School of Medicine
Showing 101-133 of 133 Results
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Laurel Stell
Research Engineer, Biomedical Data Science
Current Role at StanfordInvestigating non-glycemic genetic effects on HbA1c using the Veterans Administration Million Veteran Program (MVP). HbA1c is a widely used test that reflects average blood sugar level for the past two to three months. It is well known that certain genetic blood conditions, such as sickle cell disease, can cause HbA1c to be a misleading indicator of blood glucose levels. I am investigating the extent to which genetic variants can have this effect even without a diagnosis of one of these conditions. I am also investigating whether these effects are impacting clinical diagnosis and treatment of diabetes, and whether such impacts are reflected in health outcomes.
The impact of these variants has potentially been overlooked because they are very rare in populations with European genetic ancestry. As with the variant for sickle cell disease, they only persist when they provide an evolutionary advantage, such as protecting against malaria infection and its symptoms. Consequently, the genetic variants that I am analyzing do not appear in most genetic biobanks frequently enough to enable my analyses. MVP, however, includes genetic data for over 100,000 Veterans with African genetic ancestry, making it an ideal resource for this research. Since these variants are common among individuals with African genetic ancestry but practically non-existent in European genetic ancestry, this research may provide insight into racial health disparities in the US, particularly in T2D prevalence and outcomes. Preprint available at https://www.medrxiv.org/content/10.1101/2024.05.26.24307947v1
Member of the department's JEDI Committee since its inception in 2021, providing assistance wherever I can. -
John S. Tamaresis, PhD, MS
Biostatistician, Biomedical Data Science
BioDr. Tamaresis joined the Stanford University School of Medicine in Summer 2012. He earned the Ph.D. in Applied Mathematics from the University of California, Davis and received the M.S. in Statistics from the California State University, East Bay. He has conducted research in computational biology as a postdoctoral scholar at the University of California, Merced and as a biostatistician at the University of California, San Francisco.
As a statistician, Dr. Tamaresis has developed and validated a highly accurate statistical biomarker classifier for gynecologic disease by applying multivariate techniques to a large genomic data set. His statistical consultations have produced data analyses for published research studies and analysis plans for novel research proposals in grant applications. As an applied mathematician, Dr. Tamaresis has created computational biology models and devised numerical methods for their solution. He devised a probabilistic model to study how the number of binding sites on a novel therapeutic molecule affected contact time with cancer cells to advise medical researchers about its design. For his doctoral dissertation, he created and analyzed the first mathematical system model for a mechanosensory network in vascular endothelial cells to investigate the initial stage of atherosclerotic disease. -
Thodsawit Tiyarattanachai
Masters Student in Biomedical Data Science, admitted Autumn 2023
Current Research and Scholarly Interestsartificial intelligence
medical imaging
ultrasound
screening and surveillance of liver cancer
cancer prediction models
cancer biomarkers -
Weiruo Zhang
Research Engineer, Biomedical Data Science
BioDr. Zhang is currently a research engineer at the Department of Biomedical Data Science, and the data manager in the Center for Cancer Systems Biology at Stanford. Dr. Zhang completed her M.S. and Ph.D. in Electrical Engineering, both from Stanford University. Her Ph.D. studies focused on developing machine learning (ML) algorithms for metabolomics data analysis using graph theory. She received Young Scientist Award from the Metabolomics Society for her algorithm on metabolic network analysis delineating the effects of genetic mutants and drug treatment on the metabolome. Her postdoctoral studies at the Department of Radiology, Stanford School of Medicine, integrated radiomic, genomic, transcriptomic, histopathologic and clinical data that identified a prognostic metabolic regulation biomarker for non-small cell lung cancer. She has developed open-source computational tools that have been appreciated by the broad research community, including the CELESTA algorithm which has been incorporated into commercial analytical platform of NanoString. Dr. Zhang's research has made significant impacts in the fields of spatial multi-omics and cancer systems biology, and she has authored and co-authored publications including Nature, Cell, Nature Methods etc.
Dr. Zhang's current research at Stanford primarily focuses on developing and implementing ML/AI methods to integrate and analyze multi-modality data, including spatial multi-omics, radiologic imaging, histopathologic images and clinical data. Her research aims at bridging the gap between underlying disease molecular/cellular biology and clinical assessment to improve diagnostics, prognostics and treatment strategies. -
Yihan Zhao
Masters Student in Biomedical Data Science, admitted Autumn 2024
Bio* Part-time Adult, Lover for Hiking, Photograph, Jazz, Surfing, Pool
* AI4Health
* How Human make better AI? How AI make better Human?
* I want to make: Anticancer Drugs, Contraceptive for Male, Artificial Womb, Weight Loss Pills
Don't create opium, create a forest, create air and water
