks@81 147 url = {http://www.ncbi.nlm.nih.gov/pubmed/12466850},
bshanks@81 148 doi = {10.1038/nature01262},
bshanks@81 149 abstract = {The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.},
bshanks@81 150 number = {6915},
bshanks@81 151 journal = {Nature},
bshanks@81 152 author = {Robert H Waterston and Kerstin {Lindblad-Toh} and Ewan Birney and Jane Rogers and Josep F Abril and Pankaj Agarwal and Richa Agarwala and Rachel Ainscough and Marina Alexandersson and Peter An and Stylianos E Antonarakis and John Attwood and Robert Baertsch and Jonathon Bailey and Karen Barlow and Stephan Beck and Eric Berry and Bruce Birren and Toby Bloom and Peer Bork and Marc Botcherby and Nicolas Bray and Michael R Brent and Daniel G Brown and Stephen D Brown and Carol Bult and John Burton and Jonathan Butler and Robert D Campbell and Piero Carninci and Simon Cawley and Francesca Chiaromonte and Asif T Chinwalla and Deanna M Church and Michele Clamp and Christopher Clee and Francis S Collins and Lisa L Cook and Richard R Copley and Alan Coulson and Olivier Couronne and James Cuff and Val Curwen and Tim Cutts and Mark Daly and Robert David and Joy Davies and Kimberly D Delehaunty and Justin Deri and Emmanouil T Dermitzakis and Colin Dewey and Nicholas J Dickens and Mark Diekhans and Sheila Dodge and Inna Dubchak and Diane M Dunn and Sean R Eddy and Laura Elnitski and Richard D Emes and Pallavi Eswara and Eduardo Eyras and Adam Felsenfeld and Ginger A Fewell and Paul Flicek and Karen Foley and Wayne N Frankel and Lucinda A Fulton and Robert S Fulton and Terrence S Furey and Diane Gage and Richard A Gibbs and Gustavo Glusman and Sante Gnerre and Nick Goldman and Leo Goodstadt and Darren Grafham and Tina A Graves and Eric D Green and Simon Gregory and Roderic Guigó and Mark Guyer and Ross C Hardison and David Haussler and Yoshihide Hayashizaki and {LaDeana} W Hillier and Angela Hinrichs and Wratko Hlavina and Timothy Holzer and Fan Hsu and Axin Hua and Tim Hubbard and Adrienne Hunt and Ian Jackson and David B Jaffe and L Steven Johnson and Matthew Jones and Thomas A Jones and Ann Joy and Michael Kamal and Elinor K Karlsson and Donna Karolchik and Arkadiusz Kasprzyk and Jun Kawai and Evan Keibler and Cristyn Kells and W James Kent and Andrew Kirby and Diana L Kolbe and Ian Korf and Raju S Kucherlapati and Edward J Kulbokas and David Kulp and Tom Landers and J P Leger and Steven Leonard and Ivica Letunic and Rosie Levine and Jia Li and Ming Li and Christine Lloyd and Susan Lucas and Bin Ma and Donna R Maglott and Elaine R Mardis and Lucy Matthews and Evan Mauceli and John H Mayer and Megan {McCarthy} and W Richard {McCombie} and Stuart {McLaren} and Kirsten {McLay} and John D {McPherson} and Jim Meldrim and Beverley Meredith and Jill P Mesirov and Webb Miller and Tracie L Miner and Emmanuel Mongin and Kate T Montgomery and Michael Morgan and Richard Mott and James C Mullikin and Donna M Muzny and William E Nash and Joanne O Nelson and Michael N Nhan and Robert Nicol and Zemin Ning and Chad Nusbaum and Michael J {O'Connor} and Yasushi Okazaki and Karen Oliver and Emma {Overton-Larty} and Lior Pachter and Genís Parra and Kymberlie H Pepin and Jane Peterson and Pavel Pevzner and Robert Plumb and Craig S Pohl and Alex Poliakov and Tracy C Ponce and Chris P Ponting and Simon Potter and Michael Quail and Alexandre Reymond and Bruce A Roe and Krishna M Roskin and Edward M Rubin and Alistair G Rust and Ralph Santos and Victor Sapojnikov and Brian Schultz and Jörg Schultz and Matthias S Schwartz and Scott Schwartz and Carol Scott and Steven Seaman and Steve Searle and Ted Sharpe and Andrew Sheridan and Ratna Shownkeen and Sarah Sims and Jonathan B Singer and Guy Slater and Arian Smit and Douglas R Smith and Brian Spencer and Arne Stabenau and Nicole {Stange-Thomann} and Charles Sugnet and Mikita Suyama and Glenn Tesler and Johanna Thompson and David Torrents and Evanne Trevaskis and John Tromp and Catherine Ucla and Abel {Ureta-Vidal} and Jade P Vinson and Andrew C Von Niederhausern and Claire M Wade and Melanie Wall and Ryan J Weber and Robert B Weiss and Michael C Wendl and Anthony P West and Kris Wetterstrand and Raymond Wheeler and Simon Whelan and Jamey Wierzbowski and David Willey and Sophie Williams and Richard K Wilson and Eitan Winter and Kim C Worley and Dudley Wyman and Shan Yang and {Shiaw-Pyng} Yang and Evgeny M Zdobnov and Michael C Zody and Eric S Lander},
bshanks@81 153 month = dec,
bshanks@81 154 year = {2002},
bshanks@81 155 note = {{PMID:} 12466850},
bshanks@81 156 keywords = {{Animals,Base} {Composition,Chromosomes,} {Mammalian,Conserved} {Sequence,CpG} {Islands,Evolution,} {Molecular,Gene} Expression {Regulation,Genes,Genetic} {Variation,Genome,Genome,} {Human,Genomics,Humans,Mice,Mice,} {Knockout,Mice,} {Transgenic,Models,} {Animal,Multigene} {Family,Mutagenesis,Neoplasms,Physical} Chromosome {Mapping,Proteome,Pseudogenes,Quantitative} Trait {Loci,Repetitive} Sequences, Nucleic {Acid,RNA,} {Untranslated,Selection} {(Genetics),Sequence} Analysis, {DNA,Sex} {Chromosomes,Species} {Specificity,Synteny}},
bshanks@81 157 pages = {520--62}
bshanks@81 158 },
bshanks@81 159
bshanks@81 160 @article{curwen_ensembl_2004,
bshanks@81 161 title = {The Ensembl Automatic Gene Annotation System},
bshanks@81 162 volume = {14},
bshanks@81 163 url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=479124},
bshanks@81 164 doi = {10.1101/gr.1858004},
bshanks@81 165 number = {5},
bshanks@81 166 journal = {Genome Research},
