Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
Publication Date
May 13, 2016
Journal
PLOS Computational Biology
Authors
Lisa M. Breckels, Sean B. Holden, David Wojnar, Claire M. Mulvey, et al
Volume
12
Issue
5
Pages
e1004920
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004920
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004920
Web of Science
000379348100026
Scopus
84975853447
Mendeley
http://www.mendeley.com/research/learning-heterogeneous-data-sources-application-spatial-proteomics-3
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Mendeley | Further Information

{"title"=>"Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics", "type"=>"journal", "authors"=>[{"first_name"=>"Lisa M.", "last_name"=>"Breckels", "scopus_author_id"=>"56023978100"}, {"first_name"=>"Sean B.", "last_name"=>"Holden", "scopus_author_id"=>"7101863607"}, {"first_name"=>"David", "last_name"=>"Wojnar", "scopus_author_id"=>"57189898977"}, {"first_name"=>"Claire M.", "last_name"=>"Mulvey", "scopus_author_id"=>"15741382300"}, {"first_name"=>"Andy", "last_name"=>"Christoforou", "scopus_author_id"=>"35067811000"}, {"first_name"=>"Arnoud", "last_name"=>"Groen", "scopus_author_id"=>"54886937100"}, {"first_name"=>"Matthew W.B.", "last_name"=>"Trotter", "scopus_author_id"=>"7006575567"}, {"first_name"=>"Oliver", "last_name"=>"Kohlbacher", "scopus_author_id"=>"6602349901"}, {"first_name"=>"Kathryn S.", "last_name"=>"Lilley", "scopus_author_id"=>"7007045074"}, {"first_name"=>"Laurent", "last_name"=>"Gatto", "scopus_author_id"=>"36937469800"}], "year"=>2016, "source"=>"PLoS Computational Biology", "identifiers"=>{"sgr"=>"84975853447", "doi"=>"10.1371/journal.pcbi.1004920", "pui"=>"610971373", "pmid"=>"27175778", "scopus"=>"2-s2.0-84975853447", "issn"=>"15537358"}, "id"=>"c8500e8d-9538-353a-a6bf-54bba36ebb4b", "abstract"=>"Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.", "link"=>"http://www.mendeley.com/research/learning-heterogeneous-data-sources-application-spatial-proteomics-3", "reader_count"=>49, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>13, "Student > Ph. D. Student"=>21, "Other"=>2, "Student > Master"=>1, "Student > Bachelor"=>4, "Lecturer"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Librarian"=>1, "Student > Doctoral Student"=>2, "Researcher"=>13, "Student > Ph. D. Student"=>21, "Other"=>2, "Student > Master"=>1, "Student > Bachelor"=>4, "Lecturer"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>15, "Agricultural and Biological Sciences"=>11, "Medicine and Dentistry"=>3, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Chemistry"=>2, "Computer Science"=>12, "Decision Sciences"=>1, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>2}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>11}, "Computer Science"=>{"Computer Science"=>12}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>15}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Finland"=>1, "United Kingdom"=>2}, "group_count"=>4}

Scopus | Further Information

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{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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