جست‌وجوی استاد و پژوهشگر

• Erythropoiesis
• Hemoglobinopathies
• Hematopoiesis
• +۵ مورد دیگر

John Cunningham is a Professor in the Department of Pediatrics at the University of Chicago. His research program focuses on hemoglobinopathies, molecular mechanisms of hematopoiesis, leukemia stem cell development, and vertebrate transcriptional regulation. Key projects include developmental erythropoiesis, neural crest gene networks, and allogenic stem cell therapies for sickle cell disease. He maintains collaborative partnerships across developmental biology, informatics, and cancer biology. Education includes: MB BCh BAO in Medicine from University College Dublin (1982) Residency at St. Laurences Hospital, Dublin (1985) Membership of the Royal College of Physicians UK (1986) Hematology Fellowship at Mater Misericordiae Hospital, Dublin (1987) MSc in Biochemistry from Kings College London (1990) Wellcome Fellowship at Royal Free Hospital (1991) Clinical Hematology Fellowship at NIH/NHLBI (1993) Research interests span erythropoiesis, hemoglobinopathies, hematopoietic stem cell biology, neural crest development, sickle cell disease therapeutics, gene regulatory networks, and stem cell transplantation approaches. His work integrates molecular biology with clinical translation. Publication analysis reveals consistent focus on hematologic malignancies, stem cell biology, computational oncology, and pediatric translational research. Recent work emphasizes neuroblastoma biomarkers, stem cell transplantation outcomes, and molecular diagnostics. Scientific honors include: Wellcome Fellowship (1988-1991) Fogarty Fellowship (1991-1993) Pritzker Professorship (2015-2017) Bucksbaum Senior Fellowship (2017-2019) George M. Eisenberg Professorship (2017-2019) Research collaborations span developmental biology, computational genomics, and clinical hematology/oncology. Current projects focus on gene regulatory networks in neuroblastoma and novel stem cell therapies for hemoglobinopathies.

• Molecular Biology
• Genetics
• Leukemia Research
• +۴ مورد دیگر

Lucio Castilla is a Professor at UMass Chan Medical School, holding multiple positions across the T.H. Chan School of Medicine and Morningside Graduate School of Biomedical Sciences. His primary appointment is in the Department of Molecular, Cell and Cancer Biology, with additional affiliations in the Program in Molecular Medicine and several graduate programs including Cancer Biology and the MD/PhD Program. Dr. Castilla received his B.S. from the University of Buenos Aires in 1988 and his Ph.D. in Genetics from the University of Michigan in 1995. He completed his postdoctoral training at the National Institutes of Health from 1995 to 2000 before joining UMass Medical School as an Assistant Professor in 2000. Dr. Castilla's research focuses on understanding the molecular mechanisms of leukemia development, particularly the role of core-binding factor (CBF) in hematopoietic stem cells and leukemogenesis. His laboratory uses conditional knock-in and knock-out mouse models to study how genetic alterations in CBF subunits (RUNX1 or CBFβ) lead to acute myeloid leukemia. The lab also investigates collaborating genes that work with Cbfb-MYH11 in leukemogenesis and characterizes leukemia-initiating cells to identify potential therapeutic targets. Analysis of Dr. Castilla's recent publications reveals a strong focus on inv(16) acute myeloid leukemia, with particular attention to the CBFβ-SMMHC fusion protein. His research spans molecular mechanisms, mouse models, and translational approaches to target leukemia-initiating cells. Recent work has expanded into familial platelet disorders associated with RUNX1 mutations and the inflammatory microenvironment's role in hematologic malignancies. Dr. Castilla has received numerous prestigious awards throughout his career: Special Fellow Award from the Leukemia and Lymphoma Society (1999) AACR-Sidney Kimmel Symposium for Cancer Research Scholar Award (2002) Leukemia and Lymphoma Society Scholar (2007-2012) UMASS-Medical School Dean's Award for Outstanding Faculty Contribution to Graduate Education (2011) Fellow in Medical Sciences of the American Association for the Advancement of Science (2021) As a dedicated educator, Dr. Castilla has mentored numerous graduate students and postdoctoral fellows. His laboratory actively participates in the UMass Medical School's graduate programs, offering rotation projects focused on hematopoietic stem cell function, leukemia initiation mechanisms, and translational research approaches. The lab has secured significant research funding to support its investigations into leukemia mechanisms and targeted therapies. The Castilla Laboratory maintains an active research team including research specialists, postdoctoral associates, and graduate students. The lab has developed important mouse models for studying inv(16) AML and RUNX1-related disorders, contributing significantly to the understanding of leukemia-initiating cells and potential therapeutic approaches targeting CBFβ-SMMHC interactions.