bshanks@81 167 author = {Val Curwen and Eduardo Eyras and T. Daniel Andrews and Laura Clarke and Emmanuel Mongin and Steven {M.J.} Searle and Michele Clamp},
bshanks@81 168 month = may,
bshanks@81 169 year = {2004},
bshanks@81 170 note = {{PMC479124}},
bshanks@81 171 pages = {942–950}
bshanks@81 172 },
bshanks@81 173
bshanks@81 174 @article{gong_gene_2003,
bshanks@81 175 title = {A gene expression atlas of the central nervous system based on bacterial artificial chromosomes},
bshanks@81 176 volume = {425},
bshanks@81 177 issn = {0028-0836},
bshanks@81 178 url = {http://dx.doi.org/10.1038/nature02033},
bshanks@81 179 doi = {10.1038/nature02033},
bshanks@81 180 number = {6961},
bshanks@81 181 journal = {Nature},
bshanks@81 182 author = {Shiaoching Gong and Chen Zheng and Martin L. Doughty and Kasia Losos and Nicholas Didkovsky and Uta B. Schambra and Norma J. Nowak and Alexandra Joyner and Gabrielle Leblanc and Mary E. Hatten and Nathaniel Heintz},
bshanks@81 183 month = oct,
bshanks@81 184 year = {2003},
bshanks@81 185 pages = {917--925}
bshanks@81 186 },
bshanks@81 187
bshanks@81 188 @article{visel_genepaint.org:atlas_2004,
bshanks@81 189 title = {{GenePaint.org:} an atlas of gene expression patterns in the mouse embryo},
bshanks@81 190 volume = {32},
bshanks@81 191 url = {http://nar.oxfordjournals.org/cgi/content/abstract/32/suppl_1/D552},
bshanks@81 192 doi = {10.1093/nar/gkh029},
bshanks@81 193 abstract = {High-throughput instruments were recently developed to determine gene expression patterns on tissue sections by {RNA} in situ hybridization. The resulting images of gene expression patterns, chiefly of E14.5 mouse embryos, are accessible to the public at http://www.genepaint.org. This relational database is searchable for gene identifiers and {RNA} probe sequences. Moreover, patterns and intensity of expression in [{\textasciitilde}]100 different embryonic tissues are annotated and can be searched using a standardized catalog of anatomical structures. A virtual microscope tool, the Zoom Image Server, was implemented in {GenePaint.org} and permits interactive zooming and panning across [{\textasciitilde}]15 000 high-resolution images.},
bshanks@81 194 number = {suppl\_1},
bshanks@81 195 journal = {Nucl. Acids Res.},
bshanks@81 196 author = {Axel Visel and Christina Thaller and Gregor Eichele},
bshanks@81 197 year = {2004},
bshanks@81 198 pages = {D552--556}
bshanks@81 199 },
bshanks@81 200
bshanks@81 201 @article{magdaleno_bgem:in_2006,
bshanks@81 202 title = {{BGEM:} An In Situ Hybridization Database of Gene Expression in the Embryonic and Adult Mouse Nervous System},
bshanks@81 203 volume = {4},
bshanks@81 204 url = {http://dx.doi.org/10.1371%2Fjournal.pbio.0040086},
bshanks@81 205 doi = {10.1371/journal.pbio.0040086},
bshanks@81 206 number = {4},
bshanks@81 207 journal = {{PLoS} Biology},
bshanks@81 208 author = {Susan Magdaleno and Patricia Jensen and Craig L. Brumwell and Anna Seal and Karen Lehman and Andrew Asbury and Tony Cheung and Tommie Cornelius and Diana M. Batten and Christopher Eden and Shannon M. Norland and Dennis S. Rice and Nilesh Dosooye and Sundeep Shakya and Perdeep Mehta and Tom Curran},
bshanks@81 209 month = apr,
bshanks@81 210 year = {2006},
bshanks@81 211 pages = {e86 {EP} --}
bshanks@81 212 },
bshanks@81 213
bshanks@81 214 @inproceedings{carson_data_2005,
bshanks@81 215 title = {Data Mining in situ gene expression patterns at cellular resolution},
bshanks@81 216 doi = {{10.1109/CSBW.2005.49}},
bshanks@81 217 abstract = {Non-radioactive in situ hybridization {(ISH)} is a powerful technique for revealing gene expression in individual cells, the level of detail necessary for investigating how genes control cell type identity, cell differentiation, and cell-cell signaling. Although the availability of robotic {ISH} enables the expeditious determination of expression patterns for thousands of genes in serially sectioned tissues, a large collection of {ISH} images is, per se, of limited benefit. However, via accurate detection of expression strength and spatial normalization of expression location across different specimens, {ISH} images become a minable resource of annotated gene expression capable of advancing functional genomics in a mode similar to {DNA} sequence databases. We have developed computational methods to automate robotic {ISH} image annotation and applied these to over 200 genes throughout the postnatal mouse brain. Gene expression strengths were quantified for each cell tissue section images, and these images were subjected to atlas-based segmentation using a series of subdivision mesh maps that comprise our atlas of the postnatal mouse brain. With this common geometric representation of gene expression, patterns are automatically annotated and spatial searches success fully find the genes expressed in a similar fashion to custom query patterns. Cluster analysis of spatially normalized expression patterns identifies potential relationships in gene networks. Annotated gene expression patterns and query interfaces are publicly accessible at wv/w. geneattas. org.},
bshanks@81 218 booktitle = {Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. {IEEE}},
bshanks@81 219 author = {J. Carson and T. Ju and C. Thaller and M. Bello and I. Kakadiaris and J. Warren and G. Eichele and W. Chiu},
bshanks@81 220 year = {2005},
bshanks@96 221 keywords = {atlas-based segmentation,automate robotic in situ hybridization image annotation,biological techniques,biological tissues,biology {computing,Brain,cell-cell} signaling,cell differentiation,cellular biophysics,cellular resolution,cluster analysis,data {mining,DNA} sequence database,functional genomics,gene expression pattern,genetics,image classification,image segmentation,mesh maps,pattern clustering,postnatal mouse brain,query interface,statistical analysis,tissue},
