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

• Published in: Briefings in bioinformatics Vol. 21; no. 3; pp. 1047 - 1057
• Main Authors: Chen, Zhen, Zhao, Pei, Li, Fuyi, Marquez-Lago, Tatiana T, Leier, André, Revote, Jerico, Zhu, Yan, Powell, David R, Akutsu, Tatsuya, Webb, Geoffrey I, Chou, Kuo-Chen, Smith, A Ian, Daly, Roger J, Li, Jian, Song, Jiangning
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 21.05.2020; Oxford Publishing Limited (England)
• Subjects: Algorithms; Amino acid sequence; Bioinformatics; Clustering; Computer applications; Deoxyribonucleic acid; DNA; Engineering education; Feature extraction; Gene sequencing; Internet; Learning algorithms; Machine learning; Nucleotide sequence; Proteins; Reduction; Ribonucleic acid; RNA; Software; Toolkits; automated modeling; bioinformatics; data clustering; sequence analysis; integrated platform; machine learning; biomedical data mining; feature selection

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....