Sayoni Das

This paper identifies 22,411 reproducible genetic signatures associated with ME in DecodeME cohorts and finds significant overlap with long COVID genes, suggesting shared mechanisms but distinct diseases.

This paper identifies and validates novel combinatorial genetic risk factors for endometriosis in UK and US cohorts, showing high reproducibility across diverse populations.

This study reproduces genetic risk factors for long COVID identified in a European ancestry cohort across ancestrally diverse populations, showing significant enrichment of disease signatures and supporting drug repurposing candidates.

This paper introduces a method to detect genetic variants that reduce disease risk in high-risk individuals, identifying protective signatures that could inform drug targets and precision medicine.

This paper identifies 73 genes associated with severe or fatigue dominant long COVID through combinatorial analysis and finds overlaps with ME/CFS and potential drug targets.

The paper introduces FunSite, a machine learning method for predicting functional sites in proteins using sequence, structure, and evolutionary data from CATH functional families, showing superior performance over existing methods.

The paper presents a drug-target-mechanism graph to identify synergistic drug combinations for SARS-CoV-2 treatment, validated through experimental testing.

This study identifies 68 genetic risk factors associated with severe COVID-19 using combinatorial analysis, highlighting pathways like calcium signaling and potential drug targets.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.