جست‌وجوی استاد و پژوهشگر

• Immunology
• Genomics
• Multiomics Analysis
• +۳ مورد دیگر

Calliope Dendrou is an Associate Professor in Clinical Pathology and Inflammation at the Kennedy Institute of Rheumatology (KIR), University of Oxford, leading the Immune Disease Multiomics Laboratory. She previously held a Wellcome & Royal Society Sir Henry Dale Fellowship at the University of Oxford’s Centre for Human Genetics before joining KIR in 2023. Her research focuses on immune disease mechanisms using multiomics approaches, including genomic profiling to identify therapeutic targets across tissues and immune-mediated diseases. She co-leads large-scale projects like the Oxford-J&J Cartography Consortium and the Chan Zuckerberg Initiative’s LEGACY Network, and teaches on the MSc in Genomic Medicine program. Educational Background: BSc (Biology, Imperial College London, 2005; Forbes Memorial Medal Winner); PhD in Infection & Immunity (University of Cambridge, 2010). Postdoctoral training at the Weatherall Institute of Molecular Medicine under Prof. Lars Fugger. Research interests include immunogenetics, cytokine signaling pathways, drug repositioning, and cross-disease pathophysiology. Her work integrates single-cell and spatial transcriptomics to dissect immune-cell interactions in diseases like rheumatoid arthritis, inflammatory bowel disease, and celiac disease. Recent articles highlight her contributions to understanding vaccine adjuvant responses, Th17 cell roles in spondyloarthritis, and immune-epithelial networks in celiac disease. Collaborations emphasize multi-omic data analysis (e.g., Panpipes pipeline) and translational studies toward precision medicine. Awards: Forbes Memorial Medal (BSc), Wellcome & Royal Society Sir Henry Dale Fellowship. Leadership roles include Equality, Diversity, and Inclusion Champion and 'Single-Cell & Spatial Omics for Precision Medicine' Module Lead. Lab & Teams: Immune Disease Multiomics Lab at KIR. Active in collaborative initiatives such as the LEGACY Network, focusing on large-scale immune profiling in ancestrally diverse populations.

Rachel Carmodyمشاهده پروفایل

دانشیار

• Human Evolutionary Biology
• Nutrition
• Gut Microbiome
• +۳ مورد دیگر

Dr. Rachel Carmody is the Thomas D. Cabot Associate Professor of Human Evolutionary Biology at Harvard University, affiliated with the Faculty of Arts and Sciences. Her research focuses on energy metabolism, gut microbiome interactions, and their evolutionary implications. She leads the Nutritional & Microbial Ecology Lab, exploring how diet, genetics, and microbial communities influence human energy dynamics. Her work integrates evolutionary biology, physiology, and metagenomics to address questions about human uniqueness in digestion, maternal-offspring energy conflicts, and non-caloric dietary components. Office: Museum of Comparative Zoology 542; Email: carmody@fas.harvard.edu. Key research themes include gut microbiome-modulated obesity, placental hormone roles in pregnancy metabolism, and dietary digestibility frameworks. She also investigates evolutionary shifts in gut microbiota during human industrialization and animal domestication. Her lab employs mouse models, comparative studies, and multiomics approaches to dissect host-microbial interactions. Recent articles highlight microbiome effects on exercise-induced weight changes, antibiotic-induced obesity mechanisms, and cross-cultural dietary comparisons. While no awards are explicitly listed, her prolific publications reflect sustained impact in nutritional and evolutionary microbiology. No student advisees or grants are detailed in the provided text.

• Nanomedicine
• Drug Delivery Systems
• Cellular Uptake of Nanoparticles
• +۳ مورد دیگر

Anna Salvati is an Associate Professor specializing in nanomedicine and drug delivery systems. Her research focuses on nanoparticle-cell interactions, biomolecular corona formation, and the physicochemical properties governing nanomaterial behavior in biological environments. Key research themes include cellular uptake mechanisms, toxicity of nanomaterials, and nanoparticle targeting strategies. Recent publications highlight her work on nanoparticle stability, membrane interactions, and environmental impacts of microplastics. Her studies integrate multiomics approaches, advanced imaging, and interlaboratory validation to address challenges in nanomedicine design and safety testing. Current projects explore the role of high-density lipoproteins in nanoparticle functionality and the modulation of drug release kinetics through material engineering.

