Editorial: Machine learning-based methods for RNA data analysis—Volume II

Abstract

Editorial on the Research Topic Machine learning-based methods for RNA data analysis-Volume II RNAs regulate multiple biological processes including RNA transcription, splicing, stability, and translation. They play significant roles in cell biology (Connelly et al. (2016); Licatalosi and Darnell (2010); Mukherjee et al. (2022); Chen et al. (2018b)). The Encyclopedia of DNA elements project reported that only 1.5% of human genome is translated into proteins, while approximately 70%-90% is transcribed to RNAs (Falese et al. (2021)). RNAs greatly expand the range of targets from proteins to RNAs by retargeting mutated targets (Yu et al. (2019); Chen et al. (2020); Li et al. (2022); Yang et al. (2022)). Particularly, noncoding RNAs have dense linkages with human diseases including cancers. Now, RNAs have been diagnostic or prognostic markers of complex diseases (Hui et al. (2011); Xu et al. (2022); Peng et al. (2022a); Shen et al. (2022); Zhang T. et al. (2022); Chai et al. (2022)). In this topic, we aim to analyze diverse RNA data to provide clues for the diagnosis and therapy of various diseases (Dal Molin et al. (2022); Wang S. et al. (2022); Li J. et al. (2019); Liu et al. ( 2020)). Long noncoding RNAs (lncRNAs) regulate many significant biological processes (such as immune response and embryonic stem cell pluripotency) by linking to RNA-binding proteins