جست‌وجوی استاد و پژوهشگر

Mark Somozaمشاهده پروفایل

دانشیار

• Transcriptomics
• Proteomics
• Food Systems Biology
• +۳ مورد دیگر

Mark Somoza is a faculty member at the Technical University of Munich and University of Vienna , holding positions as Group Leader in Transcriptomics and Proteomics Chemistry (since 2019) and Associate Professor (since 2016). His research focuses on molecular food systems biology, transcriptomics, proteomics, and high-throughput nucleic acid synthesis technologies. PhD in Physical Chemistry, University of South Carolina–Columbia (1997–2002) Master of Science in Physics, University of Michigan–Ann Arbor (1996) Research Interests include: Decoding molecular mechanisms in food systems Developing high-resolution spatial transcriptomic/proteomic methods Photolithographic synthesis of DNA/RNA microarrays Linking food chemistry to human physiological responses Article Trends highlight his work in microarray synthesis (2015–2020), DNA data storage (2020), RNA functional studies (2017–2019), and fundamental DNA dynamics research (2004). Key fields: biochemistry, biotechnology, molecular biology, and food systems science. Grants & Projects include three major Austrian Science Fund (FWF) grants (2012–2023) for high-density RNA/DNA microarray synthesis, totaling over €1 million. Teaching spans scientific writing, biotechnology, and bioanalytical methods at University of Vienna and TUM.

Xiaojian Shaoمشاهده پروفایل

استاد مدعو

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

Dr. Xiaojian Shao is an Adjunct Professor at the Department of Biochemistry, Microbiology and Immunology of the Faculty of Medicine, University of Ottawa, and a Research Officer at the Digital Technologies Research Center, National Research Council of Canada. His research focuses on Bioinformatics, Computational Biology, and Epigenomics , with expertise in Single Cell Genomics, Machine Learning, and Cell/Gene Therapy. University: University of Ottawa School: Faculty of Medicine Department: Department of Biochemistry, Microbiology and Immunology Academic Rank: Adjunct Professor Other Role: Research Officer, NRC Digital Technologies Research Center Emails: xiaojian.shao@nrc-cnrc.gc.ca, xshao2@uOttawa.ca Dr. Shao’s research spans epigenetic mechanisms in diseases, environmental impacts on methylomes, and computational methods for genomic analysis. His work includes: Cross-sectional studies on DDT/DDE exposure and sperm epigenome DNAmethylation changes in rheumatoid arthritis and dementia Development of software tools like DMEAS for methylation analysis Investigations into growth hormone effects and folate metabolism Single-cell sequencing applications in COPD and cancer Recent publications highlight trends in environmental epigenetics, disease biomarkers, and computational methods , including cross-continental studies on toxicology and neurodegenerative diseases. Dr. Shao collaborates with institutions such as the National Research Council of Canada and contributes to journals like Environmental Health Perspectives and Communications Biology .

• Data Compression
• Medical Image Processing
• Cryptography
• +۵ مورد دیگر

Huseyin Kocak is a Professor at the University of Miami in the College of Arts and Sciences with a joint appointment in Mathematics and Computer Science. His scholarly work bridges theoretical mathematics with practical applications in data compression and security, establishing him as a significant contributor to both computational mathematics and applied computer science. Dr. Kocak's research program encompasses several interconnected domains of computational science: Advanced data compression techniques for medical imaging and color photography Integration of encryption protocols within compression algorithms Mathematical modeling of dynamical systems and chaos phenomena Development of specialized algorithms for medical diagnostics and genomic analysis Theoretical foundations of differential and difference equations with biological applications Analysis of Dr. Kocak's publication trajectory reveals a strategic evolution from fundamental mathematical research toward increasingly applied computational techniques. His early work focused on rigorous theoretical problems in dynamical systems, particularly homoclinic orbits in differential equations as evidenced by his 2021 publication on Shilnikov Saddle-Focus Homoclinic Orbits. More recently, his research has centered on solving critical healthcare challenges through innovative computational approaches, with his 2025 paper addressing FDA compliance requirements for medical image compression. His most significant contribution appears to be the development of BWIC (Burrows-Wheeler Inversion Coder), which demonstrates superior performance compared to industry standards like JPEG 2000 across multiple image types, particularly for medical applications where data integrity is paramount. Dr. Kocak's scholarly impact extends beyond pure compression research through his innovative work combining security with compression efficiency. His concurrent encryption approach, which uses the inversion frequency vector as a secure key, represents a paradigm shift in how data security is implemented within compression pipelines, offering substantial computational savings while maintaining robust security.

