IPMiner: Hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction

BMC Genomics

Volumn 17, Issue 1, 2016, Pages

  Pan, Xiaoyong ^a,d   Fan, Yong Xian ^b   Yan, Junchi ^c   Shen, Hong Bin ^a  

^a Department of Automation   (China)

^b GUILIN UNIVERSITY OF ELECTRONIC TECHNOLOGY   (China)

^c EAST CHINA NORMAL UNIVERSITY   (China)

^d UNIVERSITY OF COPENHAGEN   (Denmark)

Author keywords

Deep learning; NcRNA; NcRNA protein; Stacked ensembing

Indexed keywords

UNTRANSLATED RNA; PROTEIN BINDING; RNA BINDING PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; CLASSIFIER; COMPUTER PREDICTION; CORRELATION COEFFICIENT; DATA MINING; HUMAN; INTERMETHOD COMPARISON; MEASUREMENT ACCURACY; MEASUREMENT PRECISION; NONHUMAN; PROTEIN RNA BINDING; RANDOM FOREST; RNA SEQUENCE; SENSITIVITY AND SPECIFICITY; AREA UNDER THE CURVE; BIOLOGY; CLUSTER ANALYSIS; GENETICS; METABOLISM; PROCEDURES; REPRODUCIBILITY; SOFTWARE;

AREA UNDER CURVE; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; PROTEIN BINDING; REPRODUCIBILITY OF RESULTS; RNA, UNTRANSLATED; RNA-BINDING PROTEINS; SOFTWARE;

EID: 84981263658     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-016-2931-8     Document Type: Article

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