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report.bbl
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\field{labeltitle}{Molecular Biology of the Cell}
\field{abstract}{{ExcerptMolecular} Biology of the Cell is the classic in-depth text reference in cell biology. By extracting fundamental concepts and meaning from this enormous and ever-growing field, the authors tell the story of cell biology, and create a coherent framework through which non-expert readers may approach the subject. Written in clear and concise language, and illustrated with original drawings, the book is enjoyable to read, and provides a sense of the excitement of modern biology. Molecular Biology of the Cell not only sets forth the current understanding of cell biology (updated as of Fall 2001), but also explores the intriguing implications and possibilities of that which remains unknown.}
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\field{title}{Plots of high-dimensional data}
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\field{labeltitle}{Gene Ontology}
\field{abstract}{Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.}
\field{issn}{1061-4036}
\field{journaltitle}{Nature Genetics}
\field{month}{05}
\field{number}{1}
\field{shorttitle}{Gene Ontology}
\field{title}{Gene Ontology: tool for the unification of biology}
\field{urlday}{22}
\field{urlmonth}{04}
\field{urlyear}{2014}
\field{volume}{25}
\field{year}{2000}
\field{pages}{25\bibrangedash 29}
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{{uniquename=0,hash=373b4c12b7629a77ce2217795cc7420e}{Baolin}{B\bibinitperiod}{Liu}{L\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=5a3f24f749995d5aacefa1b22b142ec4}{Bo}{B\bibinitperiod}{Hu}{H\bibinitperiod}{}{}{}{}}%
}
\list{publisher}{1}{%
{Springer}%
}
\strng{namehash}{c30de795aaac81b1d95882f71644271a}
\strng{fullhash}{c30de795aaac81b1d95882f71644271a}
\field{sortinit}{B}
\field{labelyear}{2007}
\field{datelabelsource}{}
\field{labeltitle}{{HPRD}}
\field{booktitle}{Network and Parallel Computing}
\field{shorttitle}{{HPRD}}
\field{title}{{HPRD}: a high performance {RDF} database}
\field{urlday}{14}
\field{urlmonth}{08}
\field{urlyear}{2014}
\field{year}{2007}
\field{pages}{364\bibrangedash 374}
\verb{file}
\verb [PDF] from igroupnet.com:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/DM9DI5DB/Baolin and Bo - 2007 - HPRD a high performance RDF database.pdf:application/pdf;Snapshot:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/VU85ZQW5/978-3-540-74784-0_37.html:text/html
\endverb
\verb{url}
\verb http://link.springer.com/chapter/10.1007/978-3-540-74784-0_37
\endverb
\endentry
\entry{barber_bayesian_2012}{book}{}
\name{labelname}{1}{}{%
{{uniquename=0,hash=9a97f74e5fb11fe785cbe0a83fc5276c}{Barber}{B\bibinitperiod}{David}{D\bibinitperiod}{}{}{}{}}%
}
\name{author}{1}{}{%
{{uniquename=0,hash=9a97f74e5fb11fe785cbe0a83fc5276c}{Barber}{B\bibinitperiod}{David}{D\bibinitperiod}{}{}{}{}}%
}
\list{language}{1}{%
{en}%
}
\list{publisher}{1}{%
{Cambridge University Press}%
}
\strng{namehash}{9a97f74e5fb11fe785cbe0a83fc5276c}
\strng{fullhash}{9a97f74e5fb11fe785cbe0a83fc5276c}
\field{sortinit}{B}
\field{labelyear}{2012}
\field{labelmonth}{02}
\field{datelabelsource}{}
\field{labeltitle}{Bayesian Reasoning and Machine Learning}
\field{abstract}{Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, {DNA} sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a {MATLAB} toolbox, are available online.}
\field{isbn}{9780521518147}
\field{month}{02}
\field{title}{Bayesian Reasoning and Machine Learning}
\field{year}{2012}
\keyw{Computers / Computer Vision \& Pattern Recognition,Computers / Intelligence ({AI}) \& Semantics,Mathematics / Probability \& Statistics / General}
\endentry
\entry{black_mechanisms_2003}{article}{}