bshanks@81 222 pages = {358}
bshanks@81 223 },
bshanks@81 224
bshanks@81 225 @article{venkataraman_emage_2008,
bshanks@81 226 title = {{EMAGE} Edinburgh Mouse Atlas of Gene Expression: 2008 update},
bshanks@81 227 volume = {36},
bshanks@81 228 url = {http://nar.oxfordjournals.org/cgi/content/abstract/36/suppl_1/D860},
bshanks@81 229 doi = {10.1093/nar/gkm938},
bshanks@81 230 abstract = {{EMAGE} {(http://genex.hgu.mrc.ac.uk/Emage/database)} is a database of in situ gene expression patterns in the developing mouse embryo. Domains of expression from raw data images are spatially integrated into a set of standard {3D} virtual mouse embryos at different stages of development, allowing data interrogation by spatial methods. Sites of expression are also described using an anatomy ontology and data can be queried using text-based methods. Here we describe recent enhancements to {EMAGE} which include advances in spatial search methods including: a refined local spatial similarity search algorithm, a method to allow global spatial comparison of patterns in {EMAGE} and subsequent hierarchical-clustering, and spatial searches across multiple stages of development. In addition, we have extended data access by the introduction of web services and new {HTML-based} search interfaces, which allow access to data that has not yet been spatially annotated. We have also started incorporating full {3D} images of gene expression that have been generated using optical projection tomography {(OPT).}},
bshanks@81 231 number = {suppl\_1},
bshanks@81 232 journal = {Nucl. Acids Res.},
bshanks@81 233 author = {Shanmugasundaram Venkataraman and Peter Stevenson and Yiya Yang and Lorna Richardson and Nicholas Burton and Thomas P. Perry and Paul Smith and Richard A. Baldock and Duncan R. Davidson and Jeffrey H. Christiansen},
bshanks@81 234 year = {2008},
bshanks@81 235 pages = {D860--865}
bshanks@81 236 },
bshanks@81 237
bshanks@81 238 @inbook{hemert_matching_2008,
bshanks@81 239 series = {Communications in Computer and Information Science},
bshanks@81 240 title = {Matching Spatial Regions with Combinations of Interacting Gene Expression Patterns},
bshanks@81 241 volume = {13},
bshanks@81 242 url = {http://dx.doi.org/10.1007/978-3-540-70600-7_26},
bshanks@81 243 abstract = {The Edinburgh Mouse Atlas aims to capture in-situ gene expression patterns in a common spatial framework. In this study, we construct a grammar to define spatial regions by
bshanks@81 244 combinations of these patterns. Combinations are formed by applying operators to curated gene expression patterns from the
bshanks@81 245 atlas, thereby resembling gene interactions in a spatial context. The space of combinations is searched using an evolutionary
bshanks@81 246 algorithm with the objective of finding the best match to a given target pattern. We evaluate the method by testing its robustness
bshanks@81 247 and the statistical significance of the results it finds.},
bshanks@81 248 booktitle = {Bioinformatics Research and Development},
bshanks@81 249 publisher = {Springer Berlin Heidelberg},
bshanks@81 250 author = {Jano Hemert and Richard Baldock},
bshanks@81 251 year = {2008},
bshanks@81 252 pages = {347--361}
bshanks@81 253 },
bshanks@81 254
bshanks@81 255 @inbook{van_hemert_mining_2007,
bshanks@81 256 series = {Lecture Notes in Computer Science},
bshanks@81 257 title = {Mining Spatial Gene Expression Data for Association Rules},
bshanks@81 258 volume = {4414/2007},
bshanks@81 259 isbn = {978-3-540-71232-9},
bshanks@81 260 url = {http://dx.doi.org/10.1007/978-3-540-71233-6_6},
bshanks@81 261 abstract = {We analyse data from the Edinburgh Mouse Atlas {Gene-Expression} Database {(EMAGE)} which is a high quality data source for spatio-temporal
bshanks@81 262 gene expression patterns. Using a novel process whereby generated patterns are used to probe spatially-mapped gene expression
bshanks@81 263 domains, we are able to get unbiased results as opposed to using annotations based predefined anatomy regions. We describe
bshanks@81 264 two processes to form association rules based on spatial configurations, one that associates spatial regions, the other associates
bshanks@81 265 genes.},
bshanks@81 266 booktitle = {Bioinformatics Research and Development},
bshanks@81 267 publisher = {Springer Berlin / Heidelberg},
bshanks@81 268 author = {Jano van Hemert and Richard Baldock},
bshanks@81 269 year = {2007},
bshanks@81 270 pages = {66--76}
bshanks@81 271 },
bshanks@81 272
bshanks@81 273 @article{sprague_zebrafish_2006,
bshanks@81 274 title = {The Zebrafish Information Network: the zebrafish model organism database},
bshanks@81 275 volume = {34},
bshanks@81 276 issn = {1362-4962},
bshanks@81 277 url = {http://www.ncbi.nlm.nih.gov/pubmed/16381936},
bshanks@81 278 doi = {10.1093/nar/gkj086},
bshanks@81 279 abstract = {The Zebrafish Information Network {(ZFIN;} http://zfin.org) is a web based community resource that implements the curation of zebrafish genetic, genomic and developmental data. {ZFIN} provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community resources such as meeting announcements and contact information. Recent enhancements to {ZFIN} include (i) comprehensive curation of gene expression data from the literature and from directly submitted data, (ii) increased support and annotation of the genome sequence, (iii) expanded use of ontologies to support curation and query forms, (iv) curation of morpholino data from the literature, and (v) increased versatility of gene pages, with new data types, links and analysis tools.},
