1.1 --- a/grant.bib Fri Jul 03 05:17:28 2009 -0700 1.2 +++ b/grant.bib Tue Jul 07 15:47:43 2009 -0700 1.3 @@ -58,7 +58,7 @@ 1.4 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}, 1.5 month = dec, 1.6 year = {2008}, 1.7 - keywords = {{MOLNEURO,SYSBIO,SYSNEURO}}, 1.8 + keywords = {{MOLNEURO,} {SYSBIO,} {SYSNEURO}}, 1.9 pages = {1010--1021} 1.10 }, 1.11 1.12 @@ -66,6 +66,7 @@ 1.13 address = {San Diego, California}, 1.14 title = {{WikiGateway:} a library for interoperability and accelerated wiki development}, 1.15 isbn = {1-59593-111-2}, 1.16 + shorttitle = {{WikiGateway}}, 1.17 url = {http://portal.acm.org/citation.cfm?id=1104973.1104979}, 1.18 doi = {10.1145/1104973.1104979}, 1.19 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.}, 1.20 @@ -73,7 +74,7 @@ 1.21 publisher = {{ACM}}, 1.22 author = {Bayle Shanks}, 1.23 year = {2005}, 1.24 - keywords = {atom,client-side wiki,interoperability,interwiki,middleware,webdav,wiki,wikiclient,wikigateway,wikirpcinterface,wiki xmlrpc}, 1.25 + keywords = {atom, client-side wiki, interoperability, interwiki, middleware, webdav, wiki, wikiclient, wikigateway, wikirpcinterface, wiki xmlrpc}, 1.26 pages = {53--66} 1.27 }, 1.28 1.29 @@ -105,7 +106,7 @@ 1.30 author = {D C Van Essen and H A Drury and J Dickson and J Harwell and D Hanlon and C H Anderson}, 1.31 year = {2001}, 1.32 note = {{PMID:} 11522765}, 1.33 - 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}, 1.34 + 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}, 1.35 pages = {443--59} 1.36 }, 1.37 1.38 @@ -116,7 +117,7 @@ 1.39 author = {Sören Sonnenburg and Gunnar Raetsch and Christin Schaefer and Bernhard Schölkopf}, 1.40 year = {2006}, 1.41 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.}, 1.42 - keywords = {{Learning/Statistics} \& {Optimisation,Multimodal} {Integration,Theory} \& Algorithms}, 1.43 + keywords = {{Learning/Statistics} \& Optimisation, Multimodal Integration, Theory \& Algorithms}, 1.44 howpublished = {http://eprints.pascal-network.org/archive/00003035/} 1.45 }, 1.46 1.47 @@ -124,6 +125,7 @@ 1.48 edition = {3}, 1.49 title = {Brain Maps: Structure of the Rat Brain}, 1.50 isbn = {0126105820}, 1.51 + shorttitle = {Brain Maps}, 1.52 publisher = {Academic Press}, 1.53 author = {Larry Swanson}, 1.54 month = nov, 1.55 @@ -153,7 +155,7 @@ 1.56 month = dec, 1.57 year = {2002}, 1.58 note = {{PMID:} 12466850}, 1.59 - 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}}, 1.60 + 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}, 1.61 pages = {520--62} 1.62 }, 1.63 1.64 @@ -188,6 +190,7 @@ 1.65 @article{visel_genepaint.org:atlas_2004, 1.66 title = {{GenePaint.org:} an atlas of gene expression patterns in the mouse embryo}, 1.67 volume = {32}, 1.68 + shorttitle = {{GenePaint.org}}, 1.69 url = {http://nar.oxfordjournals.org/cgi/content/abstract/32/suppl_1/D552}, 1.70 doi = {10.1093/nar/gkh029}, 1.71 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.}, 1.72 @@ -201,6 +204,7 @@ 1.73 @article{magdaleno_bgem:in_2006, 1.74 title = {{BGEM:} An In Situ Hybridization Database of Gene Expression in the Embryonic and Adult Mouse Nervous System}, 1.75 volume = {4}, 1.76 + shorttitle = {{BGEM}}, 1.77 url = {http://dx.doi.org/10.1371%2Fjournal.pbio.0040086}, 1.78 doi = {10.1371/journal.pbio.0040086}, 1.79 number = {4}, 1.80 @@ -218,13 +222,14 @@ 1.81 booktitle = {Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. {IEEE}}, 1.82 author = {J. Carson and T. Ju and C. Thaller and M. Bello and I. Kakadiaris and J. Warren and G. Eichele and W. Chiu}, 1.83 year = {2005}, 1.84 - 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}, 1.85 + 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}, 1.86 pages = {358} 1.87 }, 1.88 1.89 @article{venkataraman_emage_2008, 1.90 title = {{EMAGE} Edinburgh Mouse Atlas of Gene Expression: 2008 update}, 1.91 volume = {36}, 1.92 + shorttitle = {{EMAGE} Edinburgh Mouse Atlas of Gene Expression}, 1.93 url = {http://nar.oxfordjournals.org/cgi/content/abstract/36/suppl_1/D860}, 1.94 doi = {10.1093/nar/gkm938}, 1.95 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).