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Ravi KalhanView profile

Professor

• Respiratory Epidemiology
• Chronic Obstructive Pulmonary Disease (COPD)
• Asthma
• +5 more

Ravi Kalhan is a Professor of Medicine (Pulmonary and Critical Care) and Preventive Medicine (Epidemiology) at Northwestern University Feinberg School of Medicine . He serves as Associate Dean for Faculty Affairs and Co-Director of the Center for Education and Career Development in the NUCATS Institute. His research bridges respiratory epidemiology , clinical trials , and precision medicine for chronic lung diseases. Brown University (BA, 1996) Case Western Reserve University (MD, 2000) Northwestern University (MS, 2006) Dr. Kalhan’s research focuses on lifecourse transitions from respiratory health to chronic lung disease , emphasizing primary prevention over secondary intervention. He leads National Institutes of Health-funded longitudinal studies like the American Lung Association Lung Health Cohort Study and CARDIA Lung Study , investigating proteomic and genetic biomarkers for early disease detection. His recent work explores proteomic risk scores for respiratory susceptibility , environmental determinants of lung health (e.g., wildfire smoke), and genetic architecture of early-onset COPD . Collaborations span artificial intelligence in medicine and multi-omics integration for COPD exacerbation prediction. Solovy Award for Advancement in COPD (2019) Excellence in Teaching Award (2009, 2008) As a clinical leader, Dr. Kalhan directs the Northwestern Asthma and COPD Program , integrating pulmonologists, allergists, and thoracic surgeons for advanced therapies like lung volume reduction and targeted biologics . He chairs the Illinois COPD Coalition and serves on editorial boards of journals including American Journal of Respiratory and Critical Care Medicine .

Manuela PettiView profile

Assistant Professor

• Precision Medicine
• Biological Network Inference
• Network Neuroscience
• +3 more

Manuela Petti is an Assistant Professor of Biomedical Engineering at Sapienza University of Rome's Department of Computer, Control, and Management Engineering since 2019. She is affiliated with the Bioengineering and Bioinformatics Lab and the National Bioengineering Group (GNB), and serves as Associate Editor for Frontiers in Medical Engineering (Computational Medicine section). Her academic background includes a cum laude M.Sc. in Biomedical Engineering from Sapienza University of Rome (2011) and a Ph.D. from the University of Bologna (2016), with a visiting stint at Indiana University Bloomington (2015). M.Sc. Biomedical Engineering, Sapienza University of Rome (2011, cum laude) Ph.D. Biomedical Engineering, University of Bologna (2016) Dr. Petti's research pioneers network-based computational approaches for precision medicine, integrating molecular data analysis, biological network inference, and brain connectivity estimation. Her work bridges bioinformatics and biomedical engineering to develop advanced methods for cancer research (focusing on sexual dimorphism and ethnic disparities), network neuroscience, and bioelectrical signal processing, with significant applications in oncology and neurology. Analysis of her 56 Scopus-indexed publications (1282 citations, H-index 13) reveals dominant trends in network medicine for cancer diagnostics, including immunological network signatures for immunotherapy response, miRNA regulation networks for sex disparities, and multi-omics integration for biomarker discovery. Her recent work emphasizes radiomics, drug repurposing, and patient stratification through network-based approaches. As an educator, she teaches "Bioinformatics and Network Medicine" and "Digital Epidemiology and Precision Medicine" in Sapienza's Data Science MSc program, having supervised over 10 Master's theses in Data Science, Computer Science, and Bioinformatics. She actively contributes to the Bioengineering and Bioinformatics Lab at Sapienza University of Rome, where her team develops computational frameworks for translating network-based discoveries into clinical applications, particularly in cancer therapeutics and neurological disorders.

Bernie DevlinView profile

Professor

• Statistical Genetics
• Neurodevelopmental Disorders
• Psychiatric Disorders
• +4 more

Bernie Devlin is a Professor in the Department of Psychiatry at the University of Pittsburgh School of Medicine. His research program centers on statistical genetics with applications to neurodevelopmental and psychiatric disorders. The Devlin Lab pursues three interconnected goals: developing novel statistical methods for genetic analysis, discovering disease-associated genetic variants, and translating these findings to elucidate neurobiological mechanisms. Primary research domains include: Statistical genetics methodology for large-scale genomic studies Genetic architecture of autism spectrum disorder and schizophrenia Functional genomics and proteomics in neural systems Integration of multi-omics data for neurodevelopmental disorders His work has been supported by multiple grants from the Simons Foundation Autism Research Initiative (SFARI), including: Transcriptomic/proteomic analysis of autism-associated genes (2022) Statistical methods development for the SSC-ASC Whole-Genome Sequencing Consortium (2018) Interactome screening for damaging de novo mutations in autism (2018) Recent publications demonstrate methodological innovations in gene-based testing and applications of genome sequencing in clinical diagnostics, with consistent focus on autism genetics.