\name{labelname}{1}{}{%
{{uniquename=0,hash=8519fcffd3635c260ba2f38cbaaae020}{Black}{B\bibinitperiod}{Douglas\bibnamedelima L.}{D\bibinitperiod\bibinitdelim L\bibinitperiod}{}{}{}{}}%
}
\name{author}{1}{}{%
{{uniquename=0,hash=8519fcffd3635c260ba2f38cbaaae020}{Black}{B\bibinitperiod}{Douglas\bibnamedelima L.}{D\bibinitperiod\bibinitdelim L\bibinitperiod}{}{}{}{}}%
}
\strng{namehash}{8519fcffd3635c260ba2f38cbaaae020}
\strng{fullhash}{8519fcffd3635c260ba2f38cbaaae020}
\field{sortinit}{B}
\field{labelyear}{2003}
\field{datelabelsource}{}
\field{labeltitle}{Mechanisms of Alternative Pre-Messenger Rna Splicing}
\field{abstract}{Alternative pre-{mRNA} splicing is a central mode of genetic regulation in higher eukaryotes. Variability in splicing patterns is a major source of protein diversity from the genome. In this review, I describe what is currently known of the molecular mechanisms that control changes in splice site choice. I start with the best-characterized systems from the Drosophila sex determination pathway, and then describe the regulators of other systems about whose mechanisms there is some data. How these regulators are combined into complex systems of tissue-specific splicing is discussed. In conclusion, very recent studies are presented that point to new directions for understanding alternative splicing and its mechanisms.}
\field{journaltitle}{Annual Review of Biochemistry}
\field{number}{1}
\field{title}{Mechanisms of Alternative Pre-Messenger Rna Splicing}
\field{urlday}{14}
\field{urlmonth}{08}
\field{urlyear}{2014}
\field{volume}{72}
\field{year}{2003}
\field{pages}{291\bibrangedash 336}
\verb{doi}
\verb 10.1146/annurev.biochem.72.121801.161720
\endverb
\verb{file}
\verb Full Text PDF:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/IEN4FFFH/Black - 2003 - Mechanisms of Alternative Pre-Messenger Rna Splici.pdf:application/pdf
\endverb
\verb{url}
\verb http://dx.doi.org/10.1146/annurev.biochem.72.121801.161720
\endverb
\keyw{alternative splicing,protein diversity,regulatory mechanisms,{RNA} binding proteins,spliceosome}
\endentry
\entry{blum_multiloc2:_2009}{article}{}
\name{labelname}{3}{}{%
{{uniquename=0,hash=130e062100a1886f10101b849e8c2a3d}{Blum}{B\bibinitperiod}{Torsten}{T\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=2c3c8d7f7c6b29e6a5c055c2b0e546ee}{Briesemeister}{B\bibinitperiod}{Sebastian}{S\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=569089e0d5a019d6333ea5f294769662}{Kohlbacher}{K\bibinitperiod}{Oliver}{O\bibinitperiod}{}{}{}{}}%
}
\name{author}{3}{}{%
{{uniquename=0,hash=130e062100a1886f10101b849e8c2a3d}{Blum}{B\bibinitperiod}{Torsten}{T\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=2c3c8d7f7c6b29e6a5c055c2b0e546ee}{Briesemeister}{B\bibinitperiod}{Sebastian}{S\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=569089e0d5a019d6333ea5f294769662}{Kohlbacher}{K\bibinitperiod}{Oliver}{O\bibinitperiod}{}{}{}{}}%
}
\strng{namehash}{6247d4bcfd996fb4bc9613660e80e2e7}
\strng{fullhash}{6247d4bcfd996fb4bc9613660e80e2e7}
\field{sortinit}{B}
\field{labelyear}{2009}
\field{datelabelsource}{}
\field{labeltitle}{{MultiLoc}2}
\field{journaltitle}{{BMC} bioinformatics}
\field{number}{1}
\field{shorttitle}{{MultiLoc}2}
\field{title}{{MultiLoc}2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction}
\field{urlday}{07}
\field{urlmonth}{08}
\field{urlyear}{2014}
\field{volume}{10}
\field{year}{2009}
\field{pages}{274}
\verb{file}
\verb [HTML] from biomedcentral.com:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/TUJFHBHZ/274.html:text/html
\endverb
\verb{url}
\verb http://www.biomedcentral.com/1471-2105/10/274/
\endverb
\endentry
\entry{chen_identifying_2013}{article}{}
\name{labelname}{4}{}{%
