جست‌وجوی استاد و پژوهشگر

• Cancer Biology
• Genome Editing
• Molecular Biology
• +۳ مورد دیگر

Wen Xue is a Professor at UMass Chan Medical School, affiliated with the RNA Therapeutics Institute within the T.H. Chan School of Medicine. She holds multiple additional roles across departments such as the Program in Molecular Medicine, Cancer Biology, and Biochemistry and Molecular Biotechnology at the Morningside Graduate School of Biomedical Sciences. Her research focuses on developing genetic models for liver and lung cancer using CRISPR/Cas9 and RNAi tools. Key areas include CRISPR-mediated genome editing for cancer gene discovery, KRAS inhibition mechanisms, and miRNA networks in lung cancer. She has secured grants from NIH, American Cancer Society, and others. Awards include the NIH Director’s New Innovator Award and Lung Cancer Research Foundation grants. Her lab actively recruits postdoctoral researchers and offers rotation projects in CRISPR technology and cancer biology. Education: B.S. and M.S. in Biochemistry from Nanjing University; Ph.D. in Biochemistry from State University of New York, Stony Brook. Research Interests: Wen Xue’s lab employs CRISPR tools to accelerate cancer gene validation and therapeutic target identification. Projects include: CRISPR-based liver cancer gene correction and oncogene deletion studies. Investigating KRAS inhibition resistance via RNAi and CRISPR in lung cancer models. Characterizing miRNA networks using TCGA data to identify therapeutic miRNA candidates. Her work bridges functional genomics with precision medicine, emphasizing in vivo and in vitro platforms. Publications: Over 100 peer-reviewed articles, including high-impact studies on CRISPR applications in gene therapy and cancer modeling. Recent work explores prime editing, base editing, and viral/non-viral delivery systems for lung diseases. Grants & Awards: NIH grants (P01HL131471, DP2HL137167), American Cancer Society (RSG-16-093), and industry partnerships like the Cystic Fibrosis Foundation. Collaborations include projects on CFTR mutation repair and AAV vector development. Labs/Teams: Xue Lab focuses on cancer genetics and gene editing, with interdisciplinary collaborations in molecular medicine and bioengineering. Ongoing projects aim to translate CRISPR-based therapies into clinical applications.

Premdass Ramdasمشاهده پروفایل

مدرس ارشد

• Cancer Cell Biology
• Quantitative Proteomics
• Genomics
• +۱۲ مورد دیگر

Dr. Premdass Ramdas is a Senior Lecturer at Monash University Malaysia, affiliated with the Jeffrey Cheah School of Medicine and Health Sciences. He joined in October 2023 with over 13 years of experience in medical biotechnology and health sciences, focusing on cancer research and bioactive natural compounds. PhD in Cancer Proteomics and Genomics, University of Malaya MSc in Medical Sciences (Cancer Genomics and Natural Products), International Medical University BSc with First-Class Honors His research focuses on the anticancer mechanisms of tocotrienols, utilizing proteomics, genomics, bioinformatics, and mouse models. He explores epigenetics, nutrigenomics, RNA/microRNA analysis, exosome biology, and AI-driven big data in cancer. His work contributes to UN Sustainable Development Goals in health and well-being. Recent publications show a strong trend in systematic reviews and mechanistic studies on vitamin E analogues in cancer, particularly colorectal and breast cancers. His research integrates molecular biology with computational approaches, including molecular docking and data mining. First-class honors for BSc Malaysian Palm Oil Board Scholarship for MSc MyBrain15 Scholarship for PhD Dr. Ramdas has secured research grants such as FRGS and actively supervises research projects. He is currently accepting PhD students and has co-supervised non-HDR reviews, indicating active mentorship. He has no known labs or teams explicitly mentioned, but collaborates extensively, particularly with Prof. Radhakrishnan A.K.