}}, 1.96 @@ -235,7 +240,7 @@ 1.97 pages = {D860--865} 1.98 }, 1.99 1.100 -@inbook{hemert_matching_2008, 1.101 +@incollection{hemert_matching_2008, 1.102 series = {Communications in Computer and Information Science}, 1.103 title = {Matching Spatial Regions with Combinations of Interacting Gene Expression Patterns}, 1.104 volume = {13}, 1.105 @@ -252,7 +257,7 @@ 1.106 pages = {347--361} 1.107 }, 1.108 1.109 -@inbook{van_hemert_mining_2007, 1.110 +@incollection{van_hemert_mining_2007, 1.111 series = {Lecture Notes in Computer Science}, 1.112 title = {Mining Spatial Gene Expression Data for Association Rules}, 1.113 volume = {4414/2007}, 1.114 @@ -274,6 +279,7 @@ 1.115 title = {The Zebrafish Information Network: the zebrafish model organism database}, 1.116 volume = {34}, 1.117 issn = {1362-4962}, 1.118 + shorttitle = {The Zebrafish Information Network}, 1.119 url = {http://www.ncbi.nlm.nih.gov/pubmed/16381936}, 1.120 doi = {10.1093/nar/gkj086}, 1.121 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.}, 1.122 @@ -282,7 +288,7 @@ 1.123 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}, 1.124 year = {2006}, 1.125 note = {{PMID:} 16381936}, 1.126 - keywords = {{Animals,Databases,} {Genetic,Gene} {Expression,Genomics,Internet,Models,} {Animal,Oligonucleotides,} {Antisense,Systems} {Integration,User-Computer} {Interface,Vocabulary,} {Controlled,Zebrafish,Zebrafish} Proteins}, 1.127 + keywords = {Animals, Databases, Genetic, Gene Expression, Genomics, Internet, Models, Animal, Oligonucleotides, Antisense, Systems Integration, {User-Computer} Interface, Vocabulary, Controlled, Zebrafish, Zebrafish Proteins}, 1.128 pages = {D581--5} 1.129 }, 1.130 1.131 @@ -340,6 +346,7 @@ 1.132 @article{barrett_ncbi_2007, 1.133 title = {{NCBI} {GEO:} mining tens of millions of expression profiles--database and tools update}, 1.134 volume = {35}, 1.135 + shorttitle = {{NCBI} {GEO}}, 1.136 url = {http://nar.oxfordjournals.org/cgi/content/abstract/35/suppl_1/D760}, 1.137 doi = {10.1093/nar/gkl887}, 1.138 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/}, 1.139 @@ -353,6 +360,7 @@ 1.140 @article{smith_mouse_2007, 1.141 title = {The mouse Gene Expression Database {(GXD):} 2007 update}, 1.142 volume = {35}, 1.143 + shorttitle = {The mouse Gene Expression Database {(GXD)}}, 1.144 url = {http://nar.oxfordjournals.org/cgi/content/abstract/35/suppl_1/D618}, 1.145 doi = {10.1093/nar/gkl1003}, 1.146 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.}, 1.147 @@ -374,7 +382,7 @@ 1.148 journal = {{NeuroImage}}, 1.149 author = {J. Annese and A. Pitiot and I. D. Dinov and A. W. Toga}, 1.150 year = {2004}, 1.151 - keywords = {Cerebral {Cortex,Cortical} {areas,Myelo-architecture}}, 1.152 + keywords = {Cerebral Cortex, Cortical areas, Myelo-architecture}, 1.153 pages = {15--26} 1.154 }, 1.155 1.156 @@ -382,6 +390,7 @@ 1.157 title = {A stereological approach to human cortical architecture: identification and delineation of cortical areas}, 1.158 volume = {20}, 1.159 issn = {0891-0618}, 1.160 + shorttitle = {A stereological approach to human cortical architecture}, 1.161 url = {http://www.sciencedirect.com/science/article/B6T02-43HDYPB-5/2/461101884330ed9e8b29a5f4195a349f}, 1.162 doi = {{10.1016/S0891-0618(00)00076-4}}, 1.163 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.