Naisha Shah, PhD

Associate Professor

Naisha Shah is an Associate Professor in the Human Genomic Medicine Department at the J. Craig Venter Institute (JCVI). Previously, Dr. Shah was the Head of Data Science and Analytics at Human Longevity Inc. (HLI). There she worked on several projects including genomic variant annotation, identification of multimodal biomarker signatures of health, and building disease risk prediction models. Over the years, she has analyzed data from several modalities including genomics, metabolomics, transcriptomics, microbiome, flow cytometry, EHR and clinical data to derive biological insights. Additionally, Dr. Shah and colleagues developed a crowdsourcing platform called OMiCC that facilitates non-computational experts to conduct statistical analysis using publicly available datasets.  

Dr. Shah received her B.Sc. in Computer Science & Molecular Biology from Simon Fraser University, Canada, and her Ph.D. in Computational Biology (Genomics of Autism Spectrum Disorder) from University College Dublin, Ireland. She conducted postdoctoral research in machine learning analysis at the NIH in Dr. Tsang's laboratory of Immune System Biology (NIAID).

Human Health and Performance 
  • Integration and analysis of multi-omics and phenotypic data to study sleep, physical, and neurocognitive performance
  • Machine learning method development and application
Annotation & Interpretation
  • Combining annotations from several sources for -omics and clinical data interpretation
Multimodal Data Platform
  • Development of a data platform that integrates publicly and privately available datasets for machine learning analysis

Publications

An unsupervised learning approach to identify novel signatures of health and disease from multimodal data.
Genome medicine. 2020-01-10; 12.1: 7.
PMID: 31924279
Profound Perturbation of the Metabolome in Obesity Is Associated with Health Risk.
Cell metabolism. 2019-02-05; 29.2: 488-500.e2.
PMID: 30318341
Identification of Misclassified ClinVar Variants via Disease Population Prevalence.
American journal of human genetics. 2018-04-05; 102.4: 609-619.
PMID: 29625023
Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults.
Proceedings of the National Academy of Sciences of the United States of America. 2018-04-03; 115.14: 3686-3691.
PMID: 29555771
The human noncoding genome defined by genetic diversity.
Nature genetics. 2018-03-01; 50.3: 333-337.
PMID: 29483654
Acetaminophen (Paracetamol) Use Modifies the Sulfation of Sex Hormones.
EBioMedicine. 2018-02-01;
PMID: 29398597
Deep sequencing of 10,000 human genomes.
Proceedings of the National Academy of Sciences of the United States of America. 2016-10-18; 113.42: 11901-11906.
PMID: 27702888
Human Health and Performance 
  • Integration and analysis of multi-omics and phenotypic data to study sleep, physical, and neurocognitive performance
  • Machine learning method development and application
Annotation & Interpretation
  • Combining annotations from several sources for -omics and clinical data interpretation
Multimodal Data Platform
  • Development of a data platform that integrates publicly and privately available datasets for machine learning analysis