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Explore labs connecting computation with biological and biomedical research. Use their profiles and source links to investigate the questions, data, and methods behind the listed research interests.
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Profiles appear here because their listed research keywords match this topic. Inclusion does not establish an open position. Check the official sources and any published application instructions before reaching out.
Prepare a research introduction ↗31 research-led lab profiles
Browse labs by their listed college affiliationAaron Quinlan Research Group
University of Utah
Aaron Quinlan · Professor and Chair of Human Genetics
Which changes in a genome matter for disease, and how can researchers find them efficiently? The group combines genetics and genomic technologies with computer science and machine learning to analyze genetic variation. Researchers develop software for identifying candidate variants in rare familial disease and detecting structural changes such as deletions, duplications and inversions. Another direction examines genomic changes involved in cancer evolution, treatment resistance and relapse. The lab also maintains practical tools for manipulating and comparing large sequencing datasets, including BEDTOOLS, GEMINI and LUMPY.
Adam Leaché Research Group
University of WashingtonAdam Leaché · Professor
How do genomic datasets and analytical methods reveal species boundaries and evolutionary relationships in reptiles and amphibians? The Leaché Lab uses genome-scale SNP and sequence-capture data to infer phylogenies and test competing tree-building approaches across herpetological groups. Researchers develop and validate species delimitation and phylogeographic methods through simulations, analytical tool development, and bioinformatics pipelines to assess lineage divergence. The group integrates field-collected samples with Burke Museum specimens and genetic resources to link contemporary populations with historical material for temporal biodiversity studies. The lab applies genomic workflows and computational frameworks to clarify taxonomic trends and evaluate gene flow in species-tree estimation.
Adriana D. Briscoe Research Group
University of California, IrvineAdriana D. Briscoe · Distinguished Professor, Ecology & Evolutionary Biology
Can genetic changes in opsins and photoreceptor expression explain color vision evolution and behavioral responses in butterflies? The lab studies gene duplication and functional diversification of visual pigment genes to link molecular sequence changes to physiology and behavior. Researchers map spatial expression of photoreceptors and colored filters in butterfly eyes and test consequences for predator avoidance and mate detection using behavioral and physiological assays. The group documents co‑evolution of UV receptors and UV‑yellow wing pigments in Heliconius via comparative genomics and spectral sensitivity measurements. They also investigate digestive enzyme expression in the proboscis to characterize feeding physiology.
Independently curated · UnclaimedAlexis Battle Research Group
Johns Hopkins University
Alexis Battle · Wu and Zhang Professor
How does genetic variation in noncoding DNA alter molecular phenotypes across human tissues? The group analyzes large-scale genomic sequencing data to study how genetic differences contribute to gene regulation and disease outcomes. The lab develops computational biology tools, statistical methods, and machine learning strategies to predict the effects of variation in noncoding DNA sequences. Researchers at the Battle Lab build integrative genomic networks and apply them to problems such as autism and rare Mendelian disease variant prediction. The lab also evaluates and predicts the impact of personal genomics and rare genetic variants to improve diagnosis of rare diseases.
Independently curated · UnclaimedAmbuj K. Singh Research Group
University of California, Santa Barbara
Ambuj K. Singh · Distinguished Professor
How can learned graph representations be measured and designed to improve downstream tasks and robustness? The group develops geometry- and topology-based methods and graph-based models for drug discovery and chemical informatics. Researchers analyze social networks combining structure with attributes like sentiment and trust to model team decision dynamics and trust evolution. The group builds methods to integrate multimodal biological and imaging data to find discriminative sub-networks that predict global clinical or brain network states. They design scalable algorithms for querying and modeling dynamic composite networks to detect unusual patterns and enable efficient sampling.