{{uniquename=1,hash=2d5e291fa924ae738b4ff3e1f360f40c}{Chen}{C\bibinitperiod}{Bolin}{B\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=b5da4fad55c757f3e96cc0bcf02f261b}{Fan}{F\bibinitperiod}{Weiwei}{W\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=18b975301a4d17c28ca70d0a3fbce135}{Liu}{L\bibinitperiod}{Juan}{J\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=dcd65426e188141d5c408fc2631d59bc}{Wu}{W\bibinitperiod}{Fang-Xiang}{F\bibinithyphendelim X\bibinitperiod}{}{}{}{}}%
}
\name{author}{4}{}{%
{{uniquename=1,hash=2d5e291fa924ae738b4ff3e1f360f40c}{Chen}{C\bibinitperiod}{Bolin}{B\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=b5da4fad55c757f3e96cc0bcf02f261b}{Fan}{F\bibinitperiod}{Weiwei}{W\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=18b975301a4d17c28ca70d0a3fbce135}{Liu}{L\bibinitperiod}{Juan}{J\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=dcd65426e188141d5c408fc2631d59bc}{Wu}{W\bibinitperiod}{Fang-Xiang}{F\bibinithyphendelim X\bibinitperiod}{}{}{}{}}%
}
\list{language}{1}{%
{en}%
}
\strng{namehash}{4611caa5793becf5bd5671ef12361b00}
\strng{fullhash}{77659d7cf9c675e1d4b6e3a43d93a050}
\field{sortinit}{C}
\field{labelyear}{2013}
\field{labelmonth}{06}
\field{datelabelsource}{}
\field{labeltitle}{Identifying protein complexes and functional modules—from static {PPI} networks to dynamic {PPI} networks}
\field{abstract}{Cellular processes are typically carried out by protein complexes and functional modules. Identifying them plays an important role for our attempt to reveal principles of cellular organizations and functions. In this article, we review computational algorithms for identifying protein complexes and/or functional modules from protein–protein interaction ({PPI}) networks. We first describe issues and pitfalls when interpreting {PPI} networks. Then based on types of data used and main ideas involved, we briefly describe protein complex and/or functional module identification algorithms in four categories: (i) those based on topological structures of unweighted {PPI} networks; (ii) those based on characters of weighted {PPI} networks; (iii) those based on multiple data integrations; and (iv) those based on dynamic {PPI} networks. The {PPI} networks are modelled increasingly precise when integrating more types of data, and the study of protein complexes would benefit by shifting from static to dynamic {PPI} networks.}
\field{issn}{1467-5463, 1477-4054}
\field{journaltitle}{Briefings in Bioinformatics}
\field{month}{06}
\field{title}{Identifying protein complexes and functional modules—from static {PPI} networks to dynamic {PPI} networks}
\field{urlday}{20}
\field{urlmonth}{03}
\field{urlyear}{2014}
\field{year}{2013}
\field{pages}{bbt039}
\verb{doi}
\verb 10.1093/bib/bbt039
\endverb
\verb{file}
\verb Full Text PDF:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/XVQSXIB5/Chen et al. - 2013 - Identifying protein complexes and functional modul.pdf:application/pdf;Snapshot:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/WRB7M2V7/bib.bbt039.html:text/html
\endverb
\verb{url}
\verb http://bib.oxfordjournals.org/content/early/2013/06/18/bib.bbt039
\endverb
\keyw{dynamic network,functional module,protein complex,protein–protein interaction,static network}
\endentry
\entry{ug4template}{online}{}
\name{labelname}{1}{}{%
{{uniquename=0,hash=9e80044f995b00111a3fab4916b119c5}{Chris\bibnamedelima Brown}{C\bibinitperiod\bibinitdelim B\bibinitperiod}{Alex\bibnamedelima Shearn}{A\bibinitperiod\bibinitdelim S\bibinitperiod}{}{}{}{}}%
}
\name{author}{1}{}{%
{{uniquename=0,hash=9e80044f995b00111a3fab4916b119c5}{Chris\bibnamedelima Brown}{C\bibinitperiod\bibinitdelim B\bibinitperiod}{Alex\bibnamedelima Shearn}{A\bibinitperiod\bibinitdelim S\bibinitperiod}{}{}{}{}}%
}
\strng{namehash}{9e80044f995b00111a3fab4916b119c5}