Kelly V. Rugglesمشاهده پروفایل

دانشیار

• Computational Biology
• Bioinformatics
• Proteomics
• +۱۰ مورد دیگر

Kelly V. Ruggles, PhD serves as Associate Professor in the Department of Medicine at NYU Langone Health's School of Medicine. Her academic office is located at 227 East 30th Street, Room 823, New York, NY 10016, with direct contact via phone (212-263-3642) and email. Her research focuses on computational multi-omics integration for understanding human health through high-dimensional data analysis. The Ruggles Lab develops statistical methods and visualization tools to analyze genomic, transcriptomic, translatomic, proteomic, metabolomic, and microbiome datasets, with primary applications in cancer, inflammation, and cardiovascular disease. The lab also creates novel approaches for interpreting clinical trial data, decision science modeling, and behavioral surveys. While publication details aren't listed in the provided text, her work demonstrates consistent emphasis on translating complex biological data into clinically actionable insights through computational frameworks. The lab actively develops open-source tools for multiomics data analysis as indicated by their dedicated projects section. Dr. Ruggles leads the Laboratory of Computational Multiomics, mentoring a research team focused on predictive modeling and data integration. The lab maintains an internal portal for members and alums, with documented research projects and computational tools development.

• Epidemiology of Birth Defects
• Gene-Environment Interactions in Perinatal Health
• Nutrition and Reproductive Outcomes
• +۴ مورد دیگر

Gary M. Shaw is the Rosemarie Hess Professor and Professor (Research) at Stanford University , with courtesy appointments in the Department of Epidemiology and Population Health and Department of Obstetrics & Gynecology - Maternal Fetal Medicine . He serves as Co-PI of the March of Dimes Prematurity Research Center at Stanford and PI of the California Center for Finding Causes and Preventives of Birth Defects . His research focuses on the Epidemiology of birth defects Gene-environment interactions in perinatal outcomes Nutritional factors in reproductive health . He has developed machine learning approaches for precision parenteral nutrition and predictive models for preterm birth, while investigating persistent metabolomic signatures following hypertensive pregnancy disorders. Shaw's recent work explores Climate change impacts on reproductive health Maternal-fetal immune interactions Epigenetic mechanisms in perinatal disease with applications of multiomics to neonatal intensive care units. As a member of Bio-X and the Maternal & Child Health Research Institute , he contributes to translational research networks while serving as Associate Editor for Birth Defects Research and American Journal of Medical Genetics . He supervises Med Scholar Project student Richard Liang Doctoral co-advisor for Saskia Comess and Richard Liang Master's advisor for Lenae Joe while leading the Division of Neonatology as Associate Chair for Clinical Research (2012-2025). His laboratory work integrates Metabolomic profiling Proteomic analysis Computational modeling Machine learning for biomedical data to advance neonatal care through precision medicine approaches.