bshanks@81 280 number = {Database issue},
bshanks@81 281 journal = {Nucleic Acids Research},
bshanks@81 282 author = {Judy Sprague and Leyla Bayraktaroglu and Dave Clements and Tom Conlin and David Fashena and Ken Frazer and Melissa Haendel and Douglas G Howe and Prita Mani and Sridhar Ramachandran and Kevin Schaper and Erik Segerdell and Peiran Song and Brock Sprunger and Sierra Taylor and Ceri E Van Slyke and Monte Westerfield},
bshanks@81 283 year = {2006},
bshanks@81 284 note = {{PMID:} 16381936},
bshanks@81 285 keywords = {{Animals,Databases,} {Genetic,Gene} {Expression,Genomics,Internet,Models,} {Animal,Oligonucleotides,} {Antisense,Systems} {Integration,User-Computer} {Interface,Vocabulary,} {Controlled,Zebrafish,Zebrafish} Proteins},
bshanks@81 286 pages = {D581--5}
bshanks@81 287 },
bshanks@81 288
bshanks@81 289 @article{bell_geishawhole-mount_2004,
bshanks@81 290 title = {{GEISHA,} a whole-mount in situ hybridization gene expression screen in chicken embryos},
bshanks@81 291 volume = {229},
bshanks@81 292 url = {http://dx.doi.org/10.1002/dvdy.10503},
bshanks@81 293 doi = {10.1002/dvdy.10503},
bshanks@81 294 abstract = {Despite the increasing quality and quantity of genomic sequence that is available to researchers, predicting gene function from sequence information remains a challenge. One method for obtaining rapid insight into potential functional roles of novel genes is through gene expression mapping. We have performed a high throughput whole-mount in situ hybridization {(ISH)} screen with chick embryos to identify novel, differentially expressed genes. Approximately 1,200 5prime expressed sequence tags {(ESTs)} were generated from {cDNA} clones of a Hamburger and Hamilton {(HH)} stage 4-7 (late gastrula) chick embryo endoderm-mesoderm library. After screening to remove ubiquitously expressed {cDNAs} and internal clustering and after comparison to {GenBank} sequences, remaining {cDNAs} (representing both characterized and uncharacterized genes) were screened for expression in {HH} stage 3-14 embryos by automated high throughput {ISH.} Of 786 {cDNAs} for which {ISH} was successfully performed, approximately 30\% showed ubiquitous expression, 40\% were negative, and approximately 30\% showed a restricted expression pattern. {cDNAs} were identified that showed restricted expression in every embryonic region, including the primitive streak, somites, developing cardiovascular system and neural tube/neural crest. A relational database was developed to hold all {EST} sequences, {ISH} images, and corresponding {BLAST} report information, and to enable browsing and querying of data. A user interface is freely accessible at . Results show that high throughput whole-mount {ISH} provides an effective approach for identifying novel genes that are differentially expressed in the developing chicken embryo. Developmental Dynamics 229:677-687, 2004. � 2004 {Wiley-Liss,} Inc.},
bshanks@81 295 number = {3},
bshanks@81 296 journal = {Developmental Dynamics},
bshanks@81 297 author = {George W. Bell and Tatiana A. Yatskievych and Parker B. Antin},
bshanks@81 298 year = {2004},
bshanks@81 299 pages = {677--687}
bshanks@81 300 },
bshanks@81 301
bshanks@81 302 @article{tomancak_systematic_2002,
bshanks@81 303 title = {Systematic determination of patterns of gene expression during Drosophila embryogenesis},
bshanks@81 304 volume = {3},
bshanks@81 305 url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=151190},
bshanks@81 306 doi = {10.1186/gb-2002-3-12-research0088},
bshanks@81 307 number = {12},
bshanks@81 308 journal = {Genome Biology},
bshanks@81 309 author = {Pavel Tomancak and Amy Beaton and Richard Weiszmann and Elaine Kwan and {ShengQiang} Shu and Suzanna E Lewis and Stephen Richards and Michael Ashburner and Volker Hartenstein and Susan E Celniker and Gerald M Rubin},
bshanks@81 310 year = {2002},
bshanks@81 311 note = {{PMC151190}},
bshanks@81 312 pages = {research00881–8814}
bshanks@81 313 },
bshanks@81 314
bshanks@81 315 @article{carson_digital_2005,
bshanks@81 316 title = {A Digital Atlas to Characterize the Mouse Brain Transcriptome},
bshanks@81 317 volume = {1},
bshanks@81 318 url = {http://dx.plos.org/10.1371%2Fjournal.pcbi.0010041},
bshanks@81 319 doi = {10.1371/journal.pcbi.0010041},
bshanks@81 320 abstract = {Massive amounts of data are being generated in an effort to represent for the brain the expression of all genes at cellular resolution. Critical to exploiting this effort is the ability to place these data into a common frame of reference. Here we have developed a computational method for annotating gene expression patterns in the context of a digital atlas to facilitate custom user queries and comparisons of this type of data. This procedure has been applied to 200 genes in the postnatal mouse brain. As an illustration of utility, we identify candidate genes that may be related to Parkinson disease by using the expression of a dopamine transporter in the substantia nigra as a search query pattern. In addition, we discover that transcription factor Rorb is down-regulated in the barrelless mutant relative to control mice by quantitative comparison of expression patterns in layer {IV} somatosensory cortex. The semi-automated annotation method developed here is applicable to a broad spectrum of complex tissues and data modalities.},
