RFQ-Genomics Foundational Large Language Model (LLM) for VCF interpretation
This tender is from the country of UAE in Middle East region. The tender was published by AD Department of Health on 17 Mar 2025 for The primary objective of this project is to develop an AI-powered system for automated genomic variant interpretation that can be integrated into Abu Dhabi’s digital healthcare ecosystem. The Department of Health – Abu Dhabi is seeking a solution that uses a foundational Large Language Model (LLM) fine-tuned on genomic data to analyze and interpret variants from VCF files. This AI model should assist in classifying genetic variants (e.g. pathogenic, likely pathogenic, benign) and generating human-readable explanations of their clinical significance. By leveraging an LLM’s ability to analyze vast amounts of biomedical knowledge, the system will support clinicians and researchers in making faster, more informed decisions about patient genomes.. The last date to submit your bid for this tender was 08 Apr 2025. This tender is for the companies specializing in Artificial Intelligence and similar sectors.
*The deadline for this tender has passed.
Tender Organisation:
AD Department of Health
Tender Sector:
Tender Service:
Worldwide
Tender Region:
Tender Country:
Tender CPV:
72212514 : Artificial Intelligence
Tender Document Type:
Tender Notice
Tender Description:
The primary objective of this project is to develop an AI-powered system for automated genomic variant interpretation that can be integrated into Abu Dhabi’s digital healthcare ecosystem. The Department of Health – Abu Dhabi is seeking a solution that uses a foundational Large Language Model (LLM) fine-tuned on genomic data to analyze and interpret variants from VCF files. This AI model should assist in classifying genetic variants (e.g. pathogenic, likely pathogenic, benign) and generating human-readable explanations of their clinical significance. By leveraging an LLM’s ability to analyze vast amounts of biomedical knowledge, the system will support clinicians and researchers in making faster, more informed decisions about patient genomes.
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Tender Notice No:
Tender Document:
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