\strng{fullhash}{9e80044f995b00111a3fab4916b119c5}
\field{sortinit}{C}
\field{labelyear}{2011}
\field{datelabelsource}{}
\field{labeltitle}{UG4 Project Report Template}
\field{title}{UG4 Project Report Template}
\field{urlday}{13}
\field{urlmonth}{08}
\field{urlyear}{2014}
\field{year}{2011}
\verb{url}
\verb https://github.com/proa/ug4-report-template
\endverb
\endentry
\entry{chua_architecture_2010}{article}{}
\name{labelname}{4}{}{%
{{uniquename=0,hash=5b6cdcf33c1178c9ee8f2d1acbd5de32}{Chua}{C\bibinitperiod}{John\bibnamedelimb J.\bibnamedelimi E.}{J\bibinitperiod\bibinitdelim J\bibinitperiod\bibinitdelim E\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=a99f4a353553ca708b9f647ffa31a07f}{Kindler}{K\bibinitperiod}{Stefan}{S\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=a3918630d3cd63d69c324a711d1d8fdf}{Boyken}{B\bibinitperiod}{Janina}{J\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=88f5a82bf9d3dee56c4b43cf5840b1eb}{Jahn}{J\bibinitperiod}{Reinhard}{R\bibinitperiod}{}{}{}{}}%
}
\name{author}{4}{}{%
{{uniquename=0,hash=5b6cdcf33c1178c9ee8f2d1acbd5de32}{Chua}{C\bibinitperiod}{John\bibnamedelimb J.\bibnamedelimi E.}{J\bibinitperiod\bibinitdelim J\bibinitperiod\bibinitdelim E\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=a99f4a353553ca708b9f647ffa31a07f}{Kindler}{K\bibinitperiod}{Stefan}{S\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=a3918630d3cd63d69c324a711d1d8fdf}{Boyken}{B\bibinitperiod}{Janina}{J\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=88f5a82bf9d3dee56c4b43cf5840b1eb}{Jahn}{J\bibinitperiod}{Reinhard}{R\bibinitperiod}{}{}{}{}}%
}
\list{language}{1}{%
{en}%
}
\strng{namehash}{693bb907d8a81aedba1181c9352f3a4c}
\strng{fullhash}{0f239ed6cd009a92fed8fb778b95ce6e}
\field{sortinit}{C}
\field{labelyear}{2010}
\field{labelmonth}{03}
\field{datelabelsource}{}
\field{labeltitle}{The architecture of an excitatory synapse}
\field{issn}{0021-9533, 1477-9137}
\field{journaltitle}{Journal of Cell Science}
\field{month}{03}
\field{number}{6}
\field{title}{The architecture of an excitatory synapse}
\field{urlday}{23}
\field{urlmonth}{04}
\field{urlyear}{2014}
\field{volume}{123}
\field{year}{2010}
\field{pages}{819\bibrangedash 823}
\verb{doi}
\verb 10.1242/jcs.052696
\endverb
\verb{file}
\verb Full Text PDF:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/DRU9ZURZ/Chua et al. - 2010 - The architecture of an excitatory synapse.pdf:application/pdf;Snapshot:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/4RXC2ZX2/819.html:text/html
\endverb
\verb{url}
\verb http://jcs.biologists.org/content/123/6/819
\endverb
\endentry
\entry{cristianini_introduction_2000}{book}{}
\name{labelname}{2}{}{%
{{uniquename=0,hash=b643588b4a53ed47702d3277b6de83a9}{Cristianini}{C\bibinitperiod}{Nello}{N\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=8feae4ec394a5f6905c74b1a7abff875}{Shawe-Taylor}{S\bibinithyphendelim T\bibinitperiod}{John}{J\bibinitperiod}{}{}{}{}}%
}
\name{author}{2}{}{%
{{uniquename=0,hash=b643588b4a53ed47702d3277b6de83a9}{Cristianini}{C\bibinitperiod}{Nello}{N\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=8feae4ec394a5f6905c74b1a7abff875}{Shawe-Taylor}{S\bibinithyphendelim T\bibinitperiod}{John}{J\bibinitperiod}{}{}{}{}}%
}
\list{language}{1}{%
{en}%
}
\list{publisher}{1}{%
{Cambridge University Press}%
}
\strng{namehash}{95272ad59aaf2ecba8126f86cda4f3d6}
\strng{fullhash}{95272ad59aaf2ecba8126f86cda4f3d6}
\field{sortinit}{C}
\field{labelyear}{2000}
\field{labelmonth}{03}
\field{datelabelsource}{}
\field{labeltitle}{An Introduction to Support Vector Machines and Other Kernel-based Learning Methods}
\field{abstract}{This is the first comprehensive introduction to Support Vector Machines ({SVMs}), a new generation learning system based on recent advances in statistical learning theory. {SVMs} deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications.}
