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Lucas SchirmerView profile

Professor

• Neuroinflammation
• Multiple Sclerosis
• Neuroimmunology
• +3 more

Lucas Schirmer is the Heisenberg Professor of Translational Neurobiology and Division Chief of Neuroimmunology at the Department of Neurology, Medical Faculty Mannheim, Heidelberg University. His research focuses on neuroinflammatory diseases, particularly multiple sclerosis (MS) and myositis, with expertise in single-cell genomics, multi-omics approaches, and translational neuroscience. The Schirmer Lab integrates experimental models and human tissue analysis to understand cellular and molecular mechanisms in neuroinflammation. Key research themes include glial cell diversity, neuro-glial interactions, and developing precision therapies. He leads major grants such as the DFG-funded NeuroFlame project (2024–2028) and the EU ERC Starting Grant DecOmPress (2021–2025). His work spans projects on iron homeostasis in neuroinflammation, liquor signatures in long-COVID, and gut microbiota-CNS interactions in MS. Collaborations include institutions like the Mannheim Institute for Innate Immunoscience (MI3) and teams across immunology and neurology. Research highlights include identifying neuron vulnerability in MS, mapping cell-type specific responses in lesions, and discovering astrocyte reactivity induced by microglia. The lab advocates translational approaches to support repair pathways and maintain cellular resilience in inflamed tissues.

Kun LiangView profile

Associate Professor

• Large-scale inference
• Statistical genetics
• High-dimensional statistics
• +3 more

Kun Liang is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, part of the Faculty of Mathematics. His research focuses on large-scale inference, statistical genetics, high-dimensional statistics, and machine learning with applications in bioinformatics and genomics. Education: Ph.D. in Statistics, Iowa State University M.S. in Statistics, Iowa State University M.S. in Automation, Tsinghua University B.E. in Automation, Tsinghua University Research Interests: His work emphasizes statistical methodologies for genomic data analysis, including false discovery rate control, ChIP-seq analysis, and integration of auxiliary information in RNA sequencing. He develops computational tools for gene ontology analysis and biomarker discovery in autoimmune diseases like psoriatic arthritis. Publications: Recent work includes advancements in directional hypothesis testing, grouped false discovery rate control, and proteomic profiling of inflammatory arthritis. His research bridges statistical theory and biomedical applications, with contributions to malaria studies and epigenetic regulation analysis. Awards: No awards explicitly listed in the provided texts. Advising & Grants: Advising details not specified here; his grants likely focus on statistical genomics and bioinformatics, though explicit mentions are absent. Labs/Teams: Engaged in interdisciplinary collaborations at the University of Waterloo, particularly in bioinformatics and statistical genetics.

• Plant Biology
• Systems Biology
• Genomics
• +4 more

Dr. Shahid M. Mukhtar is a tenured Full Professor in the Department of Genetics and Biochemistry at Clemson University's College of Science. Previously, he held faculty positions at the University of Alabama at Birmingham (UAB), advancing from Assistant to Full Professor. He earned his Ph.D. in Genetics from the Max Planck Institute (Germany) and conducted postdoctoral research at UNC Chapel Hill, focusing on Arabidopsis-pathogen interactions. His research integrates systems biology, genomics, and spatial transcriptomics to study non-canonical genetic elements like extrachromosomal DNA and microproteins. He leads efforts to establish Stereo-seq technology for high-resolution spatial profiling in plant and animal systems. Dr. Mukhtar is Director of the Multinational Arabidopsis Steering Committee (MASC) and has organized conferences for the American Society of Plant Biologists and Plant & Animal Genome meetings. He has received awards including the Odessa Woolfolk Community Service Award (2023) and President’s Award for Excellence in Teaching (2021). His lab actively seeks students interested in computational biology, multi-omics analysis, and plant-microbe interactions. Mentorship highlights include students winning NSF fellowships, Samuel Barker Awards, and other accolades.

Sorin DraghiciView profile

Professor

• Computational Biology
• Data Integration
• Disease Subtyping
• +1 more

Sorin Draghici is a Professor of Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. His research specializes in computational biology, integrating multi-omics data to discover disease subtypes and repurpose drugs. He develops bioinformatics tools like ROntoTools and CPA for pathway analysis and single-cell data interpretation. Research Focus: Machine learning applications in oncology and infectious diseases, including COVID-19 proteomics and ovarian cancer recurrence prediction. Recent innovations include AI-driven data interrogation (LmRaC) and upstream regulator prediction (PURE). Publications emphasize scalable algorithms for big data challenges.