bshanks@81 321 number = {4},
bshanks@81 322 journal = {{PLoS} Comput Biol},
bshanks@81 323 author = {James P Carson and Tao Ju and {Hui-Chen} Lu and Christina Thaller and Mei Xu and Sarah L Pallas and Michael C Crair and Joe Warren and Wah Chiu and Gregor Eichele},
bshanks@81 324 year = {2005},
bshanks@81 325 pages = {e41}
bshanks@81 326 },
bshanks@81 327
bshanks@81 328 @article{lee_high-resolution_2007,
bshanks@81 329 title = {A {High-Resolution} Anatomical Framework of the Neonatal Mouse Brain for Managing Gene Expression Data},
bshanks@81 330 volume = {1},
bshanks@81 331 url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2525996},
bshanks@81 332 doi = {10.3389/neuro.11.006.2007},
bshanks@81 333 journal = {Frontiers in Neuroinformatics},
bshanks@81 334 author = {{Erh-Fang} Lee and Jyl Boline and Arthur W. Toga},
bshanks@81 335 year = {2007},
bshanks@81 336 note = {{PMC2525996}},
bshanks@81 337 pages = {6}
bshanks@81 338 },
bshanks@81 339
bshanks@81 340 @article{barrett_ncbi_2007,
bshanks@81 341 title = {{NCBI} {GEO:} mining tens of millions of expression profiles--database and tools update},
bshanks@81 342 volume = {35},
bshanks@81 343 url = {http://nar.oxfordjournals.org/cgi/content/abstract/35/suppl_1/D760},
bshanks@81 344 doi = {10.1093/nar/gkl887},
bshanks@81 345 abstract = {The Gene Expression Omnibus {(GEO)} repository at the National Center for Biotechnology Information {(NCBI)} archives and freely disseminates microarray and other forms of high-throughput data generated by the scientific community. The database has a minimum information about a microarray experiment {(MIAME)-compliant} infrastructure that captures fully annotated raw and processed data. Several data deposit options and formats are supported, including web forms, spreadsheets, {XML} and Simple Omnibus Format in Text {(SOFT).} In addition to data storage, a collection of user-friendly web-based interfaces and applications are available to help users effectively explore, visualize and download the thousands of experiments and tens of millions of gene expression patterns stored in {GEO.} This paper provides a summary of the {GEO} database structure and user facilities, and describes recent enhancements to database design, performance, submission format options, data query and retrieval utilities. {GEO} is accessible at http://www.ncbi.nlm.nih.gov/geo/},
bshanks@81 346 number = {suppl\_1},
bshanks@81 347 journal = {Nucl. Acids Res.},
bshanks@81 348 author = {Tanya Barrett and Dennis B. Troup and Stephen E. Wilhite and Pierre Ledoux and Dmitry Rudnev and Carlos Evangelista and Irene F. Kim and Alexandra Soboleva and Maxim Tomashevsky and Ron Edgar},
bshanks@81 349 year = {2007},
bshanks@81 350 pages = {D760--765}
bshanks@81 351 },
bshanks@81 352
bshanks@81 353 @article{smith_mouse_2007,
bshanks@81 354 title = {The mouse Gene Expression Database {(GXD):} 2007 update},
bshanks@81 355 volume = {35},
bshanks@81 356 url = {http://nar.oxfordjournals.org/cgi/content/abstract/35/suppl_1/D618},
bshanks@81 357 doi = {10.1093/nar/gkl1003},
bshanks@81 358 abstract = {The Gene Expression Database {(GXD)} provides the scientific community with an extensive and easily searchable database of gene expression information about the mouse. Its primary emphasis is on developmental studies. By integrating different types of expression data, {GXD} aims to provide comprehensive information about expression patterns of transcripts and proteins in wild-type and mutant mice. Integration with the other Mouse Genome Informatics {(MGI)} databases places the gene expression information in the context of genetic, sequence, functional and phenotypic information, enabling valuable insights into the molecular biology that underlies developmental and disease processes. In recent years the utility of {GXD} has been greatly enhanced by a large increase in data content, obtained from the literature and provided by researchers doing large-scale in situ and {cDNA} screens. In addition, we have continued to refine our query and display features to make it easier for users to interrogate the data. {GXD} is available through the {MGI} web site at http://www.informatics.jax.org/ or directly at http://www.informatics.jax.org/menus/expression\_menu.shtml.},
bshanks@81 359 number = {suppl\_1},
bshanks@81 360 journal = {Nucl. Acids Res.},
bshanks@81 361 author = {Constance M. Smith and Jacqueline H. Finger and Terry F. Hayamizu and Ingeborg J. {McCright} and Janan T. Eppig and James A. Kadin and Joel E. Richardson and Martin Ringwald},
bshanks@81 362 year = {2007},
bshanks@81 363 pages = {D618--623}
bshanks@85 364 },
bshanks@85 365
bshanks@85 366 @article{annese_myelo-architectonic_2004,
bshanks@85 367 title = {A myelo-architectonic method for the structural classification of cortical areas},
bshanks@85 368 volume = {21},
bshanks@85 369 issn = {1053-8119},
bshanks@85 370 url = {http://www.sciencedirect.com/science/article/B6WNP-4B5JN94-1/2/c9519ed20d3002e0b0316bcf0031e7a2},
bshanks@85 371 doi = {10.1016/j.neuroimage.2003.08.024},
bshanks@85 372 abstract = {We describe an automatic and reproducible method to analyze the histological design of the cerebral cortex as applied to brain sections stained to reveal myelinated fibers. The technique provides an evaluation of the distribution of myelination across the width of the cortical mantle in accordance with a model of its curvature and its intrinsic geometry. The profile lines along which the density of staining is measured are generated from the solution of a partial differential equation {(PDE)} that models the intermediate layers of the cortex. Cortical profiles are classified according to significant components that emerge from wavelet analysis. Intensity profiles belonging to each distinct class are normalized and averaged to produce area-specific templates of cortical myelo-architecture.},
