Biomedical Informatics
Contact Information
Faculty and their Research Interests
The local 3D environment (secondary structure and atoms) surrounding a predicted functional site in a protein structure with unknown function from the Structural Genomics project.
The mission of the Biomedical Informatics (BMI) training program is to provide graduate training in the application of information technologies to problems in biomedical research. The focus of the training is on the creation, validation and application of novel methods for capturing, representing, storing, retrieving, visualizing and analyzing biomedical data and knowledge. The Biomedical Informatics program was founded in 1982 and broadly encompasses the areas of bioinformatics and clinical informatics.Trainees learn to work and communicate effectively at the intersection of contributing disciplines, including biology, medicine, computer science, probability and statistics, and the decision sciences. Trainees are expected to understand the ethical, legal and social implications of the technologies they use. Stanford provides an extraordinary environment to pursue interdisciplinary education in the development of novel informatics methodologies with applications spanning the full range of biomedicine.
Candidates in the BMI program may focus on research in any aspect of information management and analysis along the biomedical research pipeline. They are united in their interest in using information technology to manage, analyze and understand biomedical data, and in developing new approaches to using information to improve health care. Specific areas of investigation include: decision-support systems, knowledge acquisition, medical records, computational biology, systems biology, simulation, biological sequence analysis, biological 3D structure representation, pharmacogenetics, pharmacogenomics, genomics, collaborative technologies, network-based representation and retrieval of biomedical information and literature, medical imaging, reasoning under uncertainty, controlled terminologies for medicine and biology, technology assessment, and health-services research. The course of study requires training in the informatics methods used to represent knowledge and develop models, the computer science (CS) to implement these representations and models, and the specialized biomedical domain knowledge necessary to identify and make an impact upon important problems. Towards this end, students must take courses in 1) mathematics/CS that provide fundamental understanding of how knowledge is represented mathematically and how models are developed, 2) CS/biomedical informatics that develop understanding of how models are implemented and the technical requirements of the medium, e.g., programming languages, machine architectures, databases and algorithms, 3) the bioscience curriculum that give them deep understanding of some area of biology or medicine, and 4) social policy and ethics that examine the societal impact of new technologies.
For more information contact
Biomedical Informatics Training Program
1265 Welch Road, MSOB X215
Stanford, CA 94305-5479
(650) 723-1398
(650) 725-7944 (fax)
bmi-contact@lists.stanford.edu
http://bmi.stanford.edu
To address diverse needs and backgrounds of students interested in biomedical informatics, the BMI program offers research-oriented graduate degrees and professional education. Candidates with training in computer science, the biosciences and other related fields are preferred.
Faculty and Their Research Interests
Core Faculty are members of the Executive Committee and are advising faculty
Advising Faculty may serve as primary advisor for BMI students
Collaborating Faculty may serve as co-advisors for BMI students
Core Faculty (Executive Committee members)
Russ B. Altman. (Program Director). Research focuses on the creation of computational tools and resources to solve problems in biology and medicine. Current projects are focused on three areas: 1) creating a database for how genetic variation in humans is associated with differences in drug response (pharmacogenomics), with particular recent emphasis on the drug warfarin, (PharmGkb), 2) creating methods for identifying protein and RNA molecular function, in order to understand how we may engineer them to function differently (FEATURE) and 3) understanding how physics-based simulation of biological structures can be facilitated at scales ranging from molecules to intact humans (Simbios). Informatics methodologies include: supervised and unsupervised machine learning, natural language processing, molecular dynamics simulations, database design, knowledge representation.
Atul Butte. Translational bioinformatics has been defined as the development of analytic, storage, and interpretive methods to optimize the transformation of increasingly voluminous genomic and biological data into diagnostics and therapeutics for the clinician. The research goal is to develop translational bioinformatics methods to reason over many available genome-scale measurement and experimental modalities, and apply these methods to study complex disorders in genomic medicine, especially obesity and type 2 diabetes mellitus. The Butte Lab has four main directions in exploring integrative biology. First, we have developed bioinformatics methods to integrate genomic, genetic, phenotypic, clinical, and gene-knockout data from multiple sources and phenotypes and reason over these data. An example of this was our work in adipogenesis published in Nature Cell Biology (2005). Second, we have developed tools to automatically index and find genomic and proteomic data sets based on the phenotypic and contextual details of each experiment, published in Nature Methods (2007). We used these tools to create a comprehensive phenome-genome network published in Nature Biotechnology (2006). Third, we are building a novel gene-expression-based classification scheme for diseases across the entire field of medicine, as described in the New York Times and International Herald Tribune (2008). Fourth, we consider clinical measurements with molecular measurements to build multi-scale models of human health and disease, as published in Science (2008).
Teri E. Klein. Research interests extend over the broad spectrum of pharmacogenetics, computational biology and bioinformatics. Applications include the development of a pharmacogenetics knowledge base, structure-function relationships, de novo modeling and the structural basis of disease.