Jason Mezeyمشاهده پروفایل

استاد

• Computational Biology
• Genetic Research
• Biomedicine
• +۵ مورد دیگر

Dr. Jason Mezey is a Professor of Genetic Medicine and Systems and Computational Biomedicine at Weill Cornell Medical College. His research focuses on the intersection of computational biology, genetics, and pulmonary medicine, particularly examining how environmental factors like smoking affect airway epithelial biology. B.A., University of Pennsylvania (1994) Ph.D., Yale University (2000) Key research areas include genetic regulation of airway stem cells , epigenetic responses to environmental toxins , and computational methods for gene network reconstruction . Notable work involves characterizing the small airway epithelium's response to cigarette smoke and developing synthetic clinical trial data frameworks. Current NIH-funded projects include leading a Phase IA/IB Study of AAVrh.10hFXN Therapy for Friedreich's Ataxia cardiomyopathy and investigating basal cell biology in early COPD . He utilizes adeno-associated virus vectors and computational models to address gene therapy and disease mechanisms. External relationships include consulting roles with H2 Analytics, LLC and Medidata Solutions , alongside equity ownership in H2 Analytics. His work bridges academic research with biotechnology innovation.

Christian Martinمشاهده پروفایل

پژوهشگر

• Machine Learning
• Statistical Learning
• Life Science & Medicine
• +۷ مورد دیگر

Christian Martin is a Researcher at the University of Leipzig since 2019, affiliated with the ScaDS.AI Center for Scalable Data Analytics and Artificial Intelligence in Dresden/Leipzig. His work focuses on integrating Machine Learning and Biomedical Data Analysis for applications in Life Science & Medicine , particularly in Personalized Medicine and Medical Imaging . Current affiliation: ScaDS.AI Center, University of Leipzig Previous roles: Software Developer & Research Associate at Loeser/Meierhofer Medizintechnik GmbH (2008-2019), Research Associate at Biodata Mining Group, University of Bielefeld (2003-2008) His research spans Deep Learning , Radiomics , and Image Processing for medical applications, with notable projects like: GRAMMY (Integrative analysis of gastric cancer, microenvironment, and patient outcomes) MIRACLE (Machine learning for lung cancer relapse prediction) SaxoCell Omics (Systems biology for radiomics classification) He has supervised multiple master's students including: Mohammad Issa (Automotive lane detection) Wael Assy (Soft tissue sarcoma classification) Julius Ellermann (MRI image prediction) Jan Philipp Zimmer (Cervix cancer classification) Marlene Mertens (Cervical cancer analysis) Contact: christian.martin@informatik.uni-leipzig.de

• Molecular Genetics
• Aging Research
• Longevity
• +۷ مورد دیگر

Robert J. S. Reis is a Professor at the University of Arkansas for Medical Sciences, affiliated with the College of Medicine and departments of Geriatrics, Biochemistry and Molecular Biology, and Pharmacology and Toxicology. His research focuses on the molecular genetics of aging and longevity, particularly through the study of model organisms like C. elegans and mammalian systems. Ph.D. in Genetics from the University of Sussex B.A. in Biology from Harvard University Dr. Reis pioneered gene mapping in C. elegans to identify over 27 loci affecting lifespan and stress resistance. His work includes the discovery of the Pirin gene's role in post-menopausal bone loss and the involvement of Rad51 recombinase in cancer progression. Recent research explores how homologous recombination contributes to treatment resistance in myeloma, prostate, and breast cancers. His publications reveal a consistent focus on aging mechanisms, genetic pathways, and their implications for age-related diseases. While no formal awards are listed, his contributions to aging and cancer genetics are foundational. Dr. Reis operates from the VA Medical Center and collaborates on interdisciplinary projects involving bioinformatics and metabolic profiling.

Nejat YUMUŞAKمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۵ مورد دیگر

Prof. Nejat YUMUŞAK is a full Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences, where he has built his academic career since 1995. His progression includes roles as Research Assistant (1993-1998), Assistant Professor (1998-2004), Associate Professor (2005-2011), and Professor (2011-present). Education PhD in Electrical-Electronics Engineering, Sakarya University (1995-1998) Thesis: Analysis of Power System Circuit Elements Using Artificial Neural Networks MSc in Electrical-Electronics Engineering, Dumlupinar University (1994-1995) Thesis: Calculation and Measurement of Transformer Parameters at High Frequencies BSc in Electrical-Electronics Engineering, Istanbul Technical University (1989-1993) Research Focus Prof. YUMUŞAK's research integrates artificial intelligence with multidisciplinary applications. Primary domains include: Bioinformatics & Medical AI : Developing deep learning models for COVID-19 mutation prediction, leukemia classification via blood smear analysis, and gastrointestinal anomaly detection using endoscopic data. Signal Processing & Security : Advancing acoustic event detection for security systems and vulnerability testing through intelligent fuzzing techniques. Optimization & Industrial Applications : Creating metaheuristic algorithms (e.g., enhanced Wild Horse Optimizer) for energy efficiency and Industry 4.0 digitalization. Publication Trends His recent articles (2022-2025) demonstrate strong emphasis on transformer architectures for virology, ensemble methods for medical diagnostics, and CNN/LSTM networks for genomic and acoustic data analysis. Over 50% of recent works focus on healthcare applications, particularly cancer and pandemic-related prediction models. Contact & Recognition Contact via nyumusak@sakarya.edu.tr or phone (0264) 264 58 94. His work has received 793 citations (Web of Science) with an H-index of 14.

• Public Health
• Social Determinants of Health
• Immunology
• +۴ مورد دیگر

Thomas W McDade is a Professor in Medical Social Sciences (Determinants of Health) and the Weinberg College of Arts and Sciences at Northwestern University. His research focuses on biosocial determinants of health, inflammation, epigenetics, and health disparities, with a particular emphasis on global health studies in the Philippines and LGBTQ+ populations. PhD in Anthropology from Emory University (1999) His work bridges social and biological sciences, examining how early life experiences, social environments, and ecological factors shape long-term health outcomes. Key areas include inflammation , epigenetic aging , and cardiometabolic health . Recent publications highlight his leadership in community-based serosurveys (e.g., SCAN study in Chicago), minimally-invasive biomarker collection , and social determinants of immune function . His research spans public health , infectious disease , and psychosomatic medicine . Elected member of the American Academy of Arts and Sciences McDade collaborates with multiple institutes at Northwestern, including the Center for Human Immunobiology, Institute for Public Health and Medicine (IPHAM), and Simpson Querrey Institute for Epigenetics. He leads clinical trials investigating COVID-19 antibody prevalence and social contexts of health .

• Biostatistics
• Bioinformatics
• High-dimensional data analysis
• +۷ مورد دیگر

Tao Wang is an Assistant Professor Adjunct in the Department of Biostatistics at Yale School of Public Health and a tenure-track Associate Professor and Principal Investigator in the SJTU-Yale Joint Center for Biostatistics and Data Science at Shanghai Jiao Tong University. His work focuses on statistical methods for high-dimensional biomedical data, especially dimension reduction, microbiome analysis, and integrative genomic modeling. Education: PhD in Statistics, Hong Kong Baptist University, 2013 MS, East China Normal University, 2010 Research Interests: Dr. Wang develops statistical and computational techniques to tackle complex, high-dimensional data arising from modern biotechnologies. His methodological portfolio includes adaptive independence tests for microbiome community data, penalized prediction models for sequencing counts, and counting-process-based dimension reduction for censored survival outcomes. He also works on Bayesian tree-structured models linking dietary nutrients to gut microorganisms and joint estimation of Gaussian graphical models over spatial and temporal domains. These interests sit at the intersection of biostatistics, bioinformatics, machine learning, and epidemiology , with applications spanning the human microbiome, epigenomics, and large-scale cohort studies. Publications at a Glance: Across six major papers (2015-2019) in Biometrics and Biometrika , Wang has consistently advanced methodology for high-dimensional count, compositional, and censored data. Themes include adaptive testing, predictive modeling, dimension reduction, and network estimation, often motivated by collaborative projects in genomics and microbiome science. Honours: Elected Member, International Statistical Institute (2020) Collaboration & Advising: He maintains an active collaboration with Yale’s Dr. Hongyu Zhao, co-authoring all showcased publications. No formal advisees are listed in the provided material. Laboratories & Teams: He leads research activities within the SJTU-Yale Joint Center for Biostatistics and Data Science, integrating resources from both Shanghai Jiao Tong University and Yale to advance data-intensive biomedical science.