bshanks@85 373 number = {1},
bshanks@85 374 journal = {{NeuroImage}},
bshanks@85 375 author = {J. Annese and A. Pitiot and I. D. Dinov and A. W. Toga},
bshanks@85 376 year = {2004},
bshanks@85 377 keywords = {Cerebral {Cortex,Cortical} {areas,Myelo-architecture}},
bshanks@85 378 pages = {15--26}
bshanks@85 379 },
bshanks@85 380
bshanks@85 381 @article{schleicher_stereological_2000,
bshanks@85 382 title = {A stereological approach to human cortical architecture: identification and delineation of cortical areas},
bshanks@85 383 volume = {20},
bshanks@85 384 issn = {0891-0618},
bshanks@85 385 url = {http://www.sciencedirect.com/science/article/B6T02-43HDYPB-5/2/461101884330ed9e8b29a5f4195a349f},
bshanks@85 386 doi = {{10.1016/S0891-0618(00)00076-4}},
bshanks@85 387 abstract = {Stereology offers a variety of procedures to analyze quantitatively the regional and laminar organization in cytoarchitectonically defined areas of the human cerebral cortex. Conventional anatomical atlases are of little help in localizing specific cortical areas, since most of them are based on a single brain and use highly observer-dependent criteria for the delineation of cortical areas. In consequence, numerous cortical maps exist which greatly differ with respect to number, position, size and extent of cortical areas. We describe a novel algorithm-based procedure for the delineation of cortical areas, which exploits the automated estimation of volume densities of cortical cell bodies. Spatial sampling of the laminar pattern is performed with density profiles, followed by multivariate analysis of the profiles[`] shape, which locates the cytoarchitectonic borders between neighboring cortical areas at sites where the laminar pattern changes significantly. The borders are then mapped to a human brain atlas system comprising tools for three dimensional reconstruction, visualization and morphometric analysis. A sample of brains with labeled cortical areas is warped into the reference brain of the atlas system in order to generate a population map of the cortical areas, which describes the intersubject variability in spatial conformation of cortical areas. These population maps provide a novel tool for the interpretation of images obtained with functional imaging techniques.},
bshanks@85 388 number = {1},
bshanks@85 389 journal = {Journal of Chemical Neuroanatomy},
bshanks@85 390 author = {A. Schleicher and K. Amunts and S. Geyer and T. Kowalski and T. Schormann and N. {Palomero-Gallagher} and K. Zilles},
bshanks@85 391 month = oct,
bshanks@85 392 year = {2000},
bshanks@85 393 keywords = {Cerebral {Cortex,Density} {profile,Multivariate} {statistics,Quantitative} {cytoarchitecture,Stereology-brain} mapping},
bshanks@85 394 pages = {31--47}
bshanks@85 395 },
bshanks@85 396
bshanks@85 397 @article{schmitt_detection_2003,
bshanks@85 398 title = {Detection of cortical transition regions utilizing statistical analyses of excess masses},
bshanks@85 399 volume = {19},
bshanks@85 400 issn = {1053-8119},
bshanks@85 401 url = {http://www.sciencedirect.com/science/article/B6WNP-488VX9X-2/2/4a7467890b69d13dec8261a4f6fc66d5},
bshanks@85 402 doi = {{10.1016/S1053-8119(03)00040-5}},
bshanks@85 403 abstract = {A new statistical approach for observer-assisted detection of transition regions of adjacent cytoarchitectonic areas within the human cerebral cortex was developed. This method analyzes the structural information of cytoarchitectural profiles (e.g., the modality of a gray level intensity distribution) based on observed excess mass differences verified by a suitable statistical test. Profiles were generated by scanning the cerebral cortex over respective regions of interest that were oriented to trajectories running parallel to the orientation of cell columns. For each single profile, determination of excess masses provided evidence for a certain number of peaks in the cell density, thereby avoiding fluctuation due solely to sampling anomalies. Comparing such excess mass measurements by means of multiple local rank tests over a wide range of profiles allowed for the detection of cytoarchitectural inhomogeneities at respective given confidence levels. Special parameters (e.g., level of significance, width of targeted region, number of peaks) then could be adapted to specific pattern recognition problems in lamination analyses. Such analyses of excess masses provided a general tool for observer-assisted evaluation of profile arrays. This observer-assisted statistical method was applied to five different cortical examples. It detected the same transition regions that had been determined earlier through direct examination of samples, despite cortical convexities, concavities, and some minor staining inhomogeneities.},
bshanks@85 404 number = {1},
bshanks@85 405 journal = {{NeuroImage}},
bshanks@85 406 author = {Oliver Schmitt and Lars Hömke and Lutz Dümbgen},
bshanks@85 407 month = may,
bshanks@85 408 year = {2003},
bshanks@85 409 keywords = {Brain {mapping,Cerebral} {Cortex,Cytoarchitecture,Excess} {mass,Lamination,Multiple} local rank {test,Neuroimaging,Profiles,Trajectories,Transition} {regions,Traverses}},
bshanks@85 410 pages = {42--63}
bshanks@85 411 },
bshanks@85 412