Daniel Catchpooleمشاهده پروفایل

استاد

• Childhood Cancer Research
• Genomics
• Biomedical Data Science
• +۶ مورد دیگر

Professor Daniel Catchpoole serves as Deputy Head of School (Research) at the School of Computer Science, University of Technology Sydney (UTS), holding dual appointments at UTS and The Children's Hospital at Westmead. With over 20 years of research experience, he bridges computational sciences and pediatric cancer research through the Biomedical Data Science Lab in the Australian Artificial Intelligence Institute. His work integrates data analytics, artificial intelligence, and software development with molecular cancer biology to transform pediatric cancer treatment pathways. PhD in Cancer Cell Biology, University of New South Wales (1991-1995) Founding Fellow, Royal College of Pathologists Australasia (2010-present) Head, Children's Hospital at Westmead Tumour Bank (2001-present) Professor Catchpoole's research focuses on translational applications of genomics in childhood cancers, particularly acute lymphoblastic leukemia and neuroblastoma. His work combines high-throughput genomic technologies with advanced computational analysis to develop systems biology approaches for cancer patient assessment. Recent projects explore virtual reality applications for complex genomic data visualization and copper chelation therapies to enhance neuroblastoma immunotherapy. His research has received significant funding from Cancer Institute NSW, Sony Foundation, ARC, and NHMRC. His publication record spans biomedical data science, cancer genomics, and virtual reality applications in oncology. Recent work demonstrates leadership in 3D latent diffusion models for tumor segmentation, biobank economics, and innovative immunotherapies. His research consistently addresses the critical need for actionable knowledge from complex multidimensional biomedical data. Editorial Board Member, Cancers (2023) Associate Editor, Innovations in Digital Health, Diagnostics and Biomarkers (2019) Founding member and first President, Australasian Biospecimens Network Association Professor Catchpoole has supervised 17 Honours students (including 6 First Class Honours), 3 MSc students, and 12 PhD candidates across multiple institutions, with 6 current PhD students. His collaborative research bridges UTS's Faculty of Engineering and IT with The Children's Cancer Research Unit at The Children's Hospital at Westmead. Significant research funding includes Cancer Institute NSW grants, Sony Foundation VR projects, and ARC Discovery Projects focused on genomic data analysis and clinical decision support systems. His leadership extends to building frameworks for translational research, managing biobanks and clinical data linkages, and navigating governance requirements for cancer research. The Tumour Bank at Kids Research, CCRU, represents his long-standing commitment to pediatric cancer infrastructure development.

Andrew Bassettمشاهده پروفایل

پژوهشگر

• Genome Engineering
• Neurodegenerative Diseases
• Stem Cell Research
• +۵ مورد دیگر

Dr. Andrew Bassett serves as Head of the Cellular and Gene Editing Research group at the Wellcome Sanger Institute, where he develops cutting-edge genome engineering techniques using human pluripotent stem cells to investigate neurodegenerative diseases including Alzheimer's and Parkinson's. His work focuses on scaling genetic screening approaches and improving CRISPR specificity for modeling complex disease mechanisms. His academic training includes: PhD at the MRC Laboratory of Molecular Biology (MRC-LMB) with Andrew Travers on chromatin remodelling in heterochromatin formation Postdoctoral research with David Baulcombe at the University of Cambridge studying small RNA roles in chromatin modification Additional postdoctoral work with Chris Ponting at the MRC Functional Genomics Unit (MRC-FGU) in Oxford, where he pioneered CRISPR applications in Drosophila Bassett's research program centers on developing advanced genome engineering methodologies for precise modulation of gene expression networks during development and neurodegeneration. His group specializes in creating complex editing events (SNPs, paired knockouts, enhancer perturbations) within iPSC-derived models, with particular emphasis on epigenetic regulation and transcriptional control. Current projects integrate single-cell 'omics and phenotypic assays to decode genetic causes of neurodegenerative disorders through the OpenTargets consortium. Analysis of his 15 most recent publications reveals dominant trends in CRISPR technology development (35%), neurodegenerative disease modeling (30%), and single-cell functional genomics (25%). His work consistently bridges methodological innovation with disease mechanism studies, increasingly incorporating multi-omics approaches and expanding into cancer immunology and infectious disease applications since 2022. As group leader, Bassett mentors postdoctoral researchers and PhD students while securing major funding for genome engineering initiatives. His team operates within the Sanger Institute's Cellular Operations division and maintains critical partnerships with the OpenTargets consortium for therapeutic target validation. The laboratory specializes in high-throughput screening platforms using iPSC-derived neural and microglial models, with recent methodological advances including scSNV-seq and ONE-STEP tagging systems that significantly enhance precision genome editing capabilities.

