Khader Shameer

This paper analyzes 100,526 GWAS to identify pleiotropic genes and their therapeutic implications, finding that highly pleiotropic genes are associated with safety liabilities and that combining PAV support with intermediate pleiotropy improves therapeutic success.

This study identifies genetic risk factors associated with severe psoriasis and shows that polygenic risk scores can predict severe disease outcomes better than traditional risk factors.

This study identifies 440 genes/proteins associated with atopic dermatitis severity through multi-omics analyses and highlights four with significant cross-evidence, including CEP85, which may contribute to severity via impaired phagocytosis.

This study identifies genetic risk loci and candidate genes for inflammatory polyneuropathy and CIDP through GWAS and functional analyses.

This study conducts a GWAS to analyze acne severity, identifying two new loci and six novel genes associated with severity, while showing limited overlap with known acne risk loci.

This study identifies therapeutic targets by integrating GWAS with multi-omics data, finding enriched causal genes and novel associations like IL6ST in polymyalgia rheumatica.

The paper introduces the Pleiotropic Variability Score (PVS) to quantify the phenomic associations of genomic variants using semantic reasoning and validates it with electronic health records.

The paper describes a machine intelligence competition aimed at improving 3D domain swapping prediction, resulting in enhanced predictive modeling through crowdsourced solutions.

The paper introduces an R package for calculating fragility index in clinical trials and extends the method to survival analysis and logistic regression.

This study uses machine learning to prioritize small molecules for drug repositioning by analyzing chemical properties and leveraging public drug databases.