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Bioinformatics (Oxford, England)|December 28, 2018
mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representationBalachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Briefings in Bioinformatics|June 12, 2026
CONTRA-IL6: an interpretable hybrid convolutional neural network and Transformer framework for accurate prediction of interleukin-6-inducing peptides using protein language modelsDuong Thanh Tran, Nhat Truong Pham, Gwang Lee, et al.
Current Protein & Peptide Science|January 21, 2020
Evolution of Machine Learning Algorithms in the Prediction and Design of Anticancer PeptidesShaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Frontiers in Physiology|August 17, 2011
Similar Structures but Different Roles - An Updated Perspective on TLR StructuresBalachandran Manavalan, Shaherin Basith, Sangdun Choi
Bioinformatics (Oxford, England)|March 8, 2020
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representationMd Mehedi Hasan, Nalini Schaduangrat, Shaherin Basith, et al.
Current Medicinal Chemistry|September 3, 2021
Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation SitesShaherin Basith, Hye Jin Chang, Saraswathy Nithiyanandam, et al.
Molecular Therapy. Nucleic Acids|November 24, 2020
Empirical Comparison and Analysis of Web-Based DNA N 4-Methylcytosine Site Prediction ToolsBalachandran Manavalan, Md Mehedi Hasan, Shaherin Basith, et al.
Cells|October 31, 2019
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-methylcytosine Sites in the Mouse GenomeBalachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Oncotarget|November 5, 2017
MLACP: machine-learning-based prediction of anticancer peptidesBalachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Briefings in Bioinformatics|September 10, 2020
Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning frameworkMd Mehedi Hasan, Shaherin Basith, Mst Shamima Khatun, et al.
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