Stanford Cancer Institute
Showing 191-200 of 623 Results
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Charles Gawad
Associate Professor of Pediatrics (Hematology/Oncology)
BioOur lab works at the interface of biotechnology, computational biology, cellular biology, and clinical medicine to develop and apply new tools for characterizing genetic variation across single cells within a tissue with unparalleled sensitivity and accuracy. We are focused on applying these technologies to study cancer clonal evolution while patients are undergoing treatment with the aim of identifying cancer clonotypes that are associated with resistance to specific drugs so as to better understand and predict treatment response. We are also applying these methods to understand how more virulent pathogens emerge from a population of bacteria or viruses with an emphasis on developing a deeper understanding of how antibiotic resistance develops.
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Michael Gensheimer
Clinical Associate Professor, Radiation Oncology - Radiation Therapy
Current Research and Scholarly InterestsIn addition to my clinical research in head and neck and lung cancer, I work on the application of computer science and machine learning to cancer research. I develop tools for analyzing large datasets to improve outcomes and safety of cancer treatment. I developed a machine learning prognostic model using data from around 13,000 patients with metastatic cancer which performs better than traditional models and physicians [PubMed ID 33313792]. We recently completed a prospective randomized study in thousands of patients in which the model was used to help improve advance care planning conversations.
I also work on the methods underpinning observational and predictive modeling research. My open source nnet-survival software that allows use of neural networks for survival modeling has been used by researchers internationally. In collaboration with the Stanford Research Informatics Center, I examined how electronic medical record (EMR) survival outcome data compares to gold-standard data from a cancer registry [PubMed ID 35802836]. The EMR data captured less than 50% of deaths, a finding that affects many studies being published that use EMR outcomes data. -
Andrew Gentles
Associate Professor (Research) of Pathology, of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsComputational systems biology
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Olivier Gevaert
Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
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Amato J. Giaccia
Jack, Lulu and Sam Willson Professor, Professor of Radiation Oncology, Emeritus
Current Research and Scholarly InterestsDuring the last five years, we have identified several small molecules that kill VHL deficient renal cancer cells through a synthetic lethal screening approach. Another major interest of my laboratory is in identifying hypoxia-induced genes involved in invasion and metastases. We are also investigating how hypoxia regulates gene expression epigenetically.
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Iris C. Gibbs, MD, FACR, FASTRO
Professor of Radiation Oncology (Radiation Therapy) and, by courtesy, of Neurosurgery
Current Research and Scholarly InterestsDr. Gibbs is a board-certified radiation oncologist who specializes in the treatment of CNS tumors. Her research focuses on developing new radiation techniques to manage brain and spinal tumors in adults and children. Dr. Gibbs has gained worldwide acclaim for her expertise in Cyberknife robotic radiosurgery.
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Erin Gibson
Assistant Professor of Psychiatry and Behavioral Sciences (Sleep Medicine)
Current Research and Scholarly InterestsGlia make up more than half of the cells in the human brain, but we are just beginning to understand the complex and multifactorial role glia play in health and disease. Glia are decidedly dynamic in form and function. Understanding the mechanisms underlying this dynamic nature of glia is imperative to developing novel therapeutic strategies for diseases of the nervous system that involve aberrant gliogenesis, especially related to changes in myelination.