Yang Shenمشاهده پروفایل

دانشیار

• Algorithms for modeling biological molecules
• Optimization and machine learning
• Systems and control
• +۵ مورد دیگر

Yang Shen is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the Department of Computer Science and Engineering and the Institute of Biosciences and Technology. He holds a B.E. in Automation from the University of Science and Technology of China (2002) and a Ph.D. in Systems Engineering from Boston University (2008). His research focuses on algorithms for modeling biological molecules, systems, and data, with applications in protein docking, drug design, systems biology, and omics. He has received prestigious awards such as the NSF CAREER Award (2020) and MIRA Award (2017). His work integrates machine learning, optimization, and graph theory to address challenges in computational biology. Notable contributions include generative AI for protein design, interpretable models for compound-protein affinity prediction, and Bayesian active learning for protein docking. Shen has advised numerous students, including Yuning You, Mostafa Karimi, and Arghamitra Talukder, who have received awards like the Chevron Scholarship and NSF Graduate Fellowships. His lab actively collaborates on projects in drug discovery, synthetic biology, and precision medicine. Shen has led funded projects totaling over $3.5 million from NIH and NSF, exploring topics like molecular mechanisms of cancer mutations and AI-driven drug design. He serves on editorial boards for journals like the Journal of Biological Systems and has organized workshops such as the International Workshop on Biomedical Informatics with Optimization and Machine Learning (BOOM). His research bridges computational methods and biological systems, advancing both theory and practical applications in healthcare and biotechnology.

Ian Simpsonمشاهده پروفایل

استاد

• Biomedical Informatics
• Machine Learning
• Genomics
• +۳ مورد دیگر

Professor Ian Simpson is a faculty member at the University of Edinburgh's School of Informatics , holding the Chair in Biomedical Informatics and serving as Director of the UKRI Artificial Intelligence CDT in Biomedical Innovation . He teaches courses including Foundations in Biomedical Artificial Intelligence Research and supervises PhD students across multiple programs. Current Supervision: Aidan Marnane, Antoine Lain, Barry Ryan, Tariro Chatiza, Sebestyén Kamp, Maria Dolak, Hanane Issa, Juliana Rodriguez Cubillos Past Supervision: Xin He (2017), Maciej Pajak (2018), Emilia Wysocka (2019), Samuel Heron (2019), Alba Crespi (2022), Magdalena Navarro (2022), Michael Yates (2023), Jamie Campbell (2023), Nicholas Moir (2024) Research Focus: Developing computational and machine learning methodologies for biological systems analysis, particularly brain development and neurological diseases. Key interests include high-throughput brain data analysis, graph-based biological modeling, molecular phylogenetics, and brain disease molecular modeling. Publication Trends: Recent work emphasizes multi-omic data integration through graph neural networks (MOGDx), batch correction challenges in breast cancer datasets, and longitudinal stratification approaches for Parkinson's disease. His articles demonstrate applications of AI in heterogeneous disease diagnosis while preserving biological validity. Scientific Recognition: Fellow of the Alan Turing Institute (2021-) Member of RSE Young Academy of Scotland BBSRC Pool of Experts member (2021-) STEM Ambassador Laboratory Affiliations: Leads the Biomedical Informatics group within the Institute for Adaptive & Neural Computation, with affiliations to Edinburgh Neuroscience, Patrick Wild Centre, and the Simons Initiative for the Developing Brain.

Katherine A. Hoadleyمشاهده پروفایل

دانشیار

• Cancer Genomics
• Breast Cancer Research
• Gene Expression Analysis
• +۳ مورد دیگر

Katherine A. Hoadley, PhD, is an Associate Professor in the Department of Genetics at UNC School of Medicine and a member of the UNC Lineberger Comprehensive Cancer Center. Her lab specializes in cancer biology through gene expression analyses and integrative genomic approaches , with a focus on breast cancer and pan-cancer projects. Key Affiliations : UNC Lineberger Comprehensive Cancer Center Computational Medicine Program Research Themes : Multi-omics cancer characterization Immune landscape mapping Proteogenomic therapeutic targeting Clinical trial genomic analysis Scientific Recognition : Clarivate Analytics Highly Cited Researcher (2018–2024) Breast Cancer Research Foundation Award (2020) Clinical Research Forum Awards (2015) Her work contributes to precision oncology through large-scale initiatives like The Cancer Genome Atlas (TCGA) and the Genomic Data Analysis Network (GDAN) , with applications in RNA sequencing analysis and clinical trial optimization .