Heather Murrayمشاهده پروفایل

مدرس

• Proteomics
• Phosphoproteomics
• Acute Myeloid Leukemia (AML)
• +۳ مورد دیگر

Dr. Heather Murray is a Cancer Institute NSW Early Career Research Fellow at the University of Newcastle, affiliated with the School of Biomedical Sciences and Pharmacy. She holds dual roles as a Lecturer and Postdoctoral Researcher, focusing on proteomics and phosphoproteomics to uncover therapeutic targets in acute myeloid leukemia (AML). Her work combines genomic, proteomic, and clinical approaches to address treatment resistance and precision medicine in blood cancers. Education: PhD in Medical Biochemistry (University of Newcastle, 2020), MPhil, BSc (Hons) in Biomedical Sciences, and BSc in Biological Sciences, all from the University of Newcastle. Research Interests: Proteomic characterization of leukemia subtypes, DNA repair mechanisms, and drug resistance pathways. Her lab uses phosphoproteomics to identify synergistic drug combinations targeting oncogenic pathways like FLT3 and KIT mutations. Key Collaborations: Works with clinicians like Associate Professor Anoop Enjeti and researchers at Hunter Medical Research Institute. Partnerships include institutions like the University of Southern Denmark and QIMR Berghofer. Grants & Funding: Secured over $2M in funding, including a 2022 Cancer Institute NSW Fellowship, BarbeCURE grants, and philanthropic support from organizations like the McDonald Jones Charitable Foundation. Supervision: Mentors 6 current PhD students on topics like venetoclax resistance, breast cancer biomarkers, and ex vivo AML models. Supervision history includes 6 completed Honours projects. Labs/Teams: Leads the Molecular Oncology group under Associate Professor Nikki Verrills. Member of the HMRI Precision Medicine Program and University of Newcastle ECR committees.

Arran K Turnbullمشاهده پروفایل

مدرس ارشد

• Breast Cancer
• Endocrine Therapy Resistance
• Biomarker Discovery
• +۵ مورد دیگر

Dr Arran K Turnbull is a Senior Lecturer and Group Leader at the Edinburgh Cancer Research Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, leading the Translational Oncology Research Group focused on breast and prostate cancer mechanisms and biomarker development. Education: BSc (Hons) in Molecular Biology, MSc(Res) in Immunology, Genetics and Pathway Medicine, MBChB from University of Glasgow, and PhD in Oncology from University of Edinburgh. Research Interests: Specializes in estrogen receptor positive and HER2-positive breast cancer, investigating endocrine therapy resistance through high-throughput multiomics approaches including RNA/DNA sequencing, proteomics, and spatial genomics. Key focus areas include treatment response prediction, DCIS biomarker discovery, and prostate cancer radiotherapy resistance mechanisms, translating findings into clinically applicable diagnostic tools. Scientific Awards: Breast Cancer Now Researcher of the Year award (2018) Sir Antony Driver Prize (2018) Advising and Grants: Supervises PhD students Charlene Kay (node-positive ER+ breast cancer risk prediction) and Zuzanna Gedziorowska (prostate cancer biomarker discovery). Current funding includes Breast Cancer Now, Breast Cancer Institute, The Urology Foundation, Royal Society of Edinburgh, Tenovus Scotland, CSO, NHS Lothian Charity, John Black Foundation, and NIH grants. Laboratory and Collaborations: Leads a multidisciplinary team including postdoctoral researchers, PhD students, research nurses, and bioinformaticians. Maintains active national/international partnerships with University of North Carolina, Georgetown University, University of Hong Kong, and industry collaborators including AstraZeneca and Philips.

