Jehad Aldahdooh

SOORENA: Self-lOOp containing or autoREgulatory Nodes in biological network Analysis

SOORENA identifies autoregulatory mechanisms in biological networks by analyzing PubMed abstracts, finding 97,657 protein-specific records and generating a curated database.

Mining drug-target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model.

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.

A gene essentiality signature enables predicting the mechanism of action of drugs

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

Using BERT to identify drug-target interactions from whole PubMed

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.

DrugRepo: A novel approach to repurpose a huge collection of compounds based on chemical and genomic features

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: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

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

Minimal information for Chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments

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.

Can we assume the gene expression profile as a proxy for signaling network activity?

This study evaluates whether gene expression profiles can reliably infer signaling network activity and finds they are not more coherent with causal relationships than random data.