Marc Ladanyiمشاهده پروفایل

استاد

• Genomics
• Molecular Pathology
• Oncology
• +۳ مورد دیگر

Marc Ladanyi, MD, is a professor and molecular pathologist at Memorial Sloan Kettering Cancer Center (MSKCC), affiliated with the Human Oncology & Pathogenesis Program (HOPP) and the Department of Pathology and Laboratory Medicine. He co-directs the Genome Data Analysis Center as part of the TCGA project. His research focuses on genomics and molecular pathogenesis of sarcomas, lung adenocarcinomas, mesothelioma, and thoracic malignancies, emphasizing clinical translation of diagnostic markers and therapeutic targets. Dr. Ladanyi holds an MD from McGill University. His lab investigates mechanisms of drug resistance (e.g., EGFR inhibitors), genomic drivers (e.g., YES1 amplification, NRG1 fusions), and therapeutic vulnerabilities in cancers. Collaborations include developing novel inhibitors (e.g., proteasome inhibitors for Ewing sarcoma) and CRISPR-based tools for studying oncogenic translocations. His work bridges basic and clinical research, with a focus on precision oncology. Key projects include analyzing tumor evolution, resistance mechanisms, and genomic signatures in cancers like glioblastoma, ovarian carcinoma, and squamous lung cancer through TCGA and other large-scale initiatives. Ladanyi’s lab includes researchers and technicians such as Romel Somwar, Igor Odintsov, and Cheng-Hsiang Daniel Lu. They explore topics like MAPK pathway dependencies, bispecific antibodies for NRG1-driven tumors, and preclinical models of desmoplastic small round cell tumors.

Peter Parkمشاهده پروفایل

استاد

• Genomics
• Bioinformatics
• Computational Biology
• +۴ مورد دیگر

Peter Park is a Professor of Biomedical Informatics at Harvard Medical School and Director of its Bioinformatics and Integrative Genomics (BIG) PhD program. His research focuses on computational analysis of genomic data, particularly in cancer genomics, somatic mosaicism, and epigenetic mechanisms. He leads major projects like the Somatic Mosaicism across Human Tissues (SMaHT) network and contributes to consortia such as The Cancer Genome Atlas (TCGA) and ENCODE. His work integrates advanced computational methods with clinical applications, aiming to advance precision medicine and understand genetic drivers of disease. Education: B.A. in Applied Mathematics, Harvard University Ph.D. in Applied Mathematics, California Institute of Technology Postdoctoral studies in biostatistics, focusing on molecular biology and genetics Research Interests: His lab develops algorithms for genomic data analysis, including methods for detecting copy number variations, mutational signatures, and structural variations. Key areas include cancer genomics, somatic mosaicism in neurodegenerative diseases, and 4D nucleome structure-function relationships. He emphasizes translational research, applying computational tools to clinical problems like tumor evolution and drug response prediction. Funding & Grants: NIH grants (e.g., R01HG012573, R01CA269805) Funding from Mark Foundation, Simons Foundation, and Chan Zuckerberg Initiative Principal Investigator for multiple collaborative projects Labs & Teams: The Park Lab collaborates with interdisciplinary teams in computational biology, oncology, and neuroscience. Current projects include developing tools for single-cell DNA sequencing analysis and exploring mutational signatures in cancer and neurodegenerative disorders.