Kushal Deyمشاهده پروفایل

استادیار

• Statistical Genetics
• Machine Learning in Genomics
• Functional Genomics
• +۵ مورد دیگر

Kushal Dey serves as an Assistant Professor in the Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSKCC), part of the Graduate School of Medical Sciences in partnership with Weill Cornell Medicine. His research integrates statistical and machine learning approaches with genomic data to understand the regulatory architecture of complex diseases. Dr. Dey's research focuses on developing computational methods that integrate human disease genetics with functional genomics data. His work spans immune-related diseases including Alzheimer's and inflammatory bowel disease, as well as heritable cancers like breast and prostate cancer. His lab develops models to prioritize variants, genes, and cell states for disease using genetic, genomic, and perturbation data, with emphasis on causal directed graphs and benchmarking pipelines informed by disease genetics. His recent publications highlight expertise in GWAS, colocalization, spatial transcriptomics, Perturb-seq, and RNA+ATAC multiome analysis. His work frequently appears in top journals like Nature Genetics, with a focus on single-cell multi-omics approaches to understand disease mechanisms at cellular resolution. Scientific Awards: Josie Robertson Investigator (2023–2028) K99/R00 Pathway to Independence Award (NIH/NHGRI) (2022–2026) NIH/NHGRI Early Stage Investigator R01 (2025-2030) NCI P30 CCSG supplement – 'LLMs in cancer research' (2023-2024) Catalog Working Group Co-chair + Disease Focus Group Lead: IGVF consortium (2023-) Dr. Dey mentors several graduate students through the Weill Cornell Graduate School (WGS), including Thahmina Ali, Pretty Garcia, Karthik Guruvayurappan, Louis Liu, Sarthak Tiwari, Berk Turhan, and Harry Zhang. His lab has received multiple grants including the AWS IMAGINE Grant Children's Health Innovation Award 2024-2025 (as Project Co-lead) and PSRP Developmental Funds Awards (2025: Co-lead). The lab actively collaborates with consortia including ENCODE, ADSP, MorPhiC, and IGVF, maintaining strong ties with Columbia University, Stanford University, and Harvard T.H.Chan School of Public Health. The Kushal Dey Lab is part of the vibrant Tri-Institutional Research campus adjacent to Rockefeller University and Weill Cornell Medical College, offering a collaborative environment focused on computational genomics and disease mechanisms.

Marylyn D Ritchieمشاهده پروفایل

استاد

• Computational Genomics
• Bioinformatics
• Pharmacogenomics
• +۸ مورد دیگر

Marylyn D Ritchie, PhD, is the Edward Rose, M.D. and Elizabeth Kirk Rose, M.D. Professor at the Perelman School of Medicine, University of Pennsylvania. She concurrently serves as Director of the Institute for Biomedical Informatics, Vice President for Research Informatics for the University of Pennsylvania Health System, Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics, and Vice Dean of Artificial Intelligence and Computing. Education: BS in Biology, University of Pittsburgh at Johnstown, 1999 MS in Applied Statistics, Vanderbilt University, 2002 PhD in Statistical Genetics, Vanderbilt University, 2004 Research Interests Dr Ritchie’s work integrates computational genomics , bioinformatics , pharmacogenomics , and systems genomics to advance precision medicine. She develops statistical and machine-learning approaches to dissect epistasis , genetic epidemiology , and evolutionary computation in large-scale biobanks, with a special focus on cardiovascular disease and Alzheimer’s disease . Her group is also pioneering translational informatics methods that incorporate social determinants of health and fairness metrics into AI-driven clinical decision support. Publication Trends In 2025 alone, Dr Ritchie co-authored more than fifteen high-impact studies spanning vision-language models for 3D CT , multi-omics Alzheimer’s risk prediction , fairness in neuroimaging AI , ancestry-specific pharmacogenomics , and cloud-based polygenic risk score platforms . The collective work highlights a shift from single-omics discovery to integrative, equitable, and clinically actionable models across diverse ancestries. Awards & Honors While specific named awards were not detailed in the text, Dr Ritchie’s endowed professorship and multi-institutional leadership roles signify sustained recognition. Grants & Advising Dr Ritchie leads large NIH, foundation, and industry-funded initiatives that support interdisciplinary teams of postdocs, graduate students, and data scientists. Her lab actively mentors trainees from UPenn’s Cell and Molecular Biology and Genomics and Computational Biology graduate groups. Laboratories & Teams She directs the Ritchie Lab (ritchielab.org), which develops open-source visualization tools such as PhenoGram , PheWAS-View , and Synthesis-View for genome-wide and phenome-wide data exploration. The lab operates within the Institute for Biomedical Informatics and collaborates closely with the Penn Medicine BioBank and multiple clinical departments to translate big-data discoveries into precision medicine workflows.

