Journal article
iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Zhen Chen, Pei Zhao, Chen Li, Fuyi Li, Dongxu Xiang, Yong-Zi Chen, Tatsuya Akutsu, Roger J Daly, Geoffrey Webb, Quanzhi Zhao, Lukasz Kurgan, Jiangning Song
NUCLEIC ACIDS RESEARCH | OXFORD UNIV PRESS | Published : 2021
DOI: 10.1093/nar/gkab122
Abstract
Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate understanding of the sequence(-structure)-function paradigm for DNAs, RNAs and proteins. Rapid accumulation of sequences requires equally pervasive development of new predictive models, which depends on the availability of effective tools that support these efforts. We introduce iLearnPlus, the first machine-learning platform with graphical- and web-based interfaces for the construction of machine-learning pipelines for analysis and predictions using nucleic acid and protein sequences. iLearnPlus provides a comprehensive set of algorithms and automates sequence-based feature extraction and analysis, co..
View full abstractGrants
Awarded by National Health and Medical Research Council of Australia (NHMRC)
Awarded by Young Scientists Fund of the National Natural Science Foundation of China
Awarded by National Natural Science Foundation of China
Awarded by Australian Research Council
Awarded by National Institute of Allergy and Infectious Diseases of the National Institutes of Health
Awarded by Fundamental Research Funds for the Central Universities
Awarded by National Natural Science Foundation of Liaoning Province
Awarded by NHMRC CJ Martin Early Career Research Fellowship
Funding Acknowledgements
National Health and Medical Research Council of Australia (NHMRC) [APP1127948, APP1144652]; Young Scientists Fund of the National Natural Science Foundation of China [31701142]; National Natural Science Foundation of China [31971846]; Australian Research Council [LP110200333, DP120104460]; National Institute of Allergy and Infectious Diseases of the National Institutes of Health [R01 AI111965]; Major Inter-Disciplinary Research project awarded by Monash University, and the Collaborative Research Program of Institute for Chemical Research, Kyoto University; Fundamental Research Funds for the Central Universities [3132020170, 3132019323]; National Natural Science Foundation of Liaoning Province [20180550307]; C.L. is supported by an NHMRC CJ Martin Early Career Research Fellowship [1143366]; L.K. is supported in part by the Robert J. Mattauch Endowment funds. Funding for open access charge: Grants in support of this submission.