Akinyemi I. Ojesinaمشاهده پروفایل

استادیار

• Cancer Genomics
• Tumor Ecology
• HPV-Associated Cancers
• +۶ مورد دیگر

Akinyemi I. Ojesina, MD, PhD is an Assistant Professor in the Department of Obstetrics & Gynecology at the Medical College of Wisconsin (MCW). He leads the Ojesina Oncopathogenomics Laboratory, which investigates the underlying causes of cancer through the lens of tumor ecology, where microbes synergize with genomic alterations to influence tumor initiation, progression, response to therapy, and prognosis. His work bridges translational genomics, metatranscriptomics, molecular epidemiology, and precision oncology. Dr. Ojesina's research program focuses on three primary areas: genomic characterization of molecular drivers in women's cancers, biology of atypical cervical cancers (particularly HPV-independent tumors), and the role of tumor microbiome in cancer initiation and progression. His laboratory applies cutting-edge genomic analyses and laboratory experiments to interrogate population-based patient samples, cell lines, and animal models. A key methodological contribution is his co-development of PathSeq, a computational tool for identifying microbial sequences in high-throughput sequencing data. His recent publications demonstrate a strong emphasis on cervical cancer genomics, with particular attention to HPV-related mechanisms, genomic alterations, and potential therapeutic targets. Across his work, Dr. Ojesina integrates multiple approaches including genomic sequencing, transcriptomic analysis, and functional validation to uncover novel insights into cancer biology, with implications for diagnostic biomarkers and predictive models in both local and global health contexts. Dr. Ojesina leads an active research team comprising postdoctoral researchers and fellows, and has secured significant funding from the National Cancer Institute for his project 'Molecular Markers for Tumors Associated with Extrachromosomal HPV' (2024-2029). His collaborative approach is evident in numerous multi-institutional publications, including major contributions to The Cancer Genome Atlas (TCGA) and other large consortia focused on cervical cancer genomics. His work has appeared in high-impact journals including Nature, Nature Genetics, and Cancer Cell.

Daniel J Bratمشاهده پروفایل

استاد

• Neuropathology
• Glioblastoma Research
• Cancer Biology
• +۸ مورد دیگر

Daniel J Brat serves as Chair of the Department of Pathology and holds the Magerstadt Professorship at Northwestern University's Feinberg School of Medicine. He maintains dual professorial appointments in Pathology (Experimental Pathology) and Pathology (Neuropathology), leading significant research initiatives at the Robert H. Lurie Comprehensive Cancer Center. Dr. Brat's research focuses on the biological mechanisms underlying glioma progression, particularly glioblastoma (GBM). His laboratory investigates genetics, hypoxia, tumor microenvironment dynamics, and glioma stem cell behavior. Key research areas include asymmetric cell division regulation, hypoxia-induced microvascular hyperplasia, and the role of vaso-occlusion in tumor necrosis. His team employs patient-derived samples, mouse models, and Drosophila systems to elucidate fundamental cancer biology principles. Analysis of Dr. Brat's publication record reveals consistent research themes spanning molecular mechanisms of glioblastoma progression, with particular emphasis on tumor microenvironment interactions, stem cell regulation, and translational applications. His work demonstrates strong integration of basic science with clinical pathology, leveraging large datasets like TCGA while maintaining experimental validation through sophisticated in vivo models including intravital microscopy. Dr. Brat leads multiple clinical trials focused on pathology specimen collection and analysis, serving as principal investigator for the Pathology Core Facility and Breast Cancer Program tissue collection initiatives. His laboratory currently mentors a diverse team including graduate students, postdoctoral scholars, medical students, and undergraduates, with numerous former trainees now established in academic and clinical pathology positions. The Brat Lab, located in the Montgomery Ward Building at Northwestern's Chicago campus, maintains active research programs in glioblastoma progression mechanisms, asymmetric cell division in glioma stem cells, and in silico brain tumor research. The lab collaborates extensively with the Simpson Querrey Center for Neurogenetics, Simpson Querrey Institute for Epigenetics, and Center for Human Immunobiology, reflecting the interdisciplinary nature of modern cancer research.