bshanks@85 413 @article{schleicher_quantitative_2005,
bshanks@85 414 title = {Quantitative architectural analysis: a new approach to cortical mapping},
bshanks@85 415 volume = {210},
bshanks@85 416 url = {http://dx.doi.org/10.1007/s00429-005-0028-2},
bshanks@85 417 doi = {10.1007/s00429-005-0028-2},
bshanks@85 418 abstract = {Abstract Recent progress in anatomical and functional {MRI} has revived the demand for a reliable, topographic map of the human cerebral
bshanks@85 419 cortex. Till date, interpretations of specific activations found in functional imaging studies and their topographical analysis
bshanks@85 420 in a spatial reference system are, often, still based on classical architectonic maps. The most commonly used reference atlas
bshanks@85 421 is that of Brodmann and his successors, despite its severe inherent drawbacks. One obvious weakness in traditional, architectural
bshanks@85 422 mapping is the subjective nature of localising borders between cortical areas, by means of a purely visual, microscopical
bshanks@85 423 examination of histological specimens. To overcome this limitation, more objective, quantitative mapping procedures have been
bshanks@85 424 established in the past years. The quantification of the neocortical, laminar pattern by defining intensity line profiles
bshanks@85 425 across the cortical layers, has a long tradition. During the last years, this method has been extended to enable a reliable,
bshanks@85 426 reproducible mapping of the cortex based on image analysis and multivariate statistics. Methodological approaches to such
bshanks@85 427 algorithm-based, cortical mapping were published for various architectural modalities. In our contribution, principles of
bshanks@85 428 algorithm-based mapping are described for cyto- and receptorarchitecture. In a cytoarchitectural parcellation of the human
bshanks@85 429 auditory cortex, using a sliding window procedure, the classical areal pattern of the human superior temporal gyrus was modified
bshanks@85 430 by a replacing of Brodmann’s areas 41, 42, 22 and parts of area 21, with a novel, more detailed map. An extension and optimisation
bshanks@85 431 of the sliding window procedure to the specific requirements of receptorarchitectonic mapping, is also described using the
bshanks@85 432 macaque central sulcus and adjacent superior parietal lobule as a second, biologically independent example. Algorithm-based
bshanks@85 433 mapping procedures, however, are not limited to these two architectural modalities, but can be applied to all images in which
bshanks@85 434 a laminar cortical pattern can be detected and quantified, e.g. myeloarchitectonic and in vivo high resolution {MR} imaging.
bshanks@85 435 Defining cortical borders, based on changes in cortical lamination in high resolution, in vivo structural {MR} images will result
bshanks@85 436 in a rapid increase of our knowledge on the structural parcellation of the human cerebral cortex.},
bshanks@85 437 number = {5},
bshanks@85 438 journal = {Anatomy and Embryology},
bshanks@85 439 author = {A. Schleicher and N. {Palomero-Gallagher} and P. Morosan and S. Eickhoff and T. Kowalski and K. Vos and K. Amunts and K. Zilles},
bshanks@85 440 month = dec,
bshanks@85 441 year = {2005},
bshanks@85 442 pages = {373--386}
bshanks@85 443 },
bshanks@85 444
bshanks@85 445 @article{kruggel_analyzingneocortical_2003,
bshanks@85 446 title = {Analyzing the neocortical fine-structure},
bshanks@85 447 volume = {7},
bshanks@85 448 issn = {1361-8415},
bshanks@85 449 url = {http://www.sciencedirect.com/science/article/B6W6Y-48FSTG9-3/2/5a6f5b703630037afeea6067c27b42be},
bshanks@85 450 doi = {{10.1016/S1361-8415(03)00006-9}},
bshanks@85 451 abstract = {Cytoarchitectonic fields of the human neocortex are defined by characteristic variations in the composition of a general six-layer structure. It is commonly accepted that these fields correspond to functionally homogeneous entities. Diligent techniques were developed to characterize cytoarchitectonic fields by staining sections of post-mortem brains and subsequent statistical evaluation. Fields were found to show a considerable interindividual variability in extent and relation to macroscopic anatomical landmarks. With upcoming new high-resolution magnetic resonance imaging {(MRI)} protocols, it appears worthwhile to examine the feasibility of characterizing the neocortical fine-structure from anatomical {MRI} scans, thus, defining neocortical fields by in vivo techniques. A fixated brain hemisphere was scanned at a resolution of approximately 0.3 mm. After correcting for intensity inhomogeneities in the dataset, the cortex boundaries (the white/grey matter and grey matter/background interfaces) were determined as a triangular mesh. Radial intensity profiles following the shortest path through the cortex were computed and characterized by a sparse set of features. A statistical similarity measure between features of different regions was defined, and served to define the extent of Brodmann's Areas 4, 17, 44 and 45 in this dataset.},
bshanks@85 452 number = {3},
bshanks@85 453 journal = {Medical Image Analysis},
bshanks@85 454 author = {F. Kruggel and M. K. Brückner and Th. Arendt and C. J. Wiggins and D. Y. von Cramon},
bshanks@85 455 month = sep,
bshanks@85 456 year = {2003},
bshanks@85 457 pages = {251--264}
bshanks@85 458 },
bshanks@85 459
bshanks@85 460 @inbook{adamson_tracking_2005,
bshanks@85 461 series = {Lecture Notes in Computer Science},
bshanks@85 462 title = {A Tracking Approach to Parcellation of the Cerebral Cortex},