Arjun Raj Research Group
University of PennsylvaniaArjun Raj · Professor of Bioengineering
How do genetically identical cells adopt different molecular fates within tissues and tumors? The Raj lab studies the biology of single cells using measurements of individual RNA molecules via fluorescence microscopy and high throughput sequencing. The group develops and utilizes experimental tools including fluorescence microscopy and sequencing to make quantitative measurements of cellular behavior. Researchers apply computational image analysis and machine learning to enable accurate spot detection and accelerate analysis of microscopy and sequencing data. The lab investigates how rare cells and multicellular organization drive outcomes like drug resistance and self-organization in cancer.
Independently curated · UnclaimedAudrey Gasch Research Group
University of Wisconsin-Madison
Audrey Gasch · Professor; Director
How do fungal cells reprogram gene expression to survive environmental stresses and maintain cellular function? The lab investigates eukaryotic stress responses using genomics and systems-biology approaches to map expression changes. Researchers combine functional genomics, computational biology, genetics, and biochemistry to identify regulatory mechanisms that confer stress resistance. Projects measure genetic variation in environmental responses and study evolution of stress defense systems across fungal genomes. Experimental and computational methods are integrated to connect gene-expression programs to physiological outcomes under diverse stress conditions.
Independently curated · UnclaimedBruce Levin Research Group
Emory University
Bruce Levin · Samuel C. Dobbs Professor of Biology
How do bacterial populations and their viruses evolve and interact under antibiotic treatment and ecological change? The Levin Lab uses mathematical and computer simulation modeling alongside in vitro and laboratory animal experiments to study population and evolutionary biology of bacteria and their viruses. The group conducts experiments and theory on antimicrobial chemotherapy and the evolution of resistance to understand treatment failure. Researchers study the pharmacodynamics and pharmacokinetics of antibiotics to link drug behavior to within-host population and evolutionary dynamics. The lab investigates population dynamics, evolution, and control of infectious disease to apply evolutionary theory to health-related problems.
Cathy Wu Research Group
University of Delaware
Cathy Wu · Professor
Cathy Wu develops computational methods and knowledge resources that help make large biological datasets interpretable. Her research spans protein informatics, biological text mining, natural-language processing, ontologies, and networks connecting genes, diseases, and drugs. Machine learning and semantic computing support the extraction and organization of information that is otherwise scattered across data and scientific literature. She directs the Protein Information Resource and works across computer science and biology. The program connects biological questions with the infrastructure and analytical methods needed to turn complex information into reusable scientific knowledge.
Collaborative Bioinformatics Research Lab
The University of Alabama at BirminghamAarna · Research lead
The goal of the Collaborative Bioinformatics Research Lab is to provide high-quality modern Bioinformatics research solutions for data analyses, workflow development and consultations.
David A. Bennett Research Group
Rush University
David A. Bennett · Robert C. Borwell Professor of Neurological Sciences
David Bennett’s research follows aging over time to understand why cognition changes and why some people develop dementia. He leads studies including the Religious Orders Study and the Rush Memory and Aging Project, connecting community-based observations with genomics, imaging, biomedical measurements, and neuropathology. The broader research program examines Alzheimer’s disease, Parkinson’s disease, stroke, sleep, decision-making, and well-being. This multidisciplinary approach links the lived experience of aging with biological mechanisms, providing resources for investigating risk, resilience, and potential strategies for preventing or treating neurological disease.
David Green Research Group
Stony Brook University
David Green · Associate Professor and AMS Graduate Program Director
Proteins recognize particular partners, and understanding that specificity can help researchers design new interactions. Green uses computational methods to investigate protein binding and interaction networks. His research includes biomolecular simulations, algorithms for designing binding interfaces, and tools for examining interactions between proteins and carbohydrates. A particular application concerns the glycobiology of HIV-1 infection. By combining applied mathematics with models from biophysical chemistry, the work connects molecular recognition with biological and medical questions about how proteins interact and how those interactions might be redesigned.