\field{isbn}{9780521780193}
\field{month}{03}
\field{title}{An Introduction to Support Vector Machines and Other Kernel-based Learning Methods}
\field{year}{2000}
\keyw{Computers / Bioinformatics,Computers / Computer Science,Computers / Databases / General,Computers / Intelligence ({AI}) \& Semantics,Computers / Optical Data Processing,Computers / Programming / General}
\endentry
\entry{futschik_comparison_2007}{article}{}
\name{labelname}{3}{}{%
{{uniquename=0,hash=be9f8bcac4d912f32cfba9a7714aa508}{Futschik}{F\bibinitperiod}{Matthias\bibnamedelima E.}{M\bibinitperiod\bibinitdelim E\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=bd2bdb808d017581b4cb378accb8db91}{Chaurasia}{C\bibinitperiod}{Gautam}{G\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=adc6a3b55cc7143be922c11c7ad2b582}{Herzel}{H\bibinitperiod}{Hanspeter}{H\bibinitperiod}{}{}{}{}}%
}
\name{author}{3}{}{%
{{uniquename=0,hash=be9f8bcac4d912f32cfba9a7714aa508}{Futschik}{F\bibinitperiod}{Matthias\bibnamedelima E.}{M\bibinitperiod\bibinitdelim E\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=bd2bdb808d017581b4cb378accb8db91}{Chaurasia}{C\bibinitperiod}{Gautam}{G\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=adc6a3b55cc7143be922c11c7ad2b582}{Herzel}{H\bibinitperiod}{Hanspeter}{H\bibinitperiod}{}{}{}{}}%
}
\list{language}{1}{%
{en}%
}
\strng{namehash}{b5da17256cd54cad300f414aa3e3db7c}
\strng{fullhash}{b5da17256cd54cad300f414aa3e3db7c}
\field{sortinit}{F}
\field{labelyear}{2007}
\field{labelmonth}{03}
\field{datelabelsource}{}
\field{labeltitle}{Comparison of human protein–protein interaction maps}
\field{abstract}{Motivation: Large-scale mappings of protein–protein interactions have started to give us new views of the complex molecular mechanisms inside a cell. After initial projects to systematically map protein interactions in model organisms such as yeast, worm and fly, researchers have begun to focus on the mapping of the human interactome. To tackle this enormous challenge, different approaches have been proposed and pursued. While several large-scale human protein interaction maps have recently been published, their quality remains to be critically assessed. Results: We present here a first comparative analysis of eight currently available large-scale maps with a total of over 10 000 unique proteins and 57 000 interactions included. They are based either on literature search, orthology or by yeast-two-hybrid assays. Comparison reveals only a small, but statistically significant overlap. More importantly, our analysis gives clear indications that all interaction maps imply considerable selection and detection biases. These results have to be taken into account for future assembly of the human interactome. Availability: An integrated human interaction network called Unified Human Interactome ({UniHI}) is made publicly accessible at http://www.mdc-berlin.de/unihi. Contact: m.futschik\{at\}biologie.hu-berlin.de Supplementary information: Supplementary data are available at Bioinformatics online.}
\field{issn}{1367-4803, 1460-2059}
\field{journaltitle}{Bioinformatics}
\field{month}{03}
\field{number}{5}
\field{title}{Comparison of human protein–protein interaction maps}
\field{urlday}{14}
\field{urlmonth}{08}
\field{urlyear}{2014}
\field{volume}{23}
\field{year}{2007}
\field{pages}{605\bibrangedash 611}
\verb{doi}
\verb 10.1093/bioinformatics/btl683
\endverb
\verb{file}
\verb Full Text PDF:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/QKKN9FW5/Futschik et al. - 2007 - Comparison of human protein–protein interaction ma.pdf:application/pdf;Snapshot:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/6QHM3GTN/605.html:text/html