• Leukemia
• Biostatistics
• Computational Biology
• +۶ مورد دیگر

Tim Triche, Jr., Ph.D., is an Associate Professor at the Van Andel Institute in the Department of Epigenetics . He earned his A.B. in chemistry from Cornell University , followed by an M.S. in biostatistics and a Ph.D. in statistical genetics from the University of Southern California . Before joining Van Andel in 2017, he was a postdoctoral fellow at USC's Norris Comprehensive Cancer Center focusing on cellular senescence in blood disorders. As a key member of The Cancer Genome Atlas Research Network since 2011 with over a dozen high-impact publications in Nature , Cell , and NEJM , Dr. Triche specializes in epigenetics , biostatistics , and computational biology . His lab develops innovative approaches for pediatric AML research, integrating next-generation sequencing with clinical trial design to improve patient outcomes. His work emphasizes statistical learning for patient stratification, molecular profiling of hematological cancers, and interpretable machine learning in biomedical contexts. He leads the Bioinformatics and Biostatistics Core as faculty advisor and maintains active collaborations across institutions. 2025 Nature Cancer study on developmental heterogeneity in cancer susceptibility 2024 NAR methods paper on BISCUIT multi-omics tools 2023 PLOS One validation of MAX regulation in pituitary adenomas 2022 Nature Metabolism obesity subtyping analysis Scientific contributions include: Chan Zuckerberg Initiative grant (2022) for biomedical computing NCI SPORE grant (2021) as co-recipient Key role in Pediatric AML molecular mapping (2017)

Julia Salzmanمشاهده پروفایل

دانشیار

• RNA Biology
• Computational Biology
• Genomics
• +۴ مورد دیگر

Julia Salzman is an Associate Professor at Stanford University, affiliated with the School of Medicine in the Departments of Biomedical Data Science and Biochemistry , with courtesy appointments in Statistics and Biology . She serves as a member of interdisciplinary centers including Bio-X , Stanford Cancer Institute , and Sarafan ChEM-H . Salzman's research focuses on statistical computational biology , particularly RNA splicing , cancer genomics , and microbial systems . Her lab develops algorithms like SPLASH (Statistically Primary aLignment Agnostic Sequence Homing) and SICILIAN for precise splice junction detection, enabling reference-free analysis of genomic data across human , non-model organisms , and planetary health applications (e.g., oceanic systems , plants ). Her 15 most recent publications span 2024–2016, covering topics from cell-free RNA diagnostics to circular RNA in disease . Key methodologies include DEEPEST for gene fusion detection and SQUICH for logarithmic molecular sampling. These works highlight her contributions to cancer biology and viral pandemic response . Scientific Awards include Arc Ignite Investigator (2023) NSF CAREER AWARD (2016) Alfred P. Sloan Research Fellow (2014) Baxter Faculty Scholar Award (2014) Salzman's funding history includes NIH Pathway to Independence and NSF grants . She mentors PhD and Master’s students through courses like BIOMEDIN 290 and BIODS 228 , while leading the Salzman Lab in advancing computational genomics and biological modeling .

David E. Robbinsمشاهده پروفایل

دانشیار

• Healthcare Informatics
• Biospecimen Informatics
• Data Science
• +۷ مورد دیگر

David E. Robbins is an Associate Professor and Program Director of the Master of Health Informatics and Analytics at Samford University, affiliated with the School of Public Health and the Department of Healthcare Administration and Informatics. He holds a PhD and MSEE in computer engineering from the University of Alabama at Birmingham and a BS in computer engineering from LeTourneau University. His research focuses on healthcare informatics, biospecimen informatics, data science, and machine learning, with a strong emphasis on applying software engineering and semantic web technologies to biomedical data challenges. He has developed tools for cancer genomics analysis and digital biorepositories, contributing significantly to translational research infrastructure. His recent publications demonstrate a consistent trend in leveraging big data and web-based frameworks for cancer genomics, biospecimen collaboration, and innovative applications of virtual reality in healthcare empathy training. These works reflect interdisciplinary efforts at the intersection of computer science, public health, and clinical research. Scientific Awards: Seng-jaw Soong Award for Excellence in Biostatistics and Bioinformatics Research (2013) Outstanding Service to the Society for Design and Process Science (2012) Outstanding Graduate Student Engineer of the Year (2010-2011) Dr. Robbins has served as a key contributor in developing national-level informatics tools and frameworks. While no formal advisees are listed, his role as Program Director indicates leadership in graduate education. He has not disclosed grant funding or lab affiliations in the provided text. He is actively involved in professional societies and has presented at major conferences such as Translational Science and BCB. His work supports collaborative, data-driven advancements in public health and biomedical research.

