Nisha Bajiya

AntiOxiPred: Predicting Antioxidant Peptides using Machine Learning Techniques and BLAST

AntiOxiPred predicts antioxidant peptides using machine learning and BLAST, achieving high AUC and MCC on testing and external validation datasets.

AntiCapt: Fine-Tuned Nucleotide Language Models for Predicting and Designing Anticancer Aptamers

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

AptViralDB: A Repository of Experimentally Validated Antiviral Aptamers

AptViralDB is a manually curated database of 1,768 experimentally validated antiviral aptamers against 40 viruses and 104 targets, offering annotations, predicted structures, and search tools.

CPPLocPred: Subcellular Localization of Cell-Penetrating Peptides

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

AptBacterialDB: A Comprehensive, Manually Curated Database of Antibacterial Aptamers

AptBacterialDB is a manually curated database of antibacterial aptamers with detailed annotations and integration tools to support research and development.

AptCancerDB: A Curated Knowledgebase and Translational Discovery Platform for Anticancer Aptamers

AptCancerDB curates experimentally validated anticancer aptamers, providing a resource for discovery and development of diagnostics and therapeutics.

Prediction of lncRNA-protein interacting pairs using LLM embeddings based on evolutionary information

The paper predicts lncRNA-protein interacting pairs using embeddings from DNABERT-2 and ESM-2-t30, achieving high accuracy on an independent dataset.

In Silico Tool for Predicting and Scanning Rheumatoid Arthritis-Inducing Peptides in an Antigen

This paper develops models to predict RA-inducing peptides using machine learning, deep learning, and large language models, achieving high AUC scores and implementing them in a web tool.

Prediction of plant resistance proteins using alignment-based and alignment-free approaches

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.

Prediction of anti-freezing proteins from their evolutionary profile

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

Prediction of inhibitory peptides against E. coli with desired MIC value

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

AntiBP3: A hybrid method for predicting antibacterial peptides against gram-positive/negative/variable bacteria

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.

A hybrid approach for predicting multi-label subcellular localization of mRNA at genome scale

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