\endverb
\verb{url}
\verb http://bioinformatics.oxfordjournals.org/content/23/5/605
\endverb
\endentry
\entry{gallone_bio::homology::interologwalk_2011}{article}{}
\name{labelname}{4}{}{%
{{uniquename=0,hash=27922a2f1143578c924bfbc95f7e105d}{Gallone}{G\bibinitperiod}{Giuseppe}{G\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=d15f2390b5651789cbcd0a40046006c0}{Simpson}{S\bibinitperiod}{T.\bibnamedelimi Ian}{T\bibinitperiod\bibinitdelim I\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=929021a5d446519499c5c96b5b21d024}{Armstrong}{A\bibinitperiod}{J.\bibnamedelimi Douglas}{J\bibinitperiod\bibinitdelim D\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=7cf632f0acef2c5a73c73e0f9b3cec03}{Jarman}{J\bibinitperiod}{Andrew\bibnamedelima P.}{A\bibinitperiod\bibinitdelim P\bibinitperiod}{}{}{}{}}%
}
\name{author}{4}{}{%
{{uniquename=0,hash=27922a2f1143578c924bfbc95f7e105d}{Gallone}{G\bibinitperiod}{Giuseppe}{G\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=d15f2390b5651789cbcd0a40046006c0}{Simpson}{S\bibinitperiod}{T.\bibnamedelimi Ian}{T\bibinitperiod\bibinitdelim I\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=929021a5d446519499c5c96b5b21d024}{Armstrong}{A\bibinitperiod}{J.\bibnamedelimi Douglas}{J\bibinitperiod\bibinitdelim D\bibinitperiod}{}{}{}{}}%
{{uniquename=0,hash=7cf632f0acef2c5a73c73e0f9b3cec03}{Jarman}{J\bibinitperiod}{Andrew\bibnamedelima P.}{A\bibinitperiod\bibinitdelim P\bibinitperiod}{}{}{}{}}%
}
\list{language}{1}{%
{en}%
}
\strng{namehash}{36ace2586970df88b198f1d861b71eb8}
\strng{fullhash}{6cfaff9f633f7af0a18bd47276859655}
\field{sortinit}{G}
\field{labelyear}{2011}
\field{labelmonth}{07}
\field{datelabelsource}{}
\field{labeltitle}{Bio}
\field{abstract}{Protein-protein interaction ({PPI}) data are widely used to generate network models that aim to describe the relationships between proteins in biological systems. The fidelity and completeness of such networks is primarily limited by the paucity of protein interaction information and by the restriction of most of these data to just a few widely studied experimental organisms. In order to extend the utility of existing {PPIs}, computational methods can be used that exploit functional conservation between orthologous proteins across taxa to predict putative {PPIs} or 'interologs'. To date most interolog prediction efforts have been restricted to specific biological domains with fixed underlying data sources and there are no software tools available that provide a generalised framework for 'on-the-fly' interolog prediction. {PMID}: 21767381}
\field{issn}{1471-2105}
\field{journaltitle}{{BMC} Bioinformatics}
\field{month}{07}
\field{number}{1}
\field{shorttitle}{Bio}
\field{title}{Bio::-\\Homology::InterologWalk - A Perl module to build putative protein-protein interaction networks through interolog mapping}
\field{urlday}{09}
\field{urlmonth}{04}
\field{urlyear}{2014}
\field{volume}{12}
\field{year}{2011}
\field{pages}{289}
\verb{doi}
\verb 10.1186/1471-2105-12-289
\endverb
\verb{file}
\verb Full Text PDF:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/JWHSGI7T/Gallone et al. - 2011 - BioHomologyInterologWalk - A Perl module to bu.pdf:application/pdf;Snapshot:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/PQCF28F4/abstract.html:text/html
\endverb
\verb{url}
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\field{title}{{IntAct}: an open source molecular interaction database}
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\field{urlyear}{2014}
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\field{labeltitle}{A comprehensive two-hybrid analysis to explore the yeast protein interactome}