Martin Jinye ZhangView profile

Assistant Professor

• Genetics
• Machine Learning
• Functional Genomics
• +3 more

Martin Jinye Zhang is an Assistant Professor in Computational Biology at Carnegie Mellon University's School of Computer Science. His research bridges genetics and machine learning, focusing on integrative analysis of genetics and functional genomics data to uncover disease mechanisms. Ph.D. in Electrical Engineering (2019, Stanford University) B.Eng. in Electrical Engineering (2014, Tsinghua University) Key research areas include: Developing scDRS for single-cell disease relevance scoring Using graph foundation models to integrate genetic variants and regulatory elements Advancing empirical Bayes methods and causal inference in genomics His 15 most recent articles span topics from single-cell RNA-seq to genetic architecture , with a focus on autoimmune disorders , brain diseases , and metabolic traits , often leveraging biobank-scale data . Notably, his work demonstrates how machine learning can enhance GWAS interpretation and drug target identification .

• Single-cell Biology
• Bioinformatics
• Machine Learning
• +7 more

Christoph Bock is a Principal Investigator at CeMM Research Center for Molecular Medicine and Professor/Head of the Institute of Artificial Intelligence at Medical University of Vienna. He leads the Biomedical Sequencing Facility and contributes to the Human Cell Atlas and ELLIS initiatives. Key research areas: Single-cell biology , Epigenetics , CRISPR screening , Machine learning , Cell therapy , Multi-omics Major contributions: CellWhisperer (natural language single-cell analysis), JAK-STAT signaling in immune homeostasis, LIQUORICE for liquid biopsies, and RnBeads software Scientific Awards include: ERC Starting Grant (2016) ERC Consolidator Grant (2021) Overton Prize (2017) Erwin Schrödinger Prize (2022) Clarivate Highly Cited Researcher (2019-2024) His lab develops bioinformatics tools (e.g., RnBeads, LIQUORICE) and applies systems immunology to understand disease mechanisms. Startup leadership : Co-founder of Myllia Biotechnology. Collaborations include clinical translations and tech development with the Biomedical Sequencing Facility.

• Epigenetics
• Pancreatic Cancer
• Chromatin Regulation
• +6 more

Dr. Gwen Lomberk is a Professor in the Department of Surgery at the Medical College of Wisconsin, where she serves as Chief of the Division of Research and Director of Basic Science Research. She is also a member of both the Cancer Center and the Mellowes Center for Genomic Sciences and Precision Medicine, highlighting her interdisciplinary research profile. Dr. Lomberk's research focuses on the epigenetic mechanisms underlying pancreatic cancer development and progression. Her work has been instrumental in establishing the importance of histone modifications, chromatin regulation, and epigenetic readers/writers in cancer biology. She is particularly known for developing the Triple-Code Model, which integrates genetic, epigenetic, and nuclear structural information to understand pancreatic carcinogenesis. Her research has revealed critical insights into how epigenetic regulators like EHMT2, NUPR1, and various histone methyltransferases influence pancreatic cancer development, inflammation, and response to therapy. Recent work has expanded into computational approaches for understanding genomic variants in epigenetic regulators and developing targeted therapies for KRAS-mutant pancreatic cancer. Dr. Lomberk has published 178 scientific articles in high-impact journals, with recent work appearing in Epigenomics, Journal of Gastrointestinal Oncology, and Frontiers in Genetics. Her research demonstrates a clear trajectory from basic epigenetic mechanisms to translational applications in pancreatic cancer. As a leader in her field, Dr. Lomberk has secured significant research funding and collaborates extensively with researchers across multiple disciplines. Her work bridges basic science, computational biology, and clinical applications, positioning her at the forefront of precision medicine approaches to pancreatic cancer.

• Morphogenesis and differentiation of the gastrointestinal mucosa
• Bioinformatics interpretation of molecular profiles
• Transcriptome analysis
• +7 more

