Journal article
ILearn: An integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
Z Chen, P Zhao, F Li, TT Marquez-Lago, A Leier, J Revote, Y Zhu, DR Powell, T Akutsu, GI Webb, KC Chou, AI Smith, RJ Daly, J Li, J Song
Briefings in Bioinformatics | OXFORD UNIV PRESS | Published : 2020
DOI: 10.1093/bib/bbz041
Abstract
With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficient, accurate and high-throughput manner. A number of online web servers and stand-alone tools have been developed to address this to date; however, all these tools have their limitations and drawbacks in terms of their effectiveness, user-friendliness and capacity. Here, we present iLearn, a comprehensive and versatile Python-based toolkit, integrating the functionality of feature extraction, clustering, normalization, selection, dimensionality r..
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Grants
Awarded by National Institutes of Health
Funding Acknowledgements
This work was supported by grants from the National Health and Medical Research Council of Australia (NHMRC) (APP1127948, APP1144652 and APP490989), the Young Scientists Fund of the National Natural Science Foundation of China (31701142), the Australian Research Council (LP110200333 and DP120104460), the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965), a Major Inter-Disciplinary Research project awarded by Monash University, and the Collaborative Research Program of Institute for Chemical Research, Kyoto University (2018-28). T.M.L. and A.L.'s work was supported in part by the Informatics Institute of the School of Medicine at the University of Alabama at Birmingham. R.J.D. and J.L. are NHMRC principal research fellows.