Paolo Nanniمشاهده پروفایل

پژوهشگر

• Proteomics
• Mass Spectrometry
• Protein Characterization
• +۳ مورد دیگر

Paolo Nanni is a senior researcher and Unit Head of Proteomics at the Functional Genomics Center Zurich (FGCZ) , affiliated with ETH Zurich . He coordinates proteomics workflows and applications while supporting diverse analytical workflows including amino acid analysis, protein characterization, and proteome quantification. He actively participates in the LS2 Proteomics Section as part of the Executive Committee and contributes to the proteomics workgroup within the Core for Life alliance. His work focuses on advancing proteomics methodologies, software development for differential expression analysis, and cross-disciplinary applications in clinical research, microbiology, and archaeology. Research Interests Mass spectrometry-based quantitative proteomics Biomarker discovery in periodontal and inflammatory diseases Development of proteomics software tools (prolfqua, prolfquapp) Microbial proteomics and host-pathogen interactions Paleoproteomics for historical diet reconstruction Selected Publications (2021-2025) demonstrate expertise in proteomics standardization, bioinformatics tool development, and applications spanning cancer immunotherapy, microbial biofilms, and food security studies. His work emphasizes multi-lab collaboration and reproducibility in proteomics research. Contributions include participation in international consortia, clinical proteomics for disease characterization, and methodological innovations in chromatographic separation and ADP-ribosylation site identification. He maintains active collaborations across disciplines including oncology, microbiology, and archaeology.

Xijin Geمشاهده پروفایل

استاد

• Bioinformatics
• Artificial Intelligence
• Data Science
• +۳ مورد دیگر

Xijin Ge is a Professor in the Department of Mathematics and Statistics at South Dakota State University (SDSU). His research focuses on bioinformatics, artificial intelligence (AI), and data science, particularly in analyzing genomic datasets and developing tools like iDEP and ShinyGO. He holds a Ph.D. in Engineering from the University of Tokyo (2000), an M.S. in Condensed Matter Physics from Beijing University of Science and Technology (1997), and a B.S. in Applied Physics from the same institution (1994). Dr. Ge teaches courses such as STAT 435/535 (Applied Bioinformatics), STAT 736 (Bioinformatics), and STAT 414/514 (Basic R Programming). His research emphasizes tools for genomic data interpretation, AI-driven data analysis, and large language models like ChatGPT for visualization. His work has led to significant grants, including a $12 million NIH grant (BioSNTR-II) for translational research and a $25 million NSF-funded project (BioSNTR). His publications span bioinformatics, genomics, and computational methods, with notable contributions to RNA-Seq analysis, gene expression profiling, and software development. He leads the Ge Lab, advancing interdisciplinary research and education in data science and bioinformatics.