Jørgen Olsen is a Professor at the Department of Cellular and Molecular Medicine, University of Copenhagen, specializing in the Morphogenesis and Differentiation Program within the Faculty of Health and Medical Sciences. His research focuses on gastrointestinal mucosa development and inflammatory bowel diseases, with significant contributions to translational medicine including the discovery of rectal insulin as a potential treatment for colitis. Dr. Olsen received his Medical Doctor degree from the University of Copenhagen in 1992 and his Doctor Medicinae (DMSc) from the Faculty of Health Sciences at the same institution in 1998. His academic journey progressed from research assistant to professor, with appointments at the Department of Medical Biochemistry & Genetics and later the Department of Cellular & Molecular Medicine. His primary research interests center on morphogenesis and differentiation of the gastrointestinal mucosa, with special focus on bioinformatics interpretation of alterations in molecular profiles (transcriptome, metabolome) during mucosal inflammation. His laboratory uses mouse models for intestinal inflammation which are translated into applied research for inflammatory bowel diseases. A significant recent discovery involves the use of rectal insulin as treatment for inflammatory bowel disease, where his team demonstrated that colitis in mice can be treated using rectal instillation of insulin. Analysis of his recent publications reveals a strong focus on medical education during the pandemic era, particularly regarding student perspectives on death, body donation, and professional identity formation. His earlier work shows consistent expertise in gastrointestinal biology, molecular mechanisms of inflammation, and bioinformatics approaches to understanding mucosal disorders. The research spans basic science through clinical translation, with increasing international collaboration in recent years. Dr. Olsen has served as supervisor for 7 PhD students, 12 masters students, and 2 scholar stipend students, and has participated in evaluation committees for numerous academic positions and theses. His teaching innovations include "The Modern Anatomical Theater," "Q+A livestreamed sessions," and digital learning resources. His research has been supported by multiple grants from the Novo Nordisk Foundation, Danish Medical Research Council, and other organizations, totaling over 4 million DKK in awarded funding. He is also CEO and co-founder of Enterotarget ApS, which holds a license agreement with the University of Copenhagen for the patent "Rectal insulin for treatment of inflammatory bowel disease" (EP3538132A1).

Jing MaView profile

Associate Professor

• Network analysis
• High-dimensional data analysis
• Microbiome data analysis
• +3 more

Jing Ma is an Associate Professor in the Biostatistics Program within the Public Health Sciences Division at Fred Hutchinson Cancer Center. She also holds affiliate appointments as an Assistant Professor in the Department of Biostatistics at the University of Washington and the Department of Statistics at Texas A&M University. As a member of the Translational Data Science Integrated Research Center at Fred Hutch, Dr. Ma leads research at the intersection of statistics, machine learning, and biological applications. Dr. Ma received her PhD in Statistics from the University of Michigan in 2015 and her BS in Mathematics from Fudan University in 2010. Her academic journey has positioned her as an expert in developing novel statistical methodologies for complex biological data. Dr. Ma's research focuses on network analysis, high-dimensional data analysis, and microbiome data analysis. Her lab specializes in statistical and computational methods for genomic data, particularly microbiome data. The Ma Lab employs various statistical learning methods including dimensionality reduction, graphical models, and high-dimensional inference to address analytical challenges in interpreting omics data. Key research areas include: Statistical methods for microbiome data analysis Statistical data integration Network-based pathway enrichment analysis High-dimensional graphical models Her current projects include "Statistical Methods for Network-based Integrative Analysis of Microbiome Data" funded by an R01 award from the National Institute of General Medical Sciences, and a pilot project on "Systems biology analysis of the immunomodulatory influence of circulating gut microbe-derived metabolites after transplantation" funded by the Translational Data Science Integrated Research Center. Dr. Ma's publications reveal a strong focus on applying statistical methodology to solve problems in cancer biology, aging research, and microbiome studies. Her work frequently involves collaborations with researchers across multiple institutions and leverages large datasets like those from the Dog Aging Project. She has made significant contributions to methods for differential network analysis, microbial community structure analysis, and integrative analysis of multi-omics data. Among her notable scientific achievements are: R01 award from the National Institute of General Medical Sciences Translational Data Science Pilot Award Microbiome Research Pilot Award Jayne Koskinas Ted Giovanis Foundation Award Dr. Ma actively mentors students and postdocs, with several former trainees now holding positions at academic institutions and tech companies. She also serves as Chair of the ASA Section on Statistics in Genomics and Genetics, where she organizes the Distinguished Student Paper Award Competition. Her work bridges statistical methodology development with impactful biological applications, particularly in understanding the role of microbiomes in human health and disease.

