Gajendra P. S. Raghava

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 develops a machine learning model and software tool to predict NF-κB inhibitors from chemical libraries, achieving reliable predictions validated against experimental data.

The paper develops a hybrid model combining alignment-based and alignment-free methods to predict plant resistance proteins, achieving high AUROC scores and creating an online platform for this purpose.

The paper develops a hybrid method to predict IFN-gamma inducing peptides in human and mouse, achieving higher AUROC scores than existing methods.

This paper introduces HLA-DR4Pred2, an improved method for predicting HLA-DRB1*04:01 binders using a larger dataset and hybrid approaches, achieving higher AUC scores than existing methods.

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.

This paper develops a machine learning model to predict antifreeze proteins using evolutionary information, achieving higher accuracy than existing methods on an independent dataset.

This study develops a hybrid method combining alignment-based and alignment-free approaches to predict transcription factors with high accuracy, achieving an AUC of 0.99 on an independent dataset.

This paper introduces ToxinPred 3.0, an improved method for predicting peptide toxicity using alignment-free machine/deep learning and ensemble approaches, achieving high accuracy and sensitivity.

The paper develops a model to predict MIC values of peptides against E. coli using machine learning and a web server for peptide design.

This paper develops a deep learning model using single-cell transcriptomics to classify HNSCC and distinguish HPV+ from HPV- patients, achieving high AUROC scores on a validation dataset.

The paper develops a hybrid model combining machine learning and similarity-based methods to predict hormonal peptides, achieving high accuracy on a dataset from plants and animals.

This paper introduces AntiBP3, a hybrid method for predicting antibacterial peptides effective against gram-positive, gram-negative, and gram-variable bacteria, achieving high AUC scores on independent datasets.

The paper develops a hybrid model for predicting both linear and conformational B-cell epitopes using machine learning and BLAST, achieving an AUC of 0.83 on an independent dataset.

The paper develops a logistic regression model with a propensity index panel of ten genes to accurately screen prostate cancer, achieving 96.36% accuracy and 0.99 AUROC.

The paper describes the development of MPDSCOVID-19, a disease-specific web portal for computational drug discovery, with updated modules and tools for COVID-19 research.

This paper develops a hybrid model combining motif-based and machine learning approaches to predict exosomal proteins with high accuracy, achieving AUROC of 0.85 on an independent dataset.

This study develops a deep learning model to predict Alzheimer's disease using single-cell transcriptomics data, achieving an AUC of 0.75-0.84 on validation datasets.

Hmrbase2 is an updated database containing hormone and receptor information from 803 organisms, with improved data content and search features.

This paper develops methods to predict IL-5 inducing peptides using alignment-based and alignment-free approaches, achieving high performance with a hybrid model.

This paper develops computational tools to predict celiac disease-associated epitopes and motifs in proteins, achieving high accuracy using machine learning and motif-based approaches.

This paper develops a fast method for predicting multi-label subcellular localization of mRNA at genome scale, achieving high AUROC scores using hybrid techniques.

The paper introduces Pprint2, a method using convolutional neural networks and evolutionary profiles to predict RNA-interacting residues in proteins, achieving an AUC of 0.82.

This paper develops in-silico models to predict infectious Influenza A virus strains from genome and protein sequences, achieving high AUC scores and providing a web tool for pandemic preparedness.

The paper introduces a software package that computes over 50,000 features for protein/peptide function prediction, including residue-level annotations.

DBpred predicts DNA-interacting residues in proteins using deep learning, achieving improved AUROC scores compared to existing methods.

This paper develops models to predict, scan, and design TNF-α inducing epitopes for human and mouse, achieving AUROC values up to 0.83 for human and 0.77 for mouse.

This paper develops methods to predict and design T1DM-associated peptides, achieving high accuracy with a hybrid approach and a webserver for peptide prediction.

This study identifies penetratin as an inhibitor of α-synuclein aggregation and shows it improves locomotor function in Parkinson's disease mouse models.

This paper develops an ensemble model to predict phage-host interactions for phage therapy, achieving higher accuracy than existing methods across multiple taxonomic levels.