Naman Kumar Mehta

AntiCapt uses fine-tuned nucleotide language models to predict and design anticancer aptamers, achieving high accuracy in distinguishing them from non-aptamers.

CPPLocPred predicts subcellular localization of cell-penetrating peptides using a hierarchical machine learning framework, achieving high accuracy on validation datasets.

PEPstrMOD2 improves peptide structure prediction by using deep learning and expanding chemical modifications support, outperforming previous methods on benchmark datasets.

This paper introduces IonNTXpred, a PLM-based method for predicting ion channel modulators, achieving high accuracy and supporting drug discovery and safety evaluation.

HAIRpred2 predicts antibody-interacting residues in human antigens using structural features, achieving an AUC of 0.78 on an independent test set.

The paper develops a tool to predict, design, and scan IL-2-inducing peptides using machine learning, deep learning, and ensemble methods, achieving high accuracy on multiple datasets.

This paper benchmarks quantum SVM and classical ML algorithms for predicting therapeutic proteins, finding CML outperforms QSVM but both show promise in peptide prediction tasks.

The paper introduces IL4Pred2, a host-specific method for predicting IL-4 inducing peptides in human and mouse, achieving high AUC and MCC scores on independent datasets.

The paper uses large language models to predict pancreatic ductal adenocarcinoma from gene expression data by converting numerical features into peptide sequences.

HemoPI2.0 predicts peptide hemolysis and pHC50 from sequence; a hybrid of MERCI motif scoring and ESM2-t6 reaches AUC 0.909 on held-out peptides, beating the earlier tools tested.

This paper introduces CytoLNCpred, a method to predict cytoplasm-associated lncRNAs in 15 human cell lines using machine learning and DNABERT-2 embeddings, showing correlation-based models outperform LLM-based ones.