Jiangwen Sunمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Medicine
• +۶ مورد دیگر

Jiangwen Sun is an Assistant Professor in the Department of Computer Science at Old Dominion University (ODU), within the College of Science. He directs the ODU Computational Systems Medicine Lab and focuses on machine learning approaches for analyzing multi-dimensional biological data (phenome, genome, transcriptome, etc.) to advance precision medicine and its automation. His research is supported by ODU and federal agencies like NIH and NSF. Education: Ph.D., University of Connecticut M.E., Nanjing University, China B.M., Secondary Military Medical University, China Research Interests: Machine learning/data mining for medicine, health, drug discovery, and bioinformatics Multi-view bi-clustering and integrative analysis of genomic/phenotypic data Phenotype refinement and genetic association studies for complex diseases Applications in addiction medicine, cardiovascular biology, and bovine development Publications: Over 40 peer-reviewed articles in top venues like NIPS, ICML, Bioinformatics, and BMC Genomics. Recent work focuses on cryo-EM protein structure analysis, single-cell multiomics, and epigenetic modeling. Awards/Grants: NIH/NSF funding pending; previously supported by UConn's Health Informatics Lab and collaborations with University of Pennsylvania. Teaching: Courses include Machine Learning (CS722/822), Data Structures (CS361), and Deep Learning in Medicine (CS795/895). Labs/Teams: Leads the ODU Computational Systems Medicine Lab and collaborates with UConn Health Informatics Lab, Penn Medicine, and others.

• Lung Cancer Research
• Translational Medicine
• Immunobiology
• +۵ مورد دیگر

Steven M. Dubinett is a distinguished academic leader at the University of California Los Angeles (UCLA), serving as Dean of the School of Medicine and holding professorships in Medicine, Pathology and Laboratory Medicine, and Molecular and Medical Pharmacology. His work is deeply integrated with key research centers including the UCLA Jonsson Comprehensive Cancer Center and the UCLA Clinical and Translational Science Institute, where he drives innovation in lung cancer research and translational medicine. Dubinett earned his medical degree from Rutgers New Jersey Medical School in 1980, followed by an internship and residency in Internal Medicine at University Hospital and UCLA. He completed specialized fellowships in Pulmonary Medicine at Massachusetts General Hospital and in Tumor Immunology at Harvard Medical School, establishing the foundation for his career at the intersection of pulmonary medicine and cancer research. Dr. Dubinett's research program is centered on the pathogenesis of lung cancer, with particular emphasis on translational approaches to understanding immunobiology, tumor microenvironment dynamics, and mechanisms of epithelial-mesenchymal transition in human non-small cell lung cancer. His laboratory has made seminal contributions to understanding how chronic inflammation drives lung carcinogenesis and how these insights can be translated into novel therapeutic strategies. His work bridges molecular mechanisms with clinical applications, focusing on identifying targets for lung cancer interception and prevention. Analysis of Dr. Dubinett's recent publications (2023-2025) reveals a sophisticated integration of multi-omics approaches with spatial analysis to understand lung adenocarcinoma evolution. His research increasingly focuses on tumor heterogeneity, immune microenvironment characterization, and innovative immunotherapeutic approaches including dendritic cell vaccines and engineered immune cells. A significant portion of his work addresses mechanisms of immunotherapy resistance and strategies to overcome it through combination approaches. Among his numerous accolades, Dr. Dubinett received the prestigious William S. Middleton Award from the U.S. Department of Veterans Affairs in 2019, the American Thoracic Society Recognition Award for Scientific Accomplishments in 2008, and the Stanley Blatt Award for Lung Cancer Research in 1999. His work has been consistently recognized by the American Lung Association through multiple awards spanning his career. Dr. Dubinett has secured substantial research funding throughout his career, including NIH grants and institutional support that has enabled his team to pursue cutting-edge approaches to lung cancer diagnosis and treatment. His leadership extends to national advisory roles, including chairing the FDA Cellular, Tissue and Gene Therapies Advisory Committee, and representing UCLA in international research collaborations with the Chinese Academy of Sciences. He has mentored numerous physician-scientists who have gone on to establish their own research programs in oncology and pulmonary medicine. Dr. Dubinett leads a multidisciplinary research team within the UCLA Jonsson Comprehensive Cancer Center focused on translating basic discoveries into clinical applications. His laboratory employs advanced techniques in molecular biology, immunology, and systems biology to investigate the molecular mechanisms of lung cancer development and progression. Current research directions include developing non-invasive diagnostic approaches using liquid biopsies, understanding the evolution of premalignant lesions, and developing novel immunotherapeutic strategies to enhance T-cell responses against lung cancer.