Mark Musen. Research interests involve construction of automated systems to assist biomedical decision making, focusing on areas where the decision making is impeded by difficulties in formalizing knowledge and in encoding that knowledge for use by the computer. Current work addresses mechanisms by which computers can assist communities of scientists in the development of large, electronic knowledge bases. The Protégé system provides an experimental framework for investigation of collaborative knowledge-base development, of mapping among knowledge bases, and of knowledge-base visualization. The National Center for Biomedical Ontology drives research on ontology-based access to biomedical data and knowledge, community-based peer review of electronic knowledge bases, and management of knowledge-base evolution. The laboratory studies architectures for intelligent systems in areas as diverse as protocol-based therapy and surveillance for possible bioterrorism.
David Paik. Research interests lie at the intersection of radiology, molecular biology and informatics. We focus on developing and validating computational methodologies for extracting useful information content from anatomic, functional and molecular images, drawing upon image processing, computer vision, computer graphics, computational geometry, machine learning, biostatistics, modeling and simulation.
Daniel Rubin. Research interests focus on biomedical and translational imaging informatics. We develop computational methods to identify and to extract information and meaning from images ("imaging phenotype") and to integrate and relate the image information to biological and clinical data ("molecular/clinical phenotype"). Our goal is to exploit images on a massive scale for discovery, similar to the data-driven approaches in modern bioinformatics, enabling us to discover image biomarkers of disease and to build predictive disease models from image data. We translate our methods into practice by creating computer applications (such as decision support) that will improve diagnostic accuracy and clinical effectiveness.
Nigam Shah. My research interest is to make biomedical information actionable. I am interested in: (1) Annotation Analytics: In order to understand the "gene lists" from analysis of high-throughput data, researchers routinely use Gene Ontology based analyses. With available methods for automated annotation and the existence of over 200 biomedical ontologies, it's time for "big data" mining in annotation analysis. We have created over 5 billion annotations on 20 public data sources and need miners. (2) Data driven medicine: The goal of this research is to combine machine learning and text-mining with prior knowledge encoded in medical ontologies to discover hidden trends and build risk models as well as drive data driven decision making and comparative effectiveness studies. For example, annotation analysis can identify combinations of drug classes, risk-factors and co-morbidities that are common in patients with a certain outcome—e.g. those re-admitted after transplant surgery—to provide candidate hypotheses about the possible causes as well as predictors of that outcome. (3) Socially Conscious Informatics: Clinical decision support tools traditionally focus on supporting a high trained individual—the doctor. Let's turn decision support on its head to aid the patient and provide support on a cell phone; especially in situations where there is lack of access to a highly trained individual. A cell phone app can diagnose respiratory distress or severe dehydration in a child. Just these two conditions kill over 35% of the estimated 10.6 million child deaths each year.
Advising Faculty
Serafim Batzoglou. Our lab is interested in the applications of mathematics and computer science to genomic research. Current research focuses on alignment algorithms, comparative genomics, gene regulation, regulatory motif finding, and microarray analysis.
Mohsen Bayati. I have two main research interests: large-scale statistical data-mining, and applications of information technology in healthcare. In particular, I use tools from graph theory, machine learning, probability, and statistical physics in data-driven healthcare (predictive models, optimization, and decisions), high dimensional statistics, and networks.
Gill Bejerano. Our lab seeks to understand the human genome through vertebrate comparative, functional, and paleo-genomics, including such topics as the functional landscape of the human genome and its evolution, with particular focus on vertebrate gene regulation and its contributions to morphological diversity, development, and human disease; functions, origins, and evolution of proximal and distal cis-acting regulatory elements; the paradoxical existence of ultraconserved elements; co-option of mobile DNA elements (repeats) as a driving force in vertebrate evolution; and the interpretation of ancestral genomes.
Kwabena Boahen. Our group has two synergistic goals: To understand how brains work; this will advance treatment of neurological diseases. And to build computers that work like brains; this will increase computational power a million fold. To these two ends, we are building large-scale neural models to link cellular-level biophysical processes with the system-level functions that they enable (e.g., cognition), through an interdisciplinary effort that brings electronics and computer science in contact with neurobiology and medicine.
Margaret Brandeau. Research focus is the application of mathematical and economic models to evaluate disease prevention and treatment programs. Current research focuses on HIV and drug abuse interventions, hepatitis B screening and vaccination, pandemic influenza preparedness, and bioterror response planning.
Douglas Brutlag. Professor Emeritus of Biochemistry and Medicine (by courtesy), teaches courses in Genomics and Medicine (Biochem 118), Genomics, Bioinformatics and Medicine (Biochem 158/258 and HumBio 158G), Computational Molecular Biology (BIOC 218/BIOMEDIN 231) and Your Genes and your Health (Bio 84).
Carlos Bustamante. Research focuses on analyzing genome wide patterns of variation within and between species to address fundamental questions in biology, anthropology, and medicine. My group works on a variety of organisms and model systems ranging from humans and other primates to domesticated plant and animals. Much of our research is at the interface of computational biology, mathematical genetics, and evolutionary genomics.