• Translational Pathology
• Histopathology
• Multiplex Immunohistochemistry
• +۷ مورد دیگر

Michelle A. Wood-Trageser, PhD is an Assistant Professor in the Division of Translational Pathology at the University of Pittsburgh School of Medicine. She serves as co-director for the CAP/CLIA certified Research Histology Services Laboratory, working alongside Dr. A. Jake Demetris to advance the field of transplant pathology through innovative histological techniques. Dr. Wood-Trageser earned her BS from Elizabethtown College in 2004 and her PhD from the University of Pittsburgh in 2009, establishing a strong foundation for her career in translational research. Her research program focuses on integrating traditional histopathological approaches with multiplex immunohistochemistry and digital image analysis to enhance understanding of disease processes within tissue. The laboratory's primary focus is on characterizing the immune microenvironment within biopsy tissues, with recent expansion into genomic capabilities for developing novel clinical tests that correlate geographical information from biopsies with molecular signatures. This integrative approach bridges the gap between classical pathology and modern molecular diagnostics. Dr. Wood-Trageser's publication record demonstrates consistent productivity across two major research domains: reproductive genetics (particularly ovarian biology and infertility) and transplant pathology. Her recent work (2020-2023) shows an increasing emphasis on digital pathology applications in transplantation, including immune synapse detection and T cell monitoring in allografts. She has developed expertise in combining genomic data with spatial tissue information to create more comprehensive diagnostic tools. Dorothy Lyet Research Endowment, Summer Research Stipend, Elizabethtown College (2002, 2003) Beta Beta Beta, Biological Honors Society, Elizabethtown College (2003-2004) Stephen L. Phillips Scientific Achievement Award, University of Pittsburgh, School of Medicine (2008) Gary Betty Scholar, University of Michigan (2009) Trainee Day Travel Award, Endocrine Society (2009) Outstanding Abstract Award, Endocrine Society (2011) Lalor Foundation Travel Fellow, Society for the Study of Reproduction (2014) Cornelia Post Channing New Investigator Award, Society for the Study of Reproduction (2014) Dr. Paul M. Rike Fellowship Award, Magee-Womens Research Institute (2014) As co-director of the Research Histology Services Laboratory, Dr. Wood-Trageser oversees a CAP/CLIA certified facility that serves as a hub for advanced histological techniques applied to both clinical and research specimens. Her mentorship approach emphasizes technical excellence in histopathology while encouraging innovation in digital and genomic pathology applications. Current research directions include developing methods to correlate spatial tissue architecture with molecular signatures, creating more precise diagnostic frameworks for transplant pathology and reproductive disorders.

Ana Rita Grossoمشاهده پروفایل

استادیار

• Computational Biomedicine
• Bioinformatics
• Cancer Genomics
• +۳ مورد دیگر

Ana Rita Grosso is an Assistant Professor at NOVA University Lisbon's Faculty of Science and Technology. She leads the Computational Multi-Omics Lab (CompMultOmics@UCIBIO), focusing on understanding genome biology and its disease implications through computational approaches. Her research integrates multi-omics data (genomics, proteomics, transcriptomics) with clinical information to study: Cancer evolution and intra-tumor heterogeneity Epigenetic regulation in disease Transcriptional readthrough and pseudogene detection Metabolic control of cellular states Immune evasion mechanisms She develops statistical methods for high-throughput sequencing, microarray, and proteomic data analysis. Recent publications (2015-2022) demonstrate strong focus on: Pan-cancer analyses of genetic/epigenetic heterogeneity Innovative model systems (zebrafish xenografts) Transcriptional regulation mechanisms Computational method development Renal and colorectal cancer biology Work appears in high-impact journals including Nature Cell Biology, Cell, and Nature Communications. She actively trains MSc/PhD students and teaches computational biology courses to address bioinformatics skill gaps in research. Her lab promotes seminars for researchers across career stages.