David Haussler Research Group
University of California, Santa Cruz
David Haussler · Distinguished Professor
What genomic changes underlie human neural development differences and disease-associated variants? The Computational Genomics / Haussler Lab develops statistical and algorithmic methods to analyze genome, transcriptome, and comparative genomics data to study gene regulation and molecular evolution. Researchers apply hidden Markov models, stochastic grammars, and discriminative kernel methods to detect genomic patterns and cancer biomarkers across genomes. The lab uses CRISPR, human cerebral cortex organoids, and single-cell RNA-seq to functionally characterize neurodevelopmental genes altered in human evolution. They build and maintain web tools (UCSC Genome Browser, Xena) for large-scale genomic data sharing and comparative analysis across species.
Debashish Bhattacharya Research Group
Rutgers University–New Brunswick
Debashish Bhattacharya · Distinguished Professor
How did algae acquire their photosynthetic machinery, and how do marine organisms adapt to demanding environments? Bhattacharya studies these questions through genomics, bioinformatics, and evolutionary biology. His research examines endosymbiosis and the integration of plastids into host cells. Other projects investigate salt tolerance in Picochlorum algae and use single cell genomics to examine marine organisms that are difficult to culture. Coral research addresses genome evolution, mineralization, and symbiosis, connecting molecular changes to the biology of reef organisms and their responses to environmental stress.
Galloway Lab
University of Virginia (Main campus)Laura Galloway · Commonwealth Professor of Biology
How do plants adapt when climates warm and their geographic ranges change? The lab combines field and greenhouse studies with ecological genetics, genomics and bioinformatics to investigate evolution in natural plant populations. Research examines populations remaining at the edges of ranges after glacial retreat as opportunities to understand responses to warmer conditions. Other projects connect historical range expansion with mating-system traits. The group also investigates whether differences in organelle genomes, including chloroplast DNA, contribute to reproductive isolation during the earliest stages of speciation.
Independently curated · UnclaimedJacob D. Durrant Research Group
University of Pittsburgh-Pittsburgh campusJacob D. Durrant · Associate Professor
Can computer-aided methods reliably identify small molecules that bind and modulate disease-relevant proteins in silico? The Durrant lab develops broadly applicable computer-aided drug design (CADD) techniques to identify small-molecule ligands that bind protein grooves and pockets. The group integrates computer docking, molecular dynamics simulations, and big-data/machine-learning to improve ligand-identification workflows and prioritize compounds for experimental testing. Petascale, GPU, and cloud computing are used to scale simulations and accelerate virtual screening across many targets. The lab also builds molecular-visualization tools, including ProteinVR, to explore protein structures and communicate virtual-experiment results.
Jeffrey Skolnick Research Group
Georgia Institute of TechnologyJeffrey Skolnick · Regents' Professor; Mary and Maisie Gibson Chair & GRA Eminent Scholar in Computational Systems Biology
How can protein structure prediction and systems-level models be used to predict cellular behavior and identify druggable targets? The CE lab develops computational methods to predict protein tertiary and quaternary structure and assign enzyme functions from sequence. Researchers build tools for functional genomics, metabolic pathway assignment, and drug discovery by predicting small-molecule ligands and druggable protein targets. Methods include large-scale simulation, bioinformatics pipelines, and equilibrium/dynamic modeling of membranes and assemblies. Projects extend to cancer metabolomics and simulation of virtual cells using computational systems-biology approaches.
Jill Wegrzyn Research Group
University of Connecticut
Jill Wegrzyn · Associate Professor
How can genetic information help explain an organism’s response to its environment? Wegrzyn develops computational applications for studying individual genomes and entire populations. Her laboratory creates and maintains CartograPlant, a database and application connecting plant genetics, traits, and environmental conditions. She also participates in the Evolving Meta-Ecosystems initiative, which investigates how Arctic organisms and their ecological relationships respond to climate change. The work brings computational biology and evolutionary research together, making genomic information useful for studying adaptation and biodiversity.