Bree AldridgeView profile

Professor

• Tuberculosis
• Mycobacterium tuberculosis
• Drug Tolerance
• +5 more

Bree Aldridge is a Professor at Tufts University with joint appointments in the School of Medicine (Molecular Biology and Microbiology) and School of Engineering (Biomedical Engineering) . Her work bridges cell biology, computational modeling , and antibiotic discovery to combat tuberculosis and drug-resistant pathogens. Her research focuses on Characterizing single-cell determinants of mycobacterial drug tolerance Understanding growth heterogeneity control in bacteria Engineering optimized antibiotic combination therapies using quantitative measurements and systems approaches. Her recent publications highlight trends in multi-drug interaction analysis , single-cell imaging of tuberculosis , and AI-driven antibiotic discovery . She has received multiple teaching honors, including three Best Course nominations from Tufts University. Grants from the Bill & Melinda Gates Foundation and National Institutes of Health support her work on tuberculosis drug regimen optimization, myeloid cell suppression in granulomas, and stress response modeling. She actively contributes to conferences, symposia, and university committees in systems pharmacology and biosafety.

Tianfu WuView profile

Associate Professor

• Biomarker Discovery
• Autoimmune Diseases
• Proteomics
• +5 more

Dr. Tianfu Wu is an Associate Professor in the Department of Biomedical Engineering at the University of Houston's Cullen College of Engineering. He completed his PhD at the Chinese Academy of Sciences, followed by postdoctoral training at the Pasteur Institute (France), Max-Planck-Institute of Bioinorganic Chemistry (Germany), and UT Southwestern Medical Center. His research focuses on biomarker discovery for autoimmune diseases, particularly lupus nephritis, and developing point-of-care diagnostic systems using proteomics, immunoproteomics, and nanotechnology-based biosensors. His work emphasizes urinary and serum biomarkers for renal pathology, autoimmune mechanisms, and neurodegenerative diseases like Alzheimer's. He has pioneered protein array technologies and smartphone-based analytical tools for rapid disease monitoring. Key research areas include identifying novel autoantibodies, immune complexes, and molecular signatures linked to lupus activity, as well as engineering theranostic nanoparticles for rheumatoid arthritis. His lab integrates engineering innovations with clinical applications to advance precision medicine in autoimmune and renal disorders.

• Ribonucleic acid (RNA)
• Data Analysis
• Bioinformatics
• +12 more

Erik Dassi is an Associate Professor at the Department of Cellular, Computational and Integrative Biology (CIBIO) within the University of Trento. His research focuses on RNA biology, particularly investigating RNA structure-functional relationships, translational control mechanisms, and their implications in cancer, neurodegenerative diseases, and genetic disorders. He specializes in exploring how RNA modifications, G-quadruplex structures, and post-transcriptional regulatory networks drive disease processes and therapeutic resistance. Teaching activities include courses on Computational Biology, Cancer Genomics, Genomics Technologies, Programming, and Scientific Programming. His work emphasizes integrative approaches combining experimental and computational methods, such as polysomal profiling, allele-specific proteomics, and single-cell transcriptomics. Key research themes include: RNA G-quadruplex regulation of mitochondrial translation and energy metabolism ( n =3 articles in 2025), YTHDF proteins as tumor drivers in m6A pathways ( n =2 articles in 2024), and circular RNA roles in Huntington’s disease ( n =4 articles across 2021-2024). His work highlights translational reprogramming in cancer therapy resistance and RNA-based biomarker discovery.

• Plant Molecular Genetics
• Comparative Genomics
• Photosynthesis Mechanisms
• +3 more

