GLESKit: an MCP-based system for gene-level biomedical investigation and evidence synthesis

Biomedical investigation at the gene level requires researchers to combine expression data, functional annotations, pathways, interaction networks, genetic associations, drug evidence and literature. Accessing these resources involves databases, analysis tools and scripts that require bioinformatics expertise, complicating the production of clear, reviewable reports. We present GLESKit, a gene centred system that connects these steps through the Model Context Protocol and retains data, analyses, sources and reports within a study workspace. We evaluated report production with 15 author curated GeneReportBench tasks and question answering with a gene level subset of BixBench, a reference biology benchmark for agentic systems. GLESKit completed all 15 reports with a mean committee score of 0.904, although evidence quality varied, and earned positive credit on 35 of 39 scored human BixBench questions with a mean of 0.438. In paired tests, configurations with skills scored 0.145 higher across 20 questions and 0.085 higher across five reports.

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Publication Details

Journal
bioRxiv (Cold Spring Harbor Laboratory)
Published
2026-10-30
DOI
https://doi.org/10.64898/2026.10.25.761689
Primary Topic
Biomedical Text Mining and Ontologies
Type
preprint

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preprint

GLESKit: an MCP-based system for gene-level biomedical investigation and evidence synthesis

Athos Moraes, Pedro Gabriel Ferreira
bioRxiv (Cold Spring Harbor Laboratory)
Biomedical Text Mining and Ontologies
preprint

GLESKit: an MCP-based system for gene-level biomedical investigation and evidence synthesis

Athos Moraes, Pedro Gabriel Ferreira
preprint en

Abstract

Biomedical investigation at the gene level requires researchers to combine expression data, functional annotations, pathways, interaction networks, genetic associations, drug evidence and literature. Accessing these resources involves databases, analysis tools and scripts that require bioinformatics expertise, complicating the production of clear, reviewable reports. We present GLESKit, a gene centred system that connects these steps through the Model Context Protocol and retains data, analyses, sources and reports within a study workspace. We evaluated report production with 15 author curated GeneReportBench tasks and question answering with a gene level subset of BixBench, a reference biology benchmark for agentic systems. GLESKit completed all 15 reports with a mean committee score of 0.904, although evidence quality varied, and earned positive credit on 35 of 39 scored human BixBench questions with a mean of 0.438. In paired tests, configurations with skills scored 0.145 higher across 20 questions and 0.085 higher across five reports.

bioRxiv (Cold Spring Harbor Laboratory)
Universidade do Porto (PT)
Fundação para a Ciência e a Tecnologia
Biomedical Text Mining and Ontologies
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