Editorial [Hot Topic: Machine Learning Models in Protein Bioinformatics (Guest Editors: Lukasz Kurgan & Yaoqi Zhou)

Abstract

Bioinformatics is a relatively new field concerned with the computational analysis and prediction of properties of biomolecules, DNA, RNA, and proteins, in particular, on a genomic/proteomic scale. Machine learning models play increasingly important roles in development of novel methodologies, summarization, and high-throughput analysis in the bioinformatics field. Advances in the related area, including protein structure and function prediction [1, 2], structural bioinformatics [3], and peptide analysis [4] were recently summarized, and several works that overview specific sub-areas of protein bioinformatics, such as prediction of secondary structure [5, 6], helical transmembrane proteins [7], localization and targeting [8], binding sites [9, 10], and RNA-binding [11], were published in the last couple of years. This issue provides a comprehensive overview of current efforts related to the analysis of protein data, from sequences to structures to functions. It consists of two parts, the first with five reviews and the second that includes seven original methodology papers.