Deep learning for drug repurposing: Methods, databases, and applications

X Pan, X Lin, D Cao, X Zeng, PS Yu… - Wiley …, 2022 - Wiley Online Library
Drug development is time‐consuming and expensive. Repurposing existing drugs for new
therapies is an attractive solution that accelerates drug development at reduced …

Biological sequence classification: A review on data and general methods

C Ao, S Jiao, Y Wang, L Yu, Q Zou - Research, 2022 - spj.science.org
With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …

Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review

SR Choi, M Lee - Biology, 2023 - mdpi.com
Simple Summary The rapidly advancing field of deep learning, specifically transformer-
based architectures and attention mechanisms, has found substantial applicability in …

Positive-unlabeled learning in bioinformatics and computational biology: a brief review

F Li, S Dong, A Leier, M Han, X Guo, J Xu… - Briefings in …, 2022 - academic.oup.com
Conventional supervised binary classification algorithms have been widely applied to
address significant research questions using biological and biomedical data. This …

Novel insight into RNA modifications in tumor immunity: Promising targets to prevent tumor immune escape

Y Kong, J Yu, S Ge, X Fan - The Innovation, 2023 - cell.com
An immunosuppressive state is a typical feature of the tumor microenvironment. Despite the
dramatic success of immune checkpoint inhibitor (ICI) therapy in preventing tumor cell …

XGBoost framework with feature selection for the prediction of RNA N5-methylcytosine sites

Z Abbas, M ur Rehman, H Tayara, Q Zou, KT Chong - Molecular Therapy, 2023 - cell.com
5-methylcytosine (m5C) is indeed a critical post-transcriptional alteration that is widely
present in various kinds of RNAs and is crucial to the fundamental biological processes. By …

Towards retraining-free RNA modification prediction with incremental learning

J Qiao, J Jin, H Yu, L Wei - Information Sciences, 2024 - Elsevier
RNA modifications are important for deciphering the function of cells and their regulatory
mechanisms. In recent years, researchers have developed many deep learning methods to …

m6A-TSHub: Unveiling the Context-Specific m6A Methylation and m6A-Affecting Mutations in 23 Human Tissues

B Song, D Huang, Y Zhang, Z Wei, J Su… - Genomics …, 2023 - academic.oup.com
As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs
(lncRNAs), N 6-methyladenosine (m6A) RNA methylation has been shown to participate in …

DLm6Am: A deep-learning-based tool for identifying N6, 2′-O-dimethyladenosine sites in RNA sequences

Z Luo, W Su, L Lou, W Qiu, X Xiao, Z Xu - International Journal of …, 2022 - mdpi.com
N6, 2′-O-dimethyladenosine (m6Am) is a post-transcriptional modification that may be
associated with regulatory roles in the control of cellular functions. Therefore, it is crucial to …

ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species

R Chen, F Li, X Guo, Y Bi, C Li, S Pan… - Briefings in …, 2023 - academic.oup.com
A-to-I editing is the most prevalent RNA editing event, which refers to the change of
adenosine (A) bases to inosine (I) bases in double-stranded RNAs. Several studies have …