Positions
- Henry and Emma Meyer Chair in Molecular Genetics
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Molecular and Human Genetics
Baylor College of Medicine
Houston, TX, US
- Director
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Program in Quantitative & Computational Biosciences (QCB)
Baylor College of Medicine
- Co-Director
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Computational and Integrative Biomedical Research Center (CIBR)
Baylor College of Medicine
- Member
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Dan L Duncan Comprehensive Cancer Center
Baylor College of Medicine
Houston, Texas, United States
Education
- PhD from University Of California, Santa Cruz
- 01/1990 - Santa Cruz, CA, United States
- Comp& Info Sciences, Machine Learning
Professional Interests
- Genomics
- Bioinformatics
- Epigenomics
- Clinical Genomics
- Extracellular RNA (exRNA) Communication
Professional Statement
The Bioinformatics Research Laboratory (BRL), directed by Dr. Milosavljevic, currently has six software engineers, two bioinformatics staff, three PhD students in Genetics and Genomics and Quantitative and Computational Biology Programs, two assistant professors, one associate professor and Dr. Milosavljevic. The laboratory is at the forefront on moving Genetics through the AI singularity, continuing its long record in the development of new data intensive methods and advanced computational approaches to advance understanding of biological systems and improve human health.
The laboratory developed the computational infrastructure for agentic AI integration within the Clinical Genome Resource (ClinGen) project. ClinGen Allele Registry, Evidence Repository, Linked Data Hub, Criteria Specification Registry, extensively used by clinicals worldwide and by AI agents to deliver authoritative knowledge to inform identification of genetic variants of clinical significance. The laboratory develops informatics for advanced experimental modeling of candidate pathogenic variants involved in metabolic and developmental disorders, with a focus on congenital heart disease. In collaboration with the Texas Children’s Hospital and commercial AI partners, we are engaged in the development of multimodal AI to identify pathogenic gene regulatory variants from trio whole-genome sequencing of congenital heart disease patients. The laboratory has pioneered introduction of molecular phenotyping, including metabolomics, epigenomics, and extracellular RNA (exRNA) profiling for molecular diagnosis, leveraging its continuous involvement in leading major basic science consortia, including the Roadmap Epigenome, ExRNA Consortium, the NIH Common Fund Data Ecosystem, and dGTEx.
The laboratory developed the computational infrastructure for agentic AI integration within the Clinical Genome Resource (ClinGen) project. ClinGen Allele Registry, Evidence Repository, Linked Data Hub, Criteria Specification Registry, extensively used by clinicals worldwide and by AI agents to deliver authoritative knowledge to inform identification of genetic variants of clinical significance. The laboratory develops informatics for advanced experimental modeling of candidate pathogenic variants involved in metabolic and developmental disorders, with a focus on congenital heart disease. In collaboration with the Texas Children’s Hospital and commercial AI partners, we are engaged in the development of multimodal AI to identify pathogenic gene regulatory variants from trio whole-genome sequencing of congenital heart disease patients. The laboratory has pioneered introduction of molecular phenotyping, including metabolomics, epigenomics, and extracellular RNA (exRNA) profiling for molecular diagnosis, leveraging its continuous involvement in leading major basic science consortia, including the Roadmap Epigenome, ExRNA Consortium, the NIH Common Fund Data Ecosystem, and dGTEx.
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