• HIV Research
• Virology
• Immunology
• +۳ مورد دیگر

Yulong Wei is a Researcher in the Department of Microbial Pathogenesis at Yale School of Medicine. His work focuses on understanding viral persistence mechanisms, particularly in HIV-1 and SARS-CoV-2, using cutting-edge genomic and immunological approaches. He explores how host cellular environments influence viral integration, reservoir formation, and immune evasion. Research interests include: HIV reservoir dynamics and latency mechanisms Host-pathogen interactions in viral persistence Single-cell multiomics analysis of viral infections Antiviral drug discovery and repurposing Ribosomal adaptation and translation mechanisms in bacteria Recent work highlights his contributions to understanding how interferon signaling and chromatin structure affect HIV integration sites, as well as computational studies of griseofulvin's potential in combating SARS-CoV-2. He has also investigated evolutionary genomic signatures in microbes related to translation efficiency and environmental adaptation. His lab is part of the Yale School of Medicine's broader efforts in microbial pathogenesis and infectious disease research, with a focus on translational applications for persistent viral infections.

• Computational Biology
• Genomics
• Cancer Research
• +۵ مورد دیگر

Adam Olshen, PhD, MA is a Professor in the Department of Epidemiology & Biostatistics at the University of California, San Francisco School of Medicine. His research program bridges advanced statistical methodology with cancer genomics and clinical applications, focusing on developing computational approaches to analyze complex genomic data and understand cancer biology and patient outcomes. Dr. Olshen's research spans multiple critical areas in computational biology and cancer genomics. He is best known for developing the circular binary segmentation algorithm, a foundational method for analyzing DNA copy number data that has become standard in cancer genomics research. His work consistently applies sophisticated statistical models to address biological questions, particularly in cancer research where he examines tumor biology, treatment response mechanisms, and patient symptomatology. Recent work demonstrates expertise in multi-omic analyses, single-cell sequencing methodologies, and machine learning applications for understanding cancer stem cells, treatment resistance mechanisms, and symptom biology. His research has particular strength in translating complex genomic data into clinically relevant insights. Analysis of Dr. Olshen's recent publications reveals a strong focus on applying cutting-edge computational methods to understand cancer biology at increasingly granular levels. His work spans multiple cancer types including breast cancer, leukemia, prostate cancer, and others, with particular attention to genomic alterations, epigenetic changes, and their relationship to clinical outcomes. A notable trend is the application of advanced computational techniques to analyze single-cell and multi-omic data to uncover novel biological insights about cancer heterogeneity, progression, and symptom biology. His publications frequently appear in high-impact journals across computational biology, oncology, and clinical medicine. Dr. Olshen maintains an active collaborative research program across UCSF, working extensively with clinicians and basic scientists to apply sophisticated statistical methods to complex biological questions. His work has contributed significantly to the development of novel biomarkers, improved understanding of cancer biology, and better methods for analyzing genomic data in clinical contexts. He has been instrumental in numerous studies examining the relationship between genomic alterations and clinical outcomes, as well as the development of novel computational methods for genomic data analysis that have been widely adopted by the research community.