A Unified Multitask Architecture for Predicting Local Protein Properties
- Publication Date
- March 26, 2012
- Journal
- PLOS ONE
- Authors
- Yanjun Qi, Merja Oja, Jason Weston & William Stafford Noble
- Volume
- 7
- Issue
- 3
- Pages
- e32235
- DOI
- https://dx.plos.org/10.1371/journal.pone.0032235
- Publisher URL
- http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0032235
- PubMed
- http://www.ncbi.nlm.nih.gov/pubmed/22461885
- PubMed Central
- http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3312883
- Europe PMC
- http://europepmc.org/abstract/MED/22461885
- Web of Science
- 000304118900004
- Scopus
- 84858841519
- Mendeley
- http://www.mendeley.com/research/unified-multitask-architecture-predicting-local-protein-properties
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CiteULike | Further Information
- {"link"=>{"url"=>"http://www.citeulike.org/user/Zephyrus/article/10501654"}, "post_time"=>"2013-04-04 05:08:05", "tag"=>["machine-learning", "methods", "protein"], "linkout"=>{"type"=>"DOI", "url"=>"http://dx.doi.org/10.1371/journal.pone.0032235"}, "username"=>"Zephyrus", "article_id"=>"10501654"}
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Mendeley | Further Information
{"title"=>"A unified multitask architecture for predicting local protein properties", "type"=>"journal", "authors"=>[{"first_name"=>"Yanjun", "last_name"=>"Qi", "scopus_author_id"=>"8324721600"}, {"first_name"=>"Merja", "last_name"=>"Oja", "scopus_author_id"=>"6603449946"}, {"first_name"=>"Jason", "last_name"=>"Weston", "scopus_author_id"=>"8865128200"}, {"first_name"=>"William Stafford", "last_name"=>"Noble", "scopus_author_id"=>"7102482003"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84858841519", "sgr"=>"84858841519", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0032235", "pmid"=>"22461885", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "pui"=>"364505229"}, "id"=>"c0279b98-823e-3c22-a303-20f171d01150", "abstract"=>"A variety of functionally important protein properties, such as secondary structure, transmembrane topology and solvent accessibility, can be encoded as a labeling of amino acids. Indeed, the prediction of such properties from the primary amino acid sequence is one of the core projects of computational biology. Accordingly, a panoply of approaches have been developed for predicting such properties; however, most such approaches focus on solving a single task at a time. Motivated by recent, successful work in natural language processing, we propose to use multitask learning to train a single, joint model that exploits the dependencies among these various labeling tasks. We describe a deep neural network architecture that, given a protein sequence, outputs a host of predicted local properties, including secondary structure, solvent accessibility, transmembrane topology, signal peptides and DNA-binding residues. The network is trained jointly on all these tasks in a supervised fashion, augmented with a novel form of semi-supervised learning in which the model is trained to distinguish between local patterns from natural and synthetic protein sequences. The task-independent architecture of the network obviates the need for task-specific feature engineering. We demonstrate that, for all of the tasks that we considered, our approach leads to statistically significant improvements in performance, relative to a single task neural network approach, and that the resulting model achieves state-of-the-art performance.", "link"=>"http://www.mendeley.com/research/unified-multitask-architecture-predicting-local-protein-properties", "reader_count"=>60, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>11, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>2, "Student > Master"=>11, "Other"=>4, "Student > Bachelor"=>5, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Researcher"=>11, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>18, "Student > Postgraduate"=>2, "Student > Master"=>11, "Other"=>4, "Student > Bachelor"=>5, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Unspecified"=>4, "Biochemistry, Genetics and Molecular Biology"=>9, "Materials Science"=>1, "Mathematics"=>1, "Agricultural and Biological Sciences"=>20, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Chemistry"=>1, "Computer Science"=>13}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Computer Science"=>{"Computer Science"=>13}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"New Zealand"=>1, "Canada"=>1, "Austria"=>1, "Belgium"=>1, "United States"=>6, "Norway"=>1, "South Africa"=>1, "United Kingdom"=>1, "Israel"=>1, "Spain"=>1}, "group_count"=>6}CrossRef
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Scopus | Further Information
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Relative Metric
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