Andrew J Sharpمشاهده پروفایل

استاد

• Genomics
• Human Genome Research
• Structural Variation
• +۱۴ مورد دیگر

Andrew J Sharp is a Professor in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. He serves as Co-Director of the Graduate Program in Genetics and Genomic Sciences and directs two graduate courses: BSR2400 Translational Genomics and BSR4401 Genetics and Genomics Journal Club. Professor Sharp leads the Sharp lab, an integrated research environment combining experimental and bioinformatic approaches to study the human genome and disease through a 'reverse genetics' strategy. His research focuses on structural variation, epigenetics (particularly DNA methylation), gene expression, and tandem repeat DNA, with applications to understanding mental retardation syndromes, autism, schizophrenia, diabetes, epilepsy, multiple sclerosis, Alzheimer's disease, and neural tube defects. His research has led to significant discoveries, including identifying genetic syndromes accounting for approximately 2% of mental retardation cases worldwide and discovering the most common genetic risk factor for epilepsy. His work has been published in top journals including Nature Genetics and The New England Journal of Medicine . Professor Sharp's publication record shows a strong focus on genomic technologies and their application to human disease, with recent work emphasizing tandem repeat variation, phenome-wide association studies, and epigenetic mechanisms in neurodegenerative disorders. His notable awards include: 2009 Young Investigator Award for Outstanding Science from the European Society of Human Genetics 2006 Trainee Award (postdoctoral) from the American Society of Human Genetics 2002 Trainee Award (predoctoral) from the American Society of Human Genetics Professor Sharp welcomes students interested in human genetics and genomics research, offering a diverse environment for gaining exposure to innovative research areas. His lab provides opportunities for both experimental and computational research in human genome studies, with a focus on translating genomic discoveries into understanding human disease mechanisms.

Charles Kooperbergمشاهده پروفایل

استاد

• Statistical Genetics
• Biostatistics
• High-Dimensional Data Analysis
• +۱ مورد دیگر

Charles Kooperberg serves as Professor and Program Head of the Biostatistics Program within the Public Health Sciences Division at Fred Hutchinson Cancer Center, where he is also a Member of the Translational Data Science Integrated Research Center (TDS IRC). Additionally, he holds an Affiliate Professor appointment in Biostatistics at the University of Washington. His educational background includes: PhD in Statistics from University of California, Berkeley (1991) MA in Statistics from University of California, Berkeley (1988) BSc in Mathematics from Delft University of Technology (1985) Dr. Kooperberg specializes in statistical genetics and high-dimensional data analysis, developing novel biostatistical methods for genome-wide association studies (GWAS) using SNP genotyping, microarray gene expression, and proteomics data. His research focuses on extracting biological insights from complex patient datasets to understand disease mechanisms, improve risk prediction models, and advance personalized medicine applications. He leads the Kooperberg Lab which pioneers statistical techniques for genomic and proteomic data interpretation, and serves on the steering committee of the Women's Health Initiative studying chronic diseases in postmenopausal women. Recent work highlighted in Fred Hutch publications addresses inflammation biomarkers, DNA-based risk scores, and diversity in genetic research. No scientific awards were specified in the source material. Information about student advising and research grants was not provided in the available text.

José Antonio Castellanos Garzónمشاهده پروفایل

دانشیار

• Bioinformatics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

José Antonio Castellanos Garzón serves as an Associate Professor in the Department of Computer Science and Automation at the University of Salamanca, where he is affiliated with the Biomedical Research Institute of Salamanca (IBSAL) and leads the ESAL Expert System and Applications Lab (ESALAB). He earned his doctorate from the University of Valladolid in 2013 with a thesis on evolutionary frameworks for DNA microarray cluster analysis, supervised by Dr. Fernando Díaz Gómez. His academic foundation bridges computer science and biomedical applications through advanced computational methodologies. His research spans Bioinformatics, Artificial Intelligence, Machine Learning, Data Mining, Evolutionary Computation, and Biomedical Informatics, emphasizing algorithmic solutions for genomic data analysis. Previously, he contributed to the BISITE research group focused on Bioinformatics, Intelligent Informatic Systems, and Educational Technology, demonstrating cross-disciplinary expertise in computational biology and AI-driven healthcare systems.