}, 1.164 @@ -390,7 +399,7 @@ 1.165 author = {A. Schleicher and K. Amunts and S. Geyer and T. Kowalski and T. Schormann and N. {Palomero-Gallagher} and K. Zilles}, 1.166 month = oct, 1.167 year = {2000}, 1.168 - keywords = {Cerebral {Cortex,Density} {profile,Multivariate} {statistics,Quantitative} {cytoarchitecture,Stereology-brain} mapping}, 1.169 + keywords = {Cerebral Cortex, Density profile, Multivariate statistics, Quantitative cytoarchitecture, Stereology-brain mapping}, 1.170 pages = {31--47} 1.171 }, 1.172 1.173 @@ -406,13 +415,14 @@ 1.174 author = {Oliver Schmitt and Lars Hömke and Lutz Dümbgen}, 1.175 month = may, 1.176 year = {2003}, 1.177 - keywords = {Brain {mapping,Cerebral} {Cortex,Cytoarchitecture,Excess} {mass,Lamination,Multiple} local rank {test,Neuroimaging,Profiles,Trajectories,Transition} {regions,Traverses}}, 1.178 + keywords = {Brain mapping, Cerebral Cortex, Cytoarchitecture, Excess mass, Lamination, Multiple local rank test, Neuroimaging, Profiles, Trajectories, Transition regions, Traverses}, 1.179 pages = {42--63} 1.180 }, 1.181 1.182 @article{schleicher_quantitative_2005, 1.183 title = {Quantitative architectural analysis: a new approach to cortical mapping}, 1.184 volume = {210}, 1.185 + shorttitle = {Quantitative architectural analysis}, 1.186 url = {http://dx.doi.org/10.1007/s00429-005-0028-2}, 1.187 doi = {10.1007/s00429-005-0028-2}, 1.188 abstract = {Abstract Recent progress in anatomical and functional {MRI} has revived the demand for a reliable, topographic map of the human cerebral 1.189 @@ -457,7 +467,7 @@ 1.190 pages = {251--264} 1.191 }, 1.192 1.193 -@inbook{adamson_tracking_2005, 1.194 +@incollection{adamson_tracking_2005, 1.195 series = {Lecture Notes in Computer Science}, 1.196 title = {A Tracking Approach to Parcellation of the Cerebral Cortex}, 1.197 volume = {3749/2005}, 1.198 @@ -485,7 +495,7 @@ 1.199 author = {Christopher J. Paciorek}, 1.200 month = may, 1.201 year = {2007}, 1.202 - keywords = {Bayesian {statistics,Disease} {mapping,Fourier} {basis,Generalized} linear mixed {model,Geostatistics,Risk} {surface,Spatial} {statistics,Spectral} basis}, 1.203 + keywords = {Bayesian statistics, Disease mapping, Fourier basis, Generalized linear mixed model, Geostatistics, Risk surface, Spatial statistics, Spectral basis}, 1.204 pages = {3631--3653} 1.205 }, 1.206 1.207 @@ -507,7 +517,7 @@ 1.208 title = {Home Page of Geoffrey Hinton}, 1.209 url = {http://www.cs.toronto.edu/~hinton/}, 1.210 howpublished = {http://www.cs.toronto.edu/{\textasciitilde}hinton/}, 1.211 - comment = {eep Boltzmann Machines.} 1.212 + annote = {eep Boltzmann Machines.} 1.213 }, 1.214 1.215 @misc{_dbm.pdf_????, 1.216 @@ -526,7 +536,7 @@ 1.217 booktitle = {{AAAI}}, 1.218 author = {C Kemp and {JB} Tenenbaum and {TL} Griffiths and T Yamada and N Ueda}, 1.219 year = {2006}, 1.220 - keywords = {infinite,model,relational} 1.221 + keywords = {infinite, model, relational} 1.222 }, 1.223 1.224 @article{serpico_new_2001, 1.225 @@ -556,6 +566,15 @@ 1.226 journal = {Geoscience and Remote Sensing, {IEEE} Transactions on}, 1.227 author = {{S.B.} Serpico and L. Bruzzone}, 1.228 year = {2001}, 1.229 - keywords = {algorithm,binary string,feature extraction,feature selection,geophysical measurement technique,geophysical signal processing,geophysical techniques,hyperspectral remote sensing,image processing,land surface,multidimensional signal processing,multispectral remote sensing,optical imaging,remote sensing,suboptimal search strategy,terrain mapping}, 1.230 + keywords = {algorithm, binary string, feature extraction, feature selection, geophysical measurement technique, geophysical signal processing, geophysical techniques, hyperspectral remote sensing, image processing, land surface, multidimensional signal processing, multispectral remote sensing, optical imaging, remote sensing, suboptimal search strategy, terrain mapping}, 1.231 pages = {1360--1367} 1.232 +}, 1.233 + 1.234 +@misc{boggs_spectral_2008, 1.235 + title = {Spectral Python}, 1.236 + url = {http://spectralpython.sourceforge.net/}, 1.237 + author = {Thomas Boggs}, 1.238 + month = jul, 1.239 + year = {2008}, 1.240 + howpublished = {http://spectralpython.sourceforge.net/} 1.241 } 1.242 \ No newline at end of file