Susanna Kiwala

The paper develops a server enabling large language models to access drug-gene interaction data through natural language queries.

The paper introduces dgiLIT, an AI-driven method to prioritize and curate drug-gene interactions from biomedical literature for inclusion in DGIdb, achieving high validation rates.

The paper introduces CIViC MCP, a system that uses large language models to interface with the CIViC knowledgebase, enabling natural language queries for cancer variant interpretations.

This study identifies high-quality neoantigens in follicular lymphoma patients using WES and RNA-Seq, and initiates a clinical trial of personalized neoantigen vaccines combined with PD-1 blockade.

The paper predicts MHC anchor positions for 328 HLA alleles and shows that incorporating these improves neoantigen identification by correcting misclassified candidates in tumor samples.

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.

DGIdb 4.0 aggregates drug-gene interaction and druggability claims from 41 sources, adding crowd-sourced Drug Target Commons, Wikidata-based drug normalization, search-relative query scores, and NDEx export.

CIViCpy is a Python SDK for accessing and analyzing the CIViC knowledgebase, enabling efficient variant annotation in cancer genomics.

pVACtools provides a suite of tools for neoantigen prediction, prioritization, and vaccine design in cancer immunotherapy.

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

DGIdb 3.0 updates and expands the drug-gene interaction database by adding new sources, improving the codebase, and enhancing API features.