Arpad M. Danos

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.

CIViC-Fact evaluates AI models for verifying cancer variant interpretations using full-text evidence, showing abstracts are insufficient for accurate validation.

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.

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

The paper describes the development of CIViCmine, a text-mining tool that extracts clinically relevant cancer biomarkers from literature to aid curation into the CIViC database.

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

The paper designs single molecule molecular inversion probes targeting clinically relevant variants using the CIViC database, demonstrating high sensitivity and discovering missed variants in cancer samples.

This paper establishes a standard operating procedure for manual refinement of somatic variants in tumor sequencing data to improve consistency and reproducibility.