Subha Madhavan

The paper describes the evolution of the CIViC knowledgebase to meet the needs of the cancer variant interpretation community by expanding its functionality and data types.

This study analyzes the lung tissue microenvironment of COVID-19 patients to explore interactions between viral infection, immune response, and microbiome using multi-omics data.

MACE2K extracts variant-disease association evidence from biomedical literature using machine learning models, achieving high recall and precision in identifying relevant sentences.

The paper maps allele-specific DNA methylation in normal and cancer tissues, identifying disease-associated regulatory SNPs that are missed by other methods.

The paper harmonizes clinical interpretations of cancer genomic variants across six knowledgebases, improving overlap and matching with patient cohorts.

CDGnet recommends targeted cancer therapies using a network approach that integrates biological pathway information and patient-specific molecular data.

The paper describes the CIViC knowledge model and curation procedures for systematically organizing and interpreting cancer-related genetic variants.

SNP2SIM provides a modular workflow for generating molecular simulations of protein variants to support functional analysis and drug development.

The paper analyzes immune cell abundance in brain cancer using gene expression and copy number data from the REMBRANDT project to compare with TCGA datasets and assess clinical relevance.

viGEN detects and quantifies viral RNA and variants in tumor samples using RNA-seq data, showing successful detection of HPV in cervical cancer and HBV in liver cancer.

eGARD extracts associations between genomic anomalies and drug responses from text with high precision and recall.

The paper describes the development of standards for cancer variant interpretation and sharing through the ClinGen Cancer Somatic Working Group.

The paper introduces neoantigeR, a pipeline for identifying tumor-specific peptide epitopes (neoantigens) from sequencing data to aid in cancer immunotherapy.