Kris Sankaranمشاهده پروفایل

استادیار

• Microbiome Analysis
• Statistics
• Data Science
• +۴ مورد دیگر

Kris Sankaran is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on developing statistical tools for microbiological and Earth systems data, emphasizing interdisciplinary applications in health and environmental science. He leads a lab dedicated to multimodal mediation analysis, semisynthetic simulations, and interpretable AI. His work bridges statistical theory and practical data science, with contributions to microbiome analysis, climate modeling, and machine learning interpretability. Notable projects include the 'Multimedia' framework for microbiome data and the 'Highres-net' satellite imagery tool. He has published extensively in top journals and conferences, with a focus on methodological innovation and real-world impact. Education details are not explicitly provided, but his academic contributions span over a decade, with key collaborations in computational biology and environmental analytics. He actively engages in teaching and mentoring, though student names are not listed here. His lab's website provides further details on ongoing research and software tools.

Yves Van de Peerمشاهده پروفایل

استاد

• Bioinformatics
• Evolutionary Genomics
• Plant Biology
• +۳ مورد دیگر

Yves Van de Peer is a Professor and VIB Group Leader at the VIB-UGent Center for Plant Systems Biology. Since 2002, he has led the Bioinformatics and Evolutionary Genomics group, focusing on genome annotation, comparative genomics, and systems biology. He earned his PhD in 1995 from the University of Antwerp. His research emphasizes genome evolution, gene duplication, and plant biology. Notable contributions include multiomics databases (e.g., Water Lily Pond) and tools like the 'doubletrouble' Bioconductor package. He was recognized as a Clarivate Highly Cited Researcher in 2024. His team includes postdocs, PhD students, and technical staff. He recently became Scientific Director of the VIB-UGent Center, succeeding Dirk Inzé. Key research themes: genome evolution, gene duplication, plant systems biology. Recent work explores allergen functions in house dust mites, seagrass metabolites under climate change, and fern cell wall innovations. His lab collaborates internationally on genomics projects, advancing computational methods and evolutionary insights.

• Machine Learning
• Time Series Forecasting
• Credit Card Fraud Detection
• +۳ مورد دیگر

Gianluca Bontempi is a Professor in the Department of Computer Science at the Université Libre de Bruxelles (ULB), Faculty of Sciences. His research spans machine learning, time series forecasting, and big data analytics, with significant applications in credit card fraud detection, biomedical engineering, and causal inference. He leads projects on adaptive learning systems, financial cybersecurity, and high-dimensional data modeling. Research Focus: Bontempi's work integrates theoretical machine learning with real-world challenges. Key areas include: Developing dynamic factor models for multivariate time series forecasting Designing incremental learning frameworks for fraud detection in evolving data streams Advancing causal inference methods for personalized decision-making Creating interpretable AI systems for biomedical and financial applications Publication Trends: His recent articles (2019–2026) show a strong emphasis on combating concept drift in financial fraud systems, improving fairness in feature selection, and enhancing cross-domain adaptation techniques. Methodological innovations in multi-task learning and adversarial modeling are recurrent themes. Awards & Recognition: No awards explicitly mentioned in the source text. Research Infrastructure: Bontempi collaborates with interdisciplinary teams across Europe on large-scale projects involving biomedical data (TCGAbiolinks), urban mobility (traffic forecasting), and AI ethics (CLAIRE COVID-19 initiative).

Hao Wuمشاهده پروفایل

عضو هیئت علمی

• Bioinformatics
• Computational Biology
• Statistical Modeling
• +۵ مورد دیگر

Hao Wu is a Distinguished Professor at the Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology (SUAT). Previously, he served as an Associate Professor at Emory University's Department of Biostatistics and Bioinformatics before transitioning to SUAT in 2023. He holds a Ph.D. in Biostatistics from Johns Hopkins University and additional degrees from Tsinghua and Iowa State Universities. His research focuses on bioinformatics, computational biology, and statistical methods for analyzing large-scale genomic and biomedical data. Key areas include RNA-seq, ChIP-seq, epigenetic modifications, and applications in diseases like cancer and Alzheimer’s. He collaborates with clinicians and developers tools like DSS, TOAST, and FEAST available on Bioconductor. He advises a team of Ph.D. and Master’s students (e.g., Tao Deng, Runshu Wang, Zhiwei Ye) and oversees former students now in academia and industry. His lab (haowulab.org) emphasizes open-source software with over 30,000 annual downloads.