Dr. Christian Siadjeu is a Researcher in the Department of Biology at Johannes Gutenberg University of Mainz (JGU), where he has held the position of Research Associate since July 2024. Previously, he was a Postdoctoral Researcher at Ludwig-Maximilians-Universität München (LMU) from April 2021 to June 2024. He earned his PhD (Dr.rer.nat) in Molecular Genetics from Carl von Ossietzky University of Oldenburg in 2021. His research focuses on plant molecular genetics, comparative genomics, and the molecular mechanisms underlying complex traits and their evolution, particularly in Duckweed using single-cell genomics approaches. Education: PhD in Molecular Genetics, Carl von Ossietzky University of Oldenburg, Germany (2017–2021) Research Interests: Dr. Siadjeu investigates photosynthesis mechanisms (C4 and CAM pathways), evolutionary biology, and genomic studies in medicinal plants. His work integrates transcriptomics and comparative genomics to understand plant development and stress responses. Current projects include analyzing gene expression patterns in Duckweed and exploring metabolic adaptations in plants like Sesuvium sesuvioides and Dioscorea dumetorum . Publications: His recent work highlights studies on C4-like photosynthesis in Sesuvium sesuvioides , medicinal plant genomics applications, and RNA-Seq-based insights into C2/C4 photosynthesis regulation. These studies emphasize translational research in plant biology and agricultural genomics. Advising/Grants: No specific advising roles or grants are listed in the provided text, though his research likely involves collaborative projects in genomics and plant biology.

Naomi FastView profile

Associate Professor

• Genome Evolution
• Intron Biology
• Spliceosome Structure
• +2 more

Naomi Fast is an Associate Professor in the Department of Botany at the University of British Columbia's Faculty of Science. Her research examines genome evolution in single-celled eukaryotes, particularly focusing on how genome reduction affects intron architecture and spliceosome machinery in microsporidian parasites. Using comparative genomics approaches, Fast's lab investigates the retention and modification of introns in highly compacted genomes. Her work has revealed fundamental insights into the evolutionary trajectories of spliceosomal components under extreme genomic constraints. Current research compares splicing mechanisms across diverse eukaryotic lineages including extremophilic red algae. Fast supervises graduate students studying microsporidian genomics and eukaryotic evolution. Her research has been published in leading molecular evolution journals including Molecular Biology and Evolution.

Kevin LinView profile

Assistant Professor

• Single-cell Biology
• Statistical Genetics
• Biostatistics
• +3 more

Kevin Lin is an Assistant Professor in the Department of Biostatistics at the University of Washington, joining in Fall 2023. His research focuses on developing statistical methods for analyzing single-cell data to uncover cellular mechanisms in diseases like Alzheimer’s and immune resistance. He holds a PhD from Carnegie Mellon University and completed postdoctoral training at the University of Pennsylvania. His work bridges computational methods with biological questions, emphasizing matrix factorization, network modeling, and changepoint detection. Education: PhD in Statistics & Data Science (Carnegie Mellon University, 2020) Previous Role: Postdoctoral Researcher at University of Pennsylvania (Wharton Statistics) Research interests include high-dimensional data analysis, statistical genetics, and single-cell RNA-Seq. Notable contributions include methods like Tilted-CCA for multimodal data integration and eSVD-DE for cohort-wide differential expression analysis. His work has been recognized with awards such as the Wikimedia Foundation Research Award (2023). Lin’s lab collaborates on projects involving endolysosomal dysfunction in Alzheimer’s, yeast cell division dynamics, and lineage-aware machine learning. Key achievements include over 20 publications in journals like Nature Biotechnology , PNAS , and Biometrics . He advises on grants related to single-cell technologies and serves on the EDI subcommittee for mental health initiatives at UW. Outside academia, Lin enjoys zumba and cooking.

Simon KasifView profile

Professor

• Computational Biology
• Genomics
• Bioinformatics
• +5 more

Simon Kasif is a Professor of Biomedical Engineering at Boston University. He co-founded the Center for Advanced Genomic Technology (CAGT), COMBREX (Computational Bridges to Experiments), and is a member of the I2B2 Center. His work spans computational biology, systems biology, and AI applications in healthcare. He contributed to the Human Genome Project, co-developed tools like Glimmer and Mummer, and pioneered probabilistic networks for gene function prediction. Education: M.S. & Ph.D., Computer Science, University of Maryland B.Sc., Mathematics, Tel Aviv University Research Interests: Dr. Kasif focuses on integrating computational methods with biological data to understand disease mechanisms, particularly diabetes and cancer. His work emphasizes AI ethics, network biology, and translational research. He has developed methodologies for protein interaction networks, genomic data analysis, and systems biology modeling. Key Contributions: Co-developed Glimmer, a widely used gene prediction tool Pioneered functional linkage networks for gene function prediction Advocated Bayesian networks in computational biology Investigated disease network signatures, including diabetes and Alzheimer’s Lead projects on AI safety, SARS-CoV-2 interaction mapping, and metabolic pathway analysis Grants & Collaborations: Active in interdisciplinary projects, including the COMBREX initiative for protein function annotation and I2B2’s clinical data integration. His work bridges computational methods with clinical applications, fostering open science and reproducible research practices. Labs & Teams: Directs research at CAGT and I2B2, collaborating across institutions to advance genomic technologies and systems biology approaches.

