Two machine-learning models for predicting ΔΔG0 of TMHs and TMBs using sequence, structure, and energetic features are developed and found that disease-associated mutations were predicted to be destabilizing more frequently than benign mutations.
P. Reddy, A. Kulandaisamy, M. Gromiha· Journal of Molecular Biology· 0 citations
This chapter covers quality control, normalization, sparse data handling, and transcript quantification for both bulk and snRNA-seq, along with strategies to address challenges such as multi-mapped reads and batch effects, and examines how artificial intelligence and machine learning techniques can improve data process...
S. P. Dharshini, Y.-H. Taguchi, M. Gromiha· Methods in molecular biology· 0 citations
A machine learning model is trained, DBP-CanPred, to identify driver mutations in DBPs using the sequence-derived evolutionary features, as well as structure-based features such as mutation-perturbed structural descriptors, which contributes to understanding mutation patterns in DNA-binding proteins and supports varian...
A. Phogat, Sowmya Ramaswamy Krishnan, Medha Pandey et al.· Frontiers in Bioinformatics· 0 citations
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