• Alzheimer's Disease
• Sanfilippo Syndrome
• Zebrafish Models
• +۷ مورد دیگر

Associate Professor Michael Lardelli is affiliated with the School of Biological Sciences at the University of Adelaide, within the Faculty of Sciences, Engineering and Technology. He holds an academic rank of Associate Professor and manages the University's Drosophila melanogaster facility. His research focuses on investigating Alzheimer's disease and childhood dementia (Sanfilippo syndrome) using zebrafish and fruit fly models. Key techniques include transcriptomics, proteomics, and metabolomics to study disease mechanisms, particularly mutations in the PRESENILIN 1 gene and their effects on energy metabolism. Collaborations with bioinformatician Karissa Barthelson enhance analysis of multi-omics data. Research projects combine molecular biology, genome editing, and behavioral assays, with recent expansions into Drosophila models. His work explores shared pathogenic mechanisms between Alzheimer's and Sanfilippo syndromes, including oxidative stress responses and mitochondrial dysfunction. Notable contributions include developing zebrafish models for neurodegenerative diseases and creating tools like the tadar R/Bioconductor package for transcriptome analysis. Lardelli's lab actively engages in grant submissions and supervises higher-degree research candidates. The facility oversees Drosophila husbandry standards critical to experimental reproducibility. Despite no explicit awards listed, his publications reflect significant contributions to neurodegenerative disease research methodologies and cross-species disease modeling.

Kelly Streetمشاهده پروفایل

استادیار

• Biostatistics
• Computational Biology
• Single-Cell RNA Sequencing
• +۷ مورد دیگر

Kelly Street is an Assistant Professor in the Division of Biostatistics at the Keck School of Medicine of the University of Southern California (USC), where she conducts research at the intersection of biostatistics, computational biology, and genomics. Her work focuses on developing statistical and computational methods for analyzing high-dimensional biological data, particularly from single-cell sequencing technologies. Her educational background includes a PhD in Biostatistics with a Designated Emphasis in Computational Biology (2018), an MA in Biostatistics (2014), both from the University of California, Berkeley, and a BS in Mathematics (2012) from the University of Chicago. Kelly Street's research interests center on single-cell RNA sequencing (scRNA-seq), with a strong emphasis on trajectory inference to model continuous biological processes such as cell differentiation. She develops methods for differential expression and differential progression across conditions, enabling comparisons of developmental trajectories between experimental groups. Her work extends to multi-omics integration , combining data from diverse assays including ATAC-seq, WGBS, CyTOF, and TCR-seq to gain a more comprehensive understanding of cellular states and functions. She is an active contributor to the Bioconductor project, creating and maintaining software tools that support reproducible and scalable single-cell data analysis. The analysis of her recent publications reveals a consistent focus on improving computational frameworks for single-cell genomics, particularly in modeling dynamic biological processes, enhancing data quality (e.g., CyTOF data cleaning), and enabling cross-condition comparisons in trajectory analysis. Her work bridges statistical theory with practical applications in immunology and cancer research. Invited speaker at major institutions including MD Anderson Cancer Center, Harvard T.H. Chan School of Public Health, and Northwestern University. Regular presenter at BioC and EuroBioc conferences, highlighting her active role in the Bioconductor community. Organizer and instructor for educational workshops such as the Healthcare Innovation Replicathon and Bioconductor training sessions. Kelly Street has advised and collaborated on training programs for undergraduate and graduate students through REU programs and has contributed to open-source software development. While specific grant details are not mentioned, her active research program and workshop leadership suggest ongoing funding support. She has also collaborated with researchers at Dana-Farber Cancer Institute, as indicated by her email address and seminar presentations. Her laboratory or research group appears to focus on developing statistical methods and software for single-cell analysis, likely involving a team of graduate students, postdocs, and collaborators working on trajectory inference, multi-omics integration, and immune profiling applications.