Adam D. PfefferleView profile

Assistant Professor

• Cancer Biology
• Genomics
• Oncology
• +7 more

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).

Charles M. PerouView profile

Professor

• Breast Cancer Research
• Cancer Genetics
• Bioinformatics
• +3 more

Charles M. Perou is the May Goldman Shaw Distinguished Professor of Molecular Oncology and Professor of Genetics and Pathology & Laboratory Medicine at the University of North Carolina at Chapel Hill. He leads the UNC Lineberger Breast Cancer Research Program and co-directs the Computational Medicine Program. His work focuses on breast cancer molecular subtypes, particularly the Basal-like/TNBC subtype, integrating genomics, bioinformatics, and clinical research to improve patient outcomes. He has pioneered the PAM50 assay and contributed to understanding racial disparities in breast cancer outcomes through the Carolina Breast Cancer Study. Roles: Co-Leader, UNC Lineberger Breast Cancer Research Program; Co-Director, Computational Medicine Program; Faculty Director, LCCC Bioinformatics Group. Research Interests: Breast cancer diversity, genomic classification, tumor biology, and therapeutic targeting. His lab employs RNA-sequencing, single-cell approaches, and computational models to study metastasis and immune interactions. He actively mentors students and has secured grants from NIH/NCI, Susan G. Komen, and others. He co-founded genomics-based biotech companies to translate research into clinical care. Key Awards: AACR Academy Fellow (2023), Brinker Award (2016), and multiple recognitions for mentoring and scientific contributions. His work spans lab investigations, clinical trials, and translational research, emphasizing collaboration across disciplines (e.g., bioinformatics, oncology). He leads the UNC Breast Cancer SPORE grant and has published extensively in high-impact journals.

Michael HeideView profile

Researcher

• Neuroscience
• Developmental Biology
• Evolutionary Biology
• +3 more

Dr. Michael Heide is a Junior Research Group Leader at the German Primate Center in Göttingen, funded by an ERC Starting Grant. His research focuses on the developmental and evolutionary basis of primate neocortex morphology, particularly the genetic mechanisms underlying differences in size and folding across species. He holds a PhD from the University of Heidelberg and completed postdoctoral training at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden. His work employs brain organoid models to study species-specific genes like ARHGAP11B, comparing human, chimpanzee, and other primate species. Key research themes include neural stem/progenitor cell biology, evolutionary genetics, and comparative neuroanatomy. His ERC grant supports investigations into how primate-specific genes influence neocortex development. Recent publications highlight discoveries about ARHGAP11B’s role in cortical expansion and folding, as well as single-cell genomic analyses of human brain development. His group collaborates with institutions like the MPI-CBG and the Max Planck Society. Dr. Heide’s research integrates molecular genetics, developmental biology, and evolutionary perspectives to bridge gaps in understanding primate brain evolution. His lab is affiliated with the German Primate Center’s neurosciences and molecular biology programs.

Tom Luk R MichoelView profile

Professor

• Bioinformatics
• Computational Biology
• Machine Learning
• +3 more

Tom Luk R Michoel is a Professor at the Computational Biology Unit within the Department of Informatics at the University of Bergen. His research focuses on bioinformatics, computational biology, and machine learning applied to understanding gene regulation and causal relationships in biological systems. He teaches in both the Bachelor and Master programs in Informatics, including courses like BINF301 and MNF130. Research Interests: Michoel’s work explores how genetic variation influences gene expression and disease mechanisms. He develops machine learning algorithms to infer causal gene regulatory networks from large-scale genomic data, emphasizing causal inference over mere correlations. His recent projects include analyzing plasma protein networks linked to cardiovascular disease and applying Bayesian networks to understand gene-disease associations. Publications: His most recent work (2025) focuses on causal protein networks in myocardial infarction risk and network-driven frameworks for coronary artery disease studies. He also contributes to methodological advancements like integrating graph neural networks with metabolic models. Current Activities: Michoel leads the development of tools like Findr.jl for network inference and teaches short courses on causal inference in drug discovery. His lab collaborates on projects involving single-cell analysis, multi-tissue genomics, and systems pharmacology.