DMIL-IsoFun: predicting isoform function using deep multi-instance learning
Author(s) -
Guoxian Yu,
Guangjie Zhou,
Xiangliang Zhang,
Carlotta Domeniconi,
Maozu Guo
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab532
Subject(s) - gene isoform , alternative splicing , computational biology , computer science , focus (optics) , function (biology) , source code , gene , artificial intelligence , biology , genetics , physics , optics , operating system
Alternative splicing creates the considerable proteomic diversity and complexity on relatively limited genome. Proteoforms translated from alternatively spliced isoforms of a gene actually execute the biological functions of this gene, which reflect the functional knowledge of genes at a finer granular level. Recently, some computational approaches have been proposed to differentiate isoform functions using sequence and expression data. However, their performance is far from being desirable, mainly due to the imbalance and lack of annotations at isoform-level, and the difficulty of modeling gene-isoform relations.
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