\field{abstract}{Protein–protein interactions play crucial roles in the execution of various biological functions. Accordingly, their comprehensive description would contribute considerably to the functional interpretation of fully sequenced genomes, which are flooded with novel genes of unpredictable functions. We previously developed a system to examine two-hybrid interactions in all possible combinations between the ≈6,000 proteins of the budding yeast Saccharomyces cerevisiae. Here we have completed the comprehensive analysis using this system to identify 4,549 two-hybrid interactions among 3,278 proteins. Unexpectedly, these data do not largely overlap with those obtained by the other project [Uetz, P., et al. (2000) Nature (London) 403, 623–627] and hence have substantially expanded our knowledge on the protein interaction space or interactome of the yeast. Cumulative connection of these binary interactions generates a single huge network linking the vast majority of the proteins. Bioinformatics-aided selection of biologically relevant interactions highlights various intriguing subnetworks. They include, for instance, the one that had successfully foreseen the involvement of a novel protein in spindle pole body function as well as the one that may uncover a hitherto unidentified multiprotein complex potentially participating in the process of vesicular transport. Our data would thus significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.}
\field{issn}{0027-8424, 1091-6490}
\field{journaltitle}{Proceedings of the National Academy of Sciences}
\field{month}{04}
\field{number}{8}
\field{title}{A comprehensive two-hybrid analysis to explore the yeast protein interactome}
\field{urlday}{25}
\field{urlmonth}{07}
\field{urlyear}{2014}
\field{volume}{98}
\field{year}{2001}
\field{pages}{4569\bibrangedash 4574}
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\field{datelabelsource}{}
\field{labeltitle}{Domains in proteins}
\field{journaltitle}{Methods in enzymology}
\field{shorttitle}{Domains in proteins}
\field{title}{Domains in proteins: definitions, location, and structural principles}
\field{urlday}{07}
\field{urlmonth}{08}
\field{urlyear}{2014}
\field{volume}{115}
\field{year}{1985}
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{Morgan Kaufmann Publishers Inc.}%
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\field{labeltitle}{Estimating continuous distributions in Bayesian classifiers}
\field{booktitle}{Proceedings of the Eleventh conference on Uncertainty in artificial intelligence}
\field{title}{Estimating continuous distributions in Bayesian classifiers}
\field{urlday}{29}
\field{urlmonth}{07}
\field{urlyear}{2014}
\field{year}{1995}
\field{pages}{338\bibrangedash 345}
\verb{file}
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\verb{url}
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}
\list{language}{1}{%
{English}%
}
\list{location}{1}{%
{New York}%
}
\list{publisher}{1}{%
{{McGraw}-Hill Medical}%
}
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\field{labeltitle}{Principles of Neural Science}
\field{abstract}{A Doody's Core Title for 2011! 5 {STAR} {DOODY}'S {REVIEW}! "This is a simply wonderful book that makes accessible in one place all the details of how the neuron and brain work. The writing is clear. The drawings are elegant and educational. The book is a feast for both the eye and mind. The richness, the beauty, and the complexity of neuroscience is all captured in this superb book."--Doody's Review Service Now in resplendent color, the new edition continues to define the latest in the scientific understanding of the brain, the nervous system, and human behavior. Each chapter is thoroughly revised and includes the impact of molecular biology in the mechanisms underlying developmental processes and in the pathogenesis of disease. Important features to this edition include a new chapter - Genes and Behavior; a complete updating of development of the nervous system; the genetic basis of neurological and psychiatric disease; cognitive neuroscience of perception, planning, action, motivation and memory; ion channel mechanisms; and much more.}