Jingyi Jessica Li Research Group
University of California, Los Angeles
Jingyi Jessica Li · Professor of Statistics and Data Science
How can statistical models reveal which molecular processes drive cell-to-cell transcriptome variation across populations and within single cells? The research group develops interpretable statistical methods for biomedical data, building tools that quantify regulatory mechanisms across transcriptomes. The group extracts hidden information from transcriptomics data by designing algorithms and simulators to evaluate data-generation assumptions and benchmarking analysis workflows. Researchers construct synthetic negative controls and other statistical controls to ensure rigor and control false discoveries in high-throughput analyses. The group implements and releases software (for example, scDesign3) to simulate realistic single-cell and spatial omics data for method evaluation and experimental design.
Independently curated · UnclaimedJoshua S. Weitz Research Group
University of Maryland, College Park
Joshua S. Weitz · Professor and Clark Leadership Chair in Data Analytics
How do viruses reshape population and ecosystem outcomes across marine and human-associated systems? The group develops theories and computational models of how viral infections modulate the fates of individuals, populations, and communities to understand ecosystem-scale function. Researchers build and analyze mechanistic and data-driven mathematical models to study viral ecology, bacteriophage therapy, and infectious disease dynamics across scales. Methods include computational simulation, statistical integration of field and lab datasets, and collaborations that link models to marine sampling and clinical or experimental data. The team applies quantitative approaches to predict phage–bacteria coexistence, marine viral impacts, and mechanisms driving infection variability.
Independently curated · UnclaimedMichael Brent Research Group
Washington University in St Louis
Michael Brent · Henry Edwin Sever Professor of Engineering; Professor of Computer Science & Engineering; Professor of Biomedical Engineering; Professor of Genetics
How can a person's genome sequence predict expression levels of any gene in that person? The Brent Lab maps which genes are regulated by each transcription factor in yeast and Cryptococcus using computational and experimental methods to define regulatory targets. The group is applying computational TF-target mapping methods to integrate diverse human datasets and identify regulatory networks across humans. Researchers develop quantitative models that predict gene expression level from genome sequence by combining statistics, network analysis, and machine learning. The lab participates in the Long Life Family Study to find genetic variants associated with longevity by integrating DNA sequence, DNA methylation, and gene expression data.
Independently curated · UnclaimedMichael J. Axtell Research Group
Penn State (Main campus)
Michael J. Axtell · Professor of Biology; Louis and Hedwig Sternberg Chair in Plant Biology
How do plant microRNAs and siRNAs control gene regulation and evolution of plant development? The lab discovers and characterizes plant microRNAs and siRNAs and studies their functions in plant developmental evolution. Projects combine genomics and bioinformatics to identify small RNAs and their targets across plant species. Researchers use genetic, biochemical, and molecular biology approaches to characterize microRNA biogenesis, processing, and target interactions. Current efforts include trans-species RNA mobility studies and assembly and annotation of parasitic plant genomes.
Natasa Miskov-Zivanov Research Group
University of Pittsburgh-Pittsburgh campusNatasa Miskov-Zivanov · Associate Professor
How can automated knowledge extraction produce executable mechanistic models that explain cellular behavior? The MeLoDy Lab develops computational methods and an explainable-AI tool ecosystem (BOHEME) to convert literature-derived knowledge into executable biological models. The group builds modular workflows that extract biological interactions, verify evidence across databases, reconcile heterogeneous graphs, and assemble mechanistic models for simulation and hypothesis generation. Projects include ACCORDION, CLARINET, FLUTE, and BioRECIPE for context-aware knowledge selection, reliable model recommendation, and knowledge representation. Research combines knowledge engineering, graph algorithms, mechanistic modeling, and machine learning to scale model building alongside expanding biomedical literature.
Papin Lab
University of Virginia (Main campus)Jason Papin · Professor
Can maps of cellular metabolism reveal how cells respond to disease and changes in their environment? The group reconstructs and analyzes large biochemical networks, combining high-throughput experimental data with mathematical and computational models. Researchers develop methods for incorporating measurements into network reconstructions and use experiments to test and improve model predictions. Applications include infectious disease, cancer, toxicology and metabolic engineering. The work connects the activity of many biochemical reactions with the behavior of whole cellular systems, seeking mechanisms relevant to human health.
Showing 1–24 of 31 labs