Editorial: 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 (Wapinski and Chang (2011); Chen and Huang (2017); Ping et al. (2018); Wang et al. (2020)), Wang et al. (2021W.); Peng et al. (2020)). They have been important biomarkers for cancers (Wu et al. (2022a); Banerjee et al. (2020); Zhang S. et al. (2021); Zhou G. et al. (2021); Peng et al. (2022a); Liang et al. (2022b); Peng et al. (2021); Zhou L. et al. (2021)). For example, lncRNAs AFAP1-AS1, CCAT1, CYTOR, GAS5, HOTAIR, and PVT1 are molecular regulators of lung caner (Aftabi et al. (2021)). KCNQ1OT1may be a prognostic biomarker in colorectal cancer (Lin et al. (2021)). lncRNAs are also oncogenes (such as MKLN1-AS, GHET1, LASP1-AS, MALAT1, HULC, HOTAIR, and PAPAS) and tumor suppressors (such as CASC2, DGCR5, MEG3, GAS5, and NRON) in hepatocellular carcinoma (Guo et al. (2021)). Many machine learning methods have been proposed to OPEN ACCESS