iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data

文献类型： 外文期刊

第一作者： Chen, Zhen

作者： Chen, Zhen;Zhao, Pei;Li, Fuyi;Zhu, Yan;Daly, Roger J.;Song, Jiangning;Marquez-Lago, Tatiana T.;Leier, Andre;Revote, Jerico;Li, Jian;Powell, David R.;Akutsu, Tatsuya;Webb, Geoffrey, I;Chou, Kuo-Chen;Smith, A. Ian

作者机构：

关键词： bioinformatics; integrated platform; sequence analysis; machine learning; automated modeling; data clustering; feature selection; biomedical data mining

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：11.622； 五年影响因子：10.288 ）

ISSN： 1467-5463

年卷期： 2020 年 21 卷 3 期

页码：

收录情况： SCI

摘要： 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 reduction, predictor construction, best descriptor/model selection, ensemble learning and results visualization for DNA, RNA and protein sequences. iLearn was designed for users that only want to upload their data set and select the functions they need calculated from it, while all necessary procedures and optimal settings are completed automatically by the software. iLearn includes a variety of descriptors for DNA, RNA and proteins, and four feature output formats are supported so as to facilitate direct output usage or communication with other computational tools. In total, iLearn encompasses 16 different types of feature clustering, selection, normalization and dimensionality reduction algorithms, and five commonly used machine-learning algorithms, thereby greatly facilitating feature analysis and predictor construction. iLearn is made freely available via an online web server and a stand-alone toolkit.

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