Graham McVickerView profile

Associate Professor

• Human Genetics
• Computational Biology
• Gene Regulation
• +6 more

Graham McVicker, PhD, is an Associate Professor at the Salk Institute for Biological Studies in the Laboratory of Genetics. Promoted to Associate Professor in April 2023, he leads the McVicker Laboratory which focuses on understanding how human genetic variation influences gene regulation, particularly non-coding variants. His research bridges computational and experimental approaches to unravel the molecular mechanisms underlying complex human diseases. McVicker's educational background includes a BSc in Computer Science from the University of British Columbia, a PhD in Genome Sciences from the University of Washington, and postdoctoral training at the University of Chicago and Stanford University. His interdisciplinary expertise combines computer science with genomics to develop innovative approaches for studying genetic variation. His research interests center on human genetic variation, gene regulation, and non-coding variants. The McVicker laboratory employs a three-pronged approach: analyzing natural genetic variation, conducting genome perturbations using CRISPR technologies, and developing computational methods including machine learning algorithms. His work has significant implications for understanding the genetic basis of complex diseases including autoimmune conditions, cancer, and neurological disorders. Analysis of McVicker's recent publications reveals a strong focus on CRISPR-based screening methods, multi-omic integration, and machine learning applications in genomics. His research consistently bridges computational and experimental approaches, with particular emphasis on understanding enhancer function, quantitative trait loci mapping, and the regulatory consequences of genetic variation across different cell types. Awards and Distinctions: National Human Genome Research Institute Genomic Innovator Award (2021) Natural Science and Engineering Research Council (NSERC) Postdoctoral Fellowship (PGS-D2, 2007) Natural Science and Engineering Research Council (NSERC) Postdoctoral Fellowship (PGS-M, 2005) McVicker's laboratory has received significant grant support including a $2.85 million NHGRI Genomic Innovator Award. His research has been funded by organizations including Padres Pedal the Cause, which awarded multiple cancer research grants to Salk researchers including McVicker in 2018. The McVicker Lab actively collaborates with other research groups at Salk and beyond to investigate the molecular basis of disease through genetic variation.

• Computational Biology
• Metagenomics
• Bioinformatics
• +5 more

Jakob Nybo Nissen serves as an Assistant Professor and Affiliate Professor with the Rasmussen Group at the Faculty of Health and Medical Sciences, University of Copenhagen. His research focuses on computational biology with emphasis on metagenomics, bioinformatics, and machine learning applications for genomic data analysis. Dr. Nissen's primary research interests include: Metagenome binning and taxonomic classification Application of deep learning to genomic sequence analysis Development of computational tools for microbial community analysis Influenza virus evolution and zoonotic transmission Multi-omics data integration for disease research His recent publications demonstrate a strong focus on improving computational methods for metagenomic analysis, with several papers published in high-impact journals including Nature Communications, Nature Biotechnology, and Nature Methods. His work bridges computer science and biology, developing novel algorithms that address critical challenges in genomic data processing. Dr. Nissen is actively involved in collaborative research efforts, particularly through the Rasmussen Group, and has contributed to major initiatives like the Critical Assessment of Metagenome Interpretation. His work has practical applications in microbial ecology, disease surveillance, and personalized medicine approaches. Based at the Center for Protein Research at the University of Copenhagen, Dr. Nissen works in a vibrant research environment that integrates computational and experimental approaches to address fundamental biological questions with biomedical relevance.

• Ancient DNA
• Paleoproteomics
• Molecular Ecology
• +3 more

Liam Thomas Lanigan serves as a Guest Researcher at the Section for Molecular Ecology and Evolution within the Globe Institute at the University of Copenhagen, leveraging ancient biomolecular techniques to investigate evolutionary and ecological dynamics across deep time periods. His research focuses on methodological rigor in ancient metagenomics and paleoproteomics, with applications spanning wildlife conservation (particularly wolf population genomics across Europe and Asia) and archaeological science (including Mesolithic bone tool analysis). Key interests include contamination control in ancient DNA studies, enzymatic protein extraction from skeletal remains, and integrating genomic data for conservation decision-making, reflecting strong interdisciplinary collaboration between genetics, archaeology, and ecology. Analysis of his 2020-2024 publications reveals consistent methodological innovation in biomolecular authentication alongside substantive contributions to canid evolutionary history and archaeological interpretation. Notable trends include protease-based biomarker recovery techniques, whole-genome sequencing for taxonomic resolution in endangered species, and multi-omics integration for reconstructing past human-environment interactions. Lanigan operates within the Section for Molecular Ecology and Evolution, a research unit applying genomic and proteomic technologies to explore ancient ecosystems, wildlife population histories, and human evolutionary trajectories through cutting-edge laboratory and computational approaches.

• Hologenomics
• Microbiome Research
• Metagenomics
• +3 more

Iñaki Odriozola Larrañaga is a Postdoctoral Researcher at the University of Copenhagen's Globe Institute, specifically within the Section for Hologenomics. His research focuses on advanced genomic techniques to study host-microbe interactions across diverse species. His research interests span hologenomics, metagenomic analysis, environmental DNA applications, and microbiome dynamics. Dr. Odriozola Larrañaga specializes in developing and implementing genomic methodologies to understand how environmental factors influence microbial communities in various organisms, from mammals to fish. His work bridges computational biology with field ecology to address fundamental questions about host-microbe coevolution. Analysis of his recent publications reveals a strong emphasis on methodological development in hologenomic research, with particular attention to DNA extraction protocols, data generation standards, and functional inference techniques. His research shows consistent collaboration with the Alberdi and Gilbert research groups, focusing on comparative studies across vertebrate species and environmental contexts. Dr. Odriozola Larrañaga maintains active research collaborations across multiple institutions, as evidenced by his co-authorship patterns and international publication record. His work has generated significant academic interest, with multiple publications receiving substantial attention on academic social networks and research platforms.