Adam D. Pfefferleمشاهده پروفایل

استادیار

• Cancer Biology
• Genomics
• Oncology
• +۷ مورد دیگر

Adam D. Pfefferle, PhD, is an Assistant Professor in the Department of Genetics at the University of North Carolina at Chapel Hill and Director of the LCCC Translational Genomics Lab (TGL). He holds a PhD from UNC Chapel Hill and a Master of Engineering in Materials Science from NC State University. His research focuses on translational genomics, assay development, and cancer biology, particularly in triple-negative breast cancer and spatial transcriptomics. Pfefferle’s work bridges laboratory research with clinical applications, leveraging mouse models and genomic technologies to identify drug targets and improve diagnostic tools. Education PhD in Genetics, UNC Chapel Hill Master of Engineering in Materials Science, NC State University Research Interests Pfefferle’s research integrates molecular biology, bioinformatics, and engineering to develop diagnostic assays and understand tumor biology. Key areas include: Assay development for clinical diagnostics (e.g., microfluidic platforms) Genomic profiling of breast cancer subtypes (e.g., claudin-low, basal-like) Mechanisms of therapeutic resistance in metastatic cancers Spatial transcriptomics and single-cell sequencing for tumor heterogeneity analysis Lab & Translational Work As TGL Director, he oversees a facility providing genomic services for academic and clinical research, including NanoString assays, spatial transcriptomics, and NGS. The lab supports projects like UNCseq and The Cancer Genome Atlas (TCGA), having processed over 12,000 assays. Collaborations & Grants Pfefferle collaborates with researchers at UNC Lineberger Comprehensive Cancer Center and has contributed to studies on drug targets and metastasis mechanisms in breast cancer. His work has informed clinical trials and diagnostic technologies (e.g., Codetta Bio’s molecular diagnostics).

Rui Henriquesمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Rui Henriques is an Assistant Professor at the Department of Electrical and Computer Engineering (DEI), Instituto Superior Técnico (IST), University of Lisbon. He is also an Associate Researcher at INESC-ID. His research focuses on data mining, machine learning, bioinformatics, and urban analytics, with applications in healthcare, transportation systems, and pattern recognition. He has advised numerous PhD and MSc students, contributing to impactful projects in areas like healthcare decision support, mobility optimization, and environmental data analysis. Education: PhD in Learning from High-Dimensional Data (2016) from IST, University of Lisbon. Research interests include developing algorithms for high-dimensional data analysis, integrating spatiotemporal data for urban planning, and applying machine learning to biomedical challenges. Notable contributions include tools like BicPAMS for biological data analysis and software frameworks for transportation analytics. His work spans over 100 publications in top journals and conferences, addressing topics from triclustering algorithms to fraud detection in online tests. He leads or contributes to projects such as ILU (city mobility optimization) and Data2Help (emergency medical services optimization). His research emphasizes interdisciplinary approaches, combining data science with real-world applications in health, transport, and sustainability.

• Bioinformatics
• Machine Learning in Healthcare
• Cancer Genomics
• +۳ مورد دیگر

Tuğba Süzek is an Associate Professor in the Department of Computer Engineering at Muğla Sıtkı Koçman University, Turkey. She holds a PhD in Bioinformatics from George Mason University (2012), an MSc from Johns Hopkins University (2000), and a BSc from Middle East Technical University (1997). Academic Background: BSc: Computer Engineering, Middle East Technical University (1993-1997) MSc: Bioinformatics, Johns Hopkins University (1998-2000) PhD: Bioinformatics, George Mason University (2003-2012) Research Interests center on bioinformatics , machine learning in healthcare , cancer genomics , transcriptomics , and molecular data analysis . She develops tools like TCGAnalyzeR for pan-cancer cohort discovery and CompCorona for coronavirus transcriptome analysis. Recent Articles span lung cancer pathogenesis, orthodontic malocclusion prediction, microbiome-host disease associations, and AI-driven diagnostic tools. Her work bridges cheminformatics and bioinformatics for drug repurposing. Scientific Awards ORISE Fellowship (2000) Patent Holder: System for Early Diagnosis of Skeleton Syndromes (2024) Advising and Grants include supervising student theses on AI in orthodontics and molecular cancer research . She leads EU-funded projects like Intraperitoneal immune modulation and TÜBİTAK grants for drug repurposing and neurodegenerative disease analysis .