Kathryn Lilleyمشاهده پروفایل

استاد

• Proteomics
• Molecular Biology
• Cell Biology
• +۳ مورد دیگر

Kathryn Lilley is a Professor in the Department of Biochemistry at the University of Cambridge, affiliated with the Cambridge Centre for Proteomics and the Milner Therapeutics Institute. Her research focuses on developing and applying proteomic technologies to study the dynamic proteome and transcriptome in cells, including spatial proteomics and RNA-protein interactions. Her work addresses mechanisms of cellular processes such as differentiation, stress responses, and disease. Her research group develops methods for high-throughput measurement of protein abundance, localization, post-translational modifications, and interactions. Key areas include studying the spatial distribution of the transcriptome, RNA-protein complexes, and the interplay between proteome and transcriptome dynamics. Collaborations with academic and industrial partners enhance applications in drug discovery and disease modeling. Key methodologies include hyperLOPIT, LOPIT-DC, and orthogonal organic phase separation (OOPS) for comprehensive proteomic and transcriptomic analysis. Lilley’s group also explores the functional implications of protein localization and RNA-protein interactions in contexts like cancer, metabolic disorders, and viral infections.

• Pain Neurobiology
• Transcriptomics
• Bioinformatics
• +۵ مورد دیگر

Allison Barry (BSc, MSc, DPhil) is a Canadian-British-German pain neurobiologist currently conducting postdoctoral research at the University of Vienna and University of Texas at Dallas . Formerly a DPhil student at University of Oxford under Prof. David Bennett and Prof. Georgios Baskozos, she specializes in multi-omics approaches to pain pathophysiology, focusing on dorsal root ganglia (DRG) proteomics , spatial compartmental profiling , and iPSC-derived nociceptor modeling . Her work is rooted in open science advocacy and chronic migraine patient perspectives. Education: BSc Neuroscience (2015), Dalhousie University MSc Neurosciences (2017), Max Planck Institute/University of Göttingen DPhil Ion Channels & Disease (2022), University of Oxford Research Focus spans neuropathic pain mechanisms , multi-omics data integration , and sex-specific pain pathways . She employs spatial proteomics , proximity labeling , and synaptosome profiling to dissect sensory neuron subtypes in disease contexts. Her lab-generated DRG Directory database hosts processed sequencing datasets for community reuse. Publication Trends reveal interdisciplinary work bridging neuroscience with bioinformatics , emphasizing TNFα signaling , NKG2D ligand discovery , and neuroimmune interactions . Her methodological critiques (e.g., limitations of silhouette scores in single-cell analysis) highlight rigor in computational pain research . Grants & Collaborations: Funded by NIH, Wellcome Trust, and former Medical Research Council/NSERC support, she collaborates with teams across Harvard , King’s College London , and MDC Berlin . Her work intersects with open science movements and chronic migraine advocacy . Labs & Networks: Previously affiliated with Oxford’s Neural Injury Group, she now contributes to Vienna’s Starkl Lab (immunology focus) and Dallas’ WeirLab (chronic pain reversal strategies). She actively engages with global consortia like Bioconductor and PAIN Journal editorial initiatives.

