Nonnegative Matrix Factorization: An Analytical and Interpretive Tool in Computational Biology
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{"title"=>"Nonnegative matrix factorization: An analytical and interpretive tool in computational biology", "type"=>"generic", "authors"=>[{"first_name"=>"Karthik", "last_name"=>"Devarajan", "scopus_author_id"=>"8898017600"}], "year"=>2008, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1000029", "sgr"=>"48249151183", "issn"=>"1553734X", "pui"=>"352076424", "isbn"=>"1553-7358 (Electronic)\\r1553-734X (Linking)", "pmid"=>"18654623", "scopus"=>"2-s2.0-48249151183"}, "id"=>"ed38cb47-d64d-33f6-a71f-09ecc790ec03", "abstract"=>"In the last decade, advances in high-throughput technologies such as DNA microarrays have made it possible to simultaneously measure the expression levels of tens of thousands of genes and proteins. This has resulted in large amounts of biological data requiring analysis and interpretation. Nonnegative matrix factorization (NMF) was introduced as an unsupervised, parts-based learning paradigm involving the decomposition of a nonnegative matrix V into two nonnegative matrices, W and H, via a multiplicative updates algorithm. In the context of a pxn gene expression matrix V consisting of observations on p genes from n samples, each column of W defines a metagene, and each column of H represents the metagene expression pattern of the corresponding sample. NMF has been primarily applied in an unsupervised setting in image and natural language processing. More recently, it has been successfully utilized in a variety of applications in computational biology. Examples include molecular pattern discovery, class comparison and prediction, cross-platform and cross-species analysis, functional characterization of genes and biomedical informatics. In this paper, we review this method as a data analytical and interpretive tool in computational biology with an emphasis on these applications.", "link"=>"http://www.mendeley.com/research/nonnegative-matrix-factorization-analytical-interpretive-tool-computational-biology", "reader_count"=>359, "reader_count_by_academic_status"=>{"Unspecified"=>8, "Professor > Associate Professor"=>29, "Librarian"=>1, "Researcher"=>87, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>123, "Student > Postgraduate"=>11, "Student > Master"=>32, "Other"=>12, "Student > Bachelor"=>14, "Lecturer"=>11, "Lecturer > Senior Lecturer"=>1, "Professor"=>17}, "reader_count_by_user_role"=>{"Unspecified"=>8, "Professor > Associate Professor"=>29, "Librarian"=>1, "Researcher"=>87, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>123, "Student > Postgraduate"=>11, "Student > Master"=>32, "Other"=>12, "Student > Bachelor"=>14, "Lecturer"=>11, "Lecturer > Senior Lecturer"=>1, "Professor"=>17}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Agricultural and Biological Sciences"=>142, "Arts and Humanities"=>1, "Chemical Engineering"=>2, "Chemistry"=>4, "Computer Science"=>73, "Earth and Planetary Sciences"=>4, "Engineering"=>24, "Environmental Science"=>8, "Biochemistry, Genetics and Molecular Biology"=>36, "Mathematics"=>20, "Medicine and Dentistry"=>14, "Neuroscience"=>5, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>6, "Psychology"=>1, "Social Sciences"=>3, "Immunology and Microbiology"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>14}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>6}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>20}, "Unspecified"=>{"Unspecified"=>12}, "Environmental Science"=>{"Environmental Science"=>8}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "Chemical Engineering"=>{"Chemical Engineering"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>1}, "Engineering"=>{"Engineering"=>24}, "Chemistry"=>{"Chemistry"=>4}, "Neuroscience"=>{"Neuroscience"=>5}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>4}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>142}, "Computer Science"=>{"Computer Science"=>73}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>36}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "Colombia"=>1, "Argentina"=>1, "United States"=>22, "Japan"=>1, "United Kingdom"=>5, "Russia"=>1, "Spain"=>1, "Greece"=>1, "Canada"=>2, "Netherlands"=>1, "Belgium"=>1, "Norway"=>1, "China"=>1, "Ireland"=>1, "Italy"=>3, "Mexico"=>2, "South Africa"=>1, "Slovenia"=>2, "Israel"=>1, "Australia"=>4, "France"=>2, "Germany"=>7}, "group_count"=>13}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/925636"], "description"=>"<p>Heat map of re-ordered consensus matrix, <i>k</i> = 3.</p>", "links"=>[], "tags"=>["re-ordered"], "article_id"=>596089, "categories"=>["Genetics", "Infectious Diseases", "Mathematics", "Neuroscience", "Molecular Biology", "Biophysics", "Cancer", "Astronomy, Astrophysics, Space Science", "Medicine"], "users"=>["Karthik Devarajan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000029.g005", "stats"=>{"downloads"=>4, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Heat_map_of_re_ordered_consensus_matrix_k_3_/596089", "title"=>"Heat map of re-ordered consensus matrix, <i>k</i> = 3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-21 09:54:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/925779"], "description"=>"<p>Distribution of metagene coefficients: Leukemia data, <i>k</i> = 2.</p>", "links"=>[], "tags"=>["metagene", "leukemia"], "article_id"=>596232, "categories"=>["Genetics", "Infectious Diseases", "Mathematics", "Neuroscience", "Molecular Biology", "Biophysics", "Cancer", "Astronomy, Astrophysics, Space Science", "Medicine"], "users"=>["Karthik Devarajan"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000029.t001", "stats"=>{"downloads"=>4, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_metagene_coefficients_Leukemia_data_k_2_/596232", "title"=>"Distribution of metagene coefficients: Leukemia data, <i>k</i> = 2.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-21 09:55:29"}
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Relative Metric

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