Ziaurrehman Tanoli

CoTRA provides an R/Shiny framework for bulk and single-cell RNA-seq analysis, offering integrated workflows and reproducibility across 49 criteria.

The paper identifies aldehyde metabolism as a therapeutic target in Fanconi anemia using a multi-omics approach integrating CRISPR data and transcriptomics.

PrimeKG-Plus updates the PrimeKG knowledge graph with new data and rare-disease information, improving drug-disease connectivity and temporal relevance.

The paper develops an ML-powered drug discovery pipeline to design selective CK2 inhibitors with favorable off-target profiles, achieving a 90% hit-rate.

This paper compares drug-target interaction databases to identify repurposing opportunities and presents a pathway-based pipeline for predicting drug repositioning in cancer.

ODDB integrates ocular disease gene-drug relationships to provide a comprehensive resource for molecular mechanisms and therapeutic opportunities.

DLRNA-BERTa predicts RNA-drug binding affinities using a transformer model, achieving high Pearson correlations across RNA classes and identifying 2,859 compounds with predicted affinities.

The paper introduces a control-independent QC method to detect spatial artefacts in drug screening experiments, improving reproducibility between technical replicates and cross-dataset correlation.

The paper develops two annotation pipelines and dashboards to integrate chemical and bioactivity data for drug repurposing, validated on a compound library with over 5000 compounds.

This paper introduces an attention-based method to predict drug-target interactions across seven superfamilies, achieving high correlation and identifying missing activities in approved drugs.

Peptriever uses a bi-encoder transformer model to efficiently rank proteins interacting with peptides, achieving high AUC-ROC and AUC-PR scores.

The paper develops an ensemble transformer model using gene and chemical descriptions to extract drug-target interactions from biomedical literature, achieving high performance on a benchmark dataset.

This study introduces a gene essentiality signature to predict drug mechanisms of action and target interactions, showing improved accuracy over existing methods.

This paper uses BERT to identify and extract drug-target interactions from PubMed, finding 2.1 million previously unlisted studies with ~99% accuracy in detecting drug-target pairs.

This paper introduces a drug repurposing method using chemical and genomic data to score compounds for 674 cancer types and other diseases, with results matching ongoing clinical trials.

SynergyFinder Plus improves interpretation and annotation of drug combination screening data through updated methods and a web interface.

MICHA is a pipeline that FAIRifies chemosensitivity assays by automatically annotating compounds, samples, reagents, and data processing methods, enabling interoperable and reusable drug screening data.

MediSyn synthesizes biomedical datasets to support drug treatment selection by visualizing drug-target relations and uncertainties.