Semi-supervised Learning Predicts Approximately One Third of the Alternative Splicing Isoforms as Functional Proteins

Cell Reports

Volumn 12, Issue 2, 2015, Pages 183-189

  Hao, Yanqi ^a,b   Colak, Recep ^a,b   Teyra, Joan ^a   Corbi Verge, Carles ^a   Ignatchenko, Alexander ^a   Hahne, Hannes ^c   Wilhelm, Mathias ^c   Kuster, Bernhard ^c,d,e   Braun, Pascal ^c   Kaida, Daisuke ^f   Kislinger, Thomas ^a,g   Kim, Philip M ^a,b  

^a UNIVERSITY OF TORONTO   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^d GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^e Munich Center for Integrated Protein Science   (Germany)

^f UNIVERSITY OF TOYAMA   (Japan)

^g PRINCESS MARGARET HOSPITAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEIN VARIANT; ISOPROTEIN; PROTEIN;

ALTERNATIVE RNA SPLICING; ARTICLE; COMPUTER PREDICTION; EXON SKIPPING; LEARNING ALGORITHM; MACHINE LEARNING; MASS SPECTROMETRY; MEASUREMENT ACCURACY; PRIORITY JOURNAL; PROTEIN FOLDING; RECEIVER OPERATING CHARACTERISTIC; SEMI SUPERVISED LEARNING ALGORITHM; STRUCTURE ANALYSIS; AREA UNDER THE CURVE; CHEMISTRY; EXON; GENETICS; HUMAN; METABOLISM; PROTEIN TERTIARY STRUCTURE; SUPERVISED MACHINE LEARNING;

ALTERNATIVE SPLICING; AREA UNDER CURVE; EXONS; HUMANS; PROTEIN ISOFORMS; PROTEIN STRUCTURE, TERTIARY; PROTEINS; ROC CURVE; SUPERVISED MACHINE LEARNING;

EID: 84937526994     PISSN: None     EISSN: 22111247     Source Type: Journal    
DOI: 10.1016/j.celrep.2015.06.031     Document Type: Article

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