\field{edition}{4 edition}
\field{isbn}{9780838577011}
\field{month}{01}
\field{title}{Principles of Neural Science}
\field{year}{2000}
\endentry
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{{uniquename=0,hash=7c53dabb84c6d2e6a649e19118fb3018}{Cho}{C\bibinitperiod}{Young-Rae}{Y\bibinithyphendelim R\bibinitperiod}{}{}{}{}}%
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{{uniquename=0,hash=befcb9aeda20e39960378f72a4c094bc}{Kenley}{K\bibinitperiod}{Edward\bibnamedelima Casey}{E\bibinitperiod\bibinitdelim C\bibinitperiod}{}{}{}{}}%
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{en}%
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\strng{namehash}{086042b5ce79cf3e4000b846baa65f54}
\strng{fullhash}{086042b5ce79cf3e4000b846baa65f54}
\field{sortinit}{K}
\field{labelyear}{2011}
\field{datelabelsource}{}
\field{labeltitle}{Detecting protein complexes and functional modules from protein interaction networks}
\field{abstract}{Recent high-throughput experiments have generated protein–protein interaction data on a genomic scale, yielding the complete interactome for several organisms. Various graph clustering algorithms have been applied to protein interaction networks for identifying protein complexes and functional modules. Although the previous algorithms are scalable and robust, their accuracy is still limited because of the complex connectivity found in protein interaction networks. In this study, we propose a novel information-theoretic definition, graph entropy, as a measure of the structural complexity of a graph. Loss of graph entropy represents an increase in modularity of the graph. Based on this concept, we present a graph clustering algorithm which searches for the local optimum in modularity. The algorithm detects each optimal cluster by growing a seed in a manner that minimizes graph entropy. In the experiments with the yeast interactome, the results show that the graph entropy approach has higher accuracy in predicting protein complexes and functional modules than the best competing method. We statistically compared output clusters to both known protein complexes and Gene Ontology annotations in the biological process and molecular function categories in order to measure f-scores and p-scores as clustering accuracy. Because this algorithm is also scalable, it can be applied to the larger scale human protein interaction network.}
\field{issn}{1615-9861}
\field{journaltitle}{{PROTEOMICS}}
\field{number}{19}
\field{shorttitle}{Detecting protein complexes and functional modules from protein interaction networks}
\field{title}{Detecting protein complexes and functional modules from protein interaction networks: A graph entropy approach}
\field{urlday}{17}
\field{urlmonth}{03}
\field{urlyear}{2014}
\field{volume}{11}
\field{year}{2011}
\field{pages}{3835\bibrangedash 3844}
\verb{doi}
\verb 10.1002/pmic.201100193
\endverb
\verb{file}
\verb Full Text PDF:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/R4QRZCQZ/Kenley and Cho - 2011 - Detecting protein complexes and functional modules.pdf:application/pdf;Snapshot:/home/gavin/.mozilla/firefox/i2j16z7y.default/zotero/storage/DNFMU58J/full.html:text/html
\endverb
\verb{url}
\verb http://onlinelibrary.wiley.com/doi/10.1002/pmic.201100193/abstract
\endverb
\keyw{Bioinformatics,Functional modules,Graph clustering,Protein complexes,Protein interaction networks,Protein–protein interactions}
\endentry
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