sRNAdeep: a novel tool for bacterial sRNA prediction based on DistilBERT encoding mode and deep learning algorithms

• Published in: BMC genomics Vol. 25; no. 1; pp. 1021 - 14
• Main Authors: Qian, Weiye, Sun, Jiawei, Liu, Tianyi, Yang, Zhiyuan, Tsui, Stephen Kwok-Wing
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 31.10.2024; BioMed Central; BMC
• Subjects: Accuracy; Algorithms; Analysis; Anopheles; Bacteria; Bacterial genetics; Bacterial sRNA; Care and treatment; Computational Biology - methods; Computational linguistics; Data mining; Datasets; Deep Learning; Drug metabolism; Drug resistance; E coli; Experimental methods; Gene expression; Genes; Genome analysis; Genome, Bacterial; Genomics; Health aspects; Language processing; Machine learning; Methods; Mycobacterium tuberculosis; Mycobacterium tuberculosis - genetics; Natural language interfaces; Performance evaluation; Physiological aspects; Prediction models; Prevention; Proteins; RNA; RNA, Bacterial - genetics; RNA, Small Untranslated - genetics; Software; Sparsity; Support vector machines; Therapeutic targets; China; Deep learning; Genome analysis; Mycobacterium tuberculosis; Bacterial sRNA

Bacterial small regulatory RNA (sRNA) plays a crucial role in cell metabolism and could be used as a new potential drug target in the treatment of pathogen-induced disease. However, experimental methods for identifying sRNAs still require a large investment of human and material resources. In this s...