Radu Zabetمشاهده پروفایل

مدرس ارشد

• Computational Biology
• Genomics
• Gene Regulation
• +۴ مورد دیگر

Dr Radu Zabet is a Senior Lecturer in Computational Biology at the Blizard Institute , Queen Mary University of London. He leads a research group within the Centre for Genomics and Child Health and is affiliated with the QMUL Epigenetics Hub . PhD in Computer Science from University of Kent Postdoctoral experience at University of Kent and University of Cambridge Established independent lab at University of Essex (2015) before relocating to Queen Mary in 2020 Research Interests: Computational modeling of transcription factor-DNA interactions Mechanistic understanding of 3D chromatin organisation Epigenetic regulation through histone modifications and DNA methylation Explainable AI applications in genomics Environmental epigenetics (air pollution exposure) Recent Publications Analysis: Key contributions to 3D chromatin organisation in model organisms Development of epigenetic analysis tools like DMRcaller Integration of machine learning with experimental validation Investigations into enhancer biology and histone code interpretation Environmental epigenetics through DNA methylation profiling Translational focus on genomic medicine applications Scientific Achievements: Awarded MRC Training Fellowship in Bioinformatics Maintains active research group with multiple projects spanning chromatin dynamics and epigenetic regulation Collaborations across computational biology , molecular biology , and medical research Supervision: Currently supervising 5 PhD students in genomic and epigenetic research Lab members: Duarte Polvora Brandao and team

• Bioinformatics
• Computational Biology
• Epigenetics
• +۵ مورد دیگر

Dr. Ning Liu is a Postdoctoral Research Fellow at the South Australian Immunogenomics Cancer Institute (SAiGENCI) within the Faculty of Health and Medical Sciences at the University of Adelaide. As a bioinformatician and computational biologist, Dr. Liu specializes in developing computational tools for epigenetics and transcriptomics research, with particular expertise in next-generation sequencing data analysis including ChIP-seq, ATAC-seq, Hi-C, RNA-seq, single cell RNA-seq, and spatial transcriptomics. Dr. Liu's research focuses on developing novel computational methods to study the human genome in the context of transcriptomics and epigenomics. Currently, Dr. Liu maintains three Bioconductor software packages: standR for spatial transcriptomics analysis of GeoMx DSP data, hoodscanR for cellular neighborhood detection in spatial transcriptomics, and scider for modeling global cell density in spatial transcriptomics data. With strong emphasis on translating biological insights to medical applications, Dr. Liu's work bridges computational methodology with clinical relevance in cancer research and immunology. Dr. Liu is eligible to supervise Masters and PhD students as a Co-Supervisor, reflecting their established expertise in bioinformatics and computational biology. Their research has resulted in numerous publications focusing on spatial transcriptomics, cancer immunology, and computational tool development, with applications in understanding tumor microenvironments and immunotherapy response. Dr. Liu's scholarly contributions demonstrate a consistent focus on spatial biology methodologies applied to cancer research, particularly head and neck cancer. Their publications reveal expertise in developing computational frameworks that address technical challenges in spatial transcriptomics while providing biological insights with potential clinical applications in immunotherapy. As a research supervisor, Dr. Liu contributes to the academic community by mentoring graduate students in computational biology techniques and applications. Their work environment at the Adelaide Health and Medical Sciences building on the West End Health Precinct provides access to state-of-the-art facilities supporting their computational research in cancer immunogenomics.

• Bioinformatics
• Computational Biology
• Medical Informatics
• +۴ مورد دیگر

Dr Wenjun Liu is a Postdoctoral Researcher at the University of Adelaide, affiliated with the Faculty of Health and Medical Sciences and the School of Biomedicine. She works in the Adelaide Centre for Epigenetics under Professor Jose Polo's group, applying bioinformatics approaches to cancer epigenetics research. Double undergraduate degree in Teaching and Science (Genetics, Statistics), 2019 Master of Philosophy upgraded to PhD, 2020-2023 Her research spans transcriptomics, bioinformatics, and medical data analysis, with a focus on neurodegeneration and breast/ovarian cancer. She developed the R/Bioconductor package sSNAPPY for pathway perturbation analysis and tutored courses in mathematics, statistics, and bioinformatics during her PhD. Recent publications highlight her work in structural immunology, bioinformatics tool development, and molecular mechanisms of neurodegenerative diseases. She contributes to computational biology through software innovation and collaborative research.