bshanks@85 463 volume = {Volume 3749/2005},
bshanks@85 464 isbn = {978-3-540-29327-9},
bshanks@85 465 url = {http://dx.doi.org/10.1007/11566465_37},
bshanks@85 466 abstract = {The cerebral cortex is composed of regions with distinct laminar structure. Functional neuroimaging results are often reported with respect to these regions, usually by means of a brain “atlas”. Motivated by the need for more precise atlases, and the lack of model-based approaches in prior work in the field, this paper introduces a novel approach to parcellating the cortex into regions of distinct laminar structure, based on the theory of target tracking. The cortical layers are modelled by hidden Markov models and are tracked to determine the Bayesian evidence of layer hypotheses. This model-based parcellation method, evaluated here on a set of histological images of the cortex, is extensible to {3-D} images.},
bshanks@85 467 booktitle = {Medical Image Computing and {Computer-Assisted} Intervention – {MICCAI} 2005},
bshanks@85 468 publisher = {Springer Berlin / Heidelberg},
bshanks@85 469 author = {Chris Adamson and Leigh Johnston and Terrie Inder and Sandra Rees and Iven Mareels and Gary Egan},
bshanks@85 470 year = {2005},
bshanks@85 471 pages = {294--301}
bshanks@94 472 },
bshanks@94 473
bshanks@94 474 @article{paciorek_computational_2007,
bshanks@94 475 title = {Computational techniques for spatial logistic regression with large data sets},
bshanks@94 476 volume = {51},
bshanks@94 477 issn = {0167-9473},
bshanks@94 478 url = {http://www.sciencedirect.com/science/article/B6V8V-4MG6JWS-2/2/dfe5cd9c7ac7bc39d22ce45eebe303b8},
bshanks@94 479 doi = {10.1016/j.csda.2006.11.008},
bshanks@94 480 abstract = {In epidemiological research, outcomes are frequently non-normal, sample sizes may be large, and effect sizes are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. I focus on binary outcomes, with the risk surface a smooth function of space, but the development herein is relevant for non-normal data in general. I compare penalized likelihood {(PL)} models, including the penalized quasi-likelihood {(PQL)} approach, and Bayesian models based on fit, speed, and ease of implementation.
bshanks@94 481 A Bayesian model using a spectral basis {(SB)} representation of the spatial surface via the Fourier basis provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being reasonably computationally efficient. One of the contributions of this work is further development of this underused representation. The {SB} model outperforms the {PL} methods, which are prone to overfitting, but is slower to fit and not as easily implemented. A Bayesian Markov random field model performs less well statistically than the {SB} model, but is very computationally efficient. We illustrate the methods on a real data set of cancer cases in Taiwan.
bshanks@94 482 The success of the {SB} with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.},
bshanks@94 483 number = {8},
bshanks@94 484 journal = {Computational Statistics \& Data Analysis},
bshanks@94 485 author = {Christopher J. Paciorek},
bshanks@94 486 month = may,
bshanks@94 487 year = {2007},
bshanks@94 488 keywords = {Bayesian {statistics,Disease} {mapping,Fourier} {basis,Generalized} linear mixed {model,Geostatistics,Risk} {surface,Spatial} {statistics,Spectral} basis},
bshanks@94 489 pages = {3631--3653}
bshanks@94 490 },
bshanks@94 491
bshanks@94 492 @article{hastie_gene_2000,
bshanks@94 493 title = {{'Gene} shaving' as a method for identifying distinct sets of genes with similar expression patterns},
bshanks@94 494 volume = {1},
bshanks@94 495 issn = {1465-6906},
bshanks@94 496 url = {http://genomebiology.com/2000/1/2/research/0003/},
bshanks@94 497 doi = {10.1186/gb-2000-1-2-research0003},
bshanks@94 498 abstract = {{BACKGROUND:Large} gene expression studies, such as those conducted using {DNA} arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful {groupings.RESULTS:We} illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of {survival.CONCLUSIONS:The} gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.},
bshanks@94 499 number = {2},
bshanks@94 500 journal = {Genome Biology},
bshanks@94 501 author = {Trevor Hastie and Robert Tibshirani and Michael Eisen and Ash Alizadeh and Ronald Levy and Louis Staudt and Wing Chan and David Botstein and Patrick Brown},
bshanks@94 502 year = {2000},
bshanks@94 503 pages = {research0003.1--research0003.21}
bshanks@96 504 },
bshanks@96 505
bshanks@96 506 @misc{_home_????,
bshanks@96 507 title = {Home Page of Geoffrey Hinton},
bshanks@96 508 url = {http://www.cs.toronto.edu/~hinton/},
bshanks@96 509 howpublished = {http://www.cs.toronto.edu/{\textasciitilde}hinton/},
bshanks@96 510 comment = {eep Boltzmann Machines.}
bshanks@96 511 },
bshanks@96 512
bshanks@96 513 @misc{_dbm.pdf_????,
bshanks@96 514 title = {dbm.pdf},
bshanks@96 515 url = {http://www.cs.toronto.edu/~hinton/absps/dbm.pdf}
bshanks@96 516 },
bshanks@96 517
bshanks@96 518 @misc{_dbm.pdf_????-1,
bshanks@96 519 title = {dbm.pdf},
bshanks@96 520 url = {http://www.cs.toronto.edu/~hinton/absps/dbm.pdf}
bshanks@96 521 },
bshanks@96 522
bshanks@96 523 @inproceedings{kemp_learning_2006,
bshanks@96 524 title = {Learning systems of concepts with an infinite relational model.},
bshanks@96 525 url = {http://web.mit.edu/cocosci/josh.html},
bshanks@96 526 booktitle = {{AAAI}},
bshanks@96 527 author = {C Kemp and {JB} Tenenbaum and {TL} Griffiths and T Yamada and N Ueda},
bshanks@96 528 year = {2006},
bshanks@96 529 keywords = {infinite,model,relational}
bshanks@81 530 }