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

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

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

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 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.

Original languageEnglish
Article numberbbz041
Pages (from-to)1047-1057
Number of pages11
JournalBriefings in Bioinformatics
Volume21
Issue number3
DOIs
Publication statusPublished - 21 May 2020

Keywords

  • bioinformatics
  • integrated platform
  • sequence analysis
  • machine learning
  • automated modeling
  • data clustering
  • feature selection
  • biomedical data mining

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