J. Michael Cherry. Lab develops and maintains the Saccharomyces Genome Database (SGD). The SGD provides information and tools on budding yeast genome, its products and their interactions. Several computational tools have been developed to provide to allow the research community to explore the collected data sets. Tools for querying >50,000 full-text papers are also provided. SGD has become an essential research tool used daily by thousands of researchers around the globe. Dr. Cherry's second area of research is in the creation of ontologies to aid communication between biologists as well as biological database projects. His group is a founding member of the Gene Ontology (GO) Collaboration.
Markus Covert. Research focus is on building computational models of complex biological processes and using these models to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory, and signaling networks as well as intercellular interactions. Current research involves the dynamic behavior of NF-kappa B, an important family of transcription factors whose aberrant activity has been linked to oncogenesis, tumor progression, and resistance to chemotherapy.
Rhiju Das. The Das group strives to predict how sequence codes for structure in proteins, nucleic acids, and heteropolymers whose folds have yet to be explored. We use new computational and experimental tools to tackle the de novo modeling of protein and RNA folds, the high-throughput structure mapping of riboswitches and random RNAs, and the design of self-knotting and self-crystallizing nucleic acids.
David Dill. Our lab is interested in Boolean modeling to gaining insight into cellular processes at a systems level. Our work includes analysis of Boolean circuit models using methods based on logic and automata theory, applied to understanding of the cell cycle, signal transduction networks, etc., and Boolean analysis of relationships in multiple large data sets, to understand regulation and global differences in gene expression among cell types.
James Ferrell. Research lab has two main goals: to understand the regulation of entry into and progression through mitosis and meiosis, and to understand the basic logic of signaling cascades and loops.
Hunter Fraser. We study the regulation and evolution of gene expression using a combination of experimental and computational approaches. Our work brings together quantitative genetics, genomics, epigenetics, and evolutionary biology to achieve a deeper understanding of how genetic variation within and between species affects genome-wide gene expression and ultimately shapes the phenotypic diversity of life. Some of our long-term goals are to better understand: 1) How new mutations affect gene expression, 2) What selective pressures act on these mutations, 3) How (and how often) changes in gene expression affect other phenotypes, including human disease.
Sam (Sanjiv) Gambhir. Research focuses on merging advances in molecular biology with those in biomedical imaging to advance the new field of molecular imaging. Strategies for imaging cellular/molecular events in small animals and humans are in development. These include studying gene expression, signal transduction, enzyme levels and receptor levels in vivo. Use of these technologies for better management of cancer patients are emphasized. Mathematical modeling of these processes to better quantitate imaging data are also being pursued.
Mary Goldstein. Health services research in primary care and geriatrics. Ongoing work includes evaluation of methods of implementing clinical practice guidelines, for which she leads a multisite hypertension guidelines project using the ATHENA decision support system. Another research focus is evaluation of newly developed tools for automated guidelines, particularly for quality assessment.
Leonidas Guibas. Heads the Geometric Computation group in the Computer Science Department of Stanford University and is a member of the Computer Graphics and Artificial Intelligence Laboratories. He works on algorithms for sensing, modeling, reasoning, rendering, and acting on the physical world. His interests span computational geometry, geometric modeling, computer graphics, computer vision, sensor networks, robotics, and discrete algorithms --- all areas in which he has published and lectured extensively.
Trevor Hastie. Specializes in applied nonparametric regression and classification. His current research focuses on applied problems in biology and genomics, medicine and industry, in particular data mining, prediction and classification problems.
Susan Holmes. Applications to Biology, in particular phylogenetic trees. Computational statistics, in particular, nonparametric computer intensive methods such as the bootstrap. Teaching using simulations and web-based tools. Image analysis. Immunology.
Daphne Koller. Research focuses on applying machine learning and probabilistic methods to the analysis and reconstruction of cellular networks. Current projects include the extraction of regulatory networks from gene expression data; the analysis of the effect of individual genetic variation on regulation and phenotype; and understanding how different network structures manifest in terms of gene expression, phenotype, genetic interactions, and more.
Michael Levitt. Research asks if it is possible to understand the molecular structure and function of proteins and nucleic acids in enough detail to make accurate predictions about structure and function. We are mounting a two-pronged attack on this problem using both molecular dynamics simulation and molecular modeling.
Chris Longhurst - In my administrative role at Lucile Packard Children's Hospital, I help oversee the implementation of an electronic medical record (EMR) system that includes computerized physician order entry (CPOE) with clinical decision support (CDS) and a personal health record (PHR). My research is focussed on rigorously evaluating the best ways to implement and optimize health information technology to positively impact patient safety and quality of care for the pediatric and obstetric patients we serve at Lucile Packard Children's Hospital (LPCH). One specific area of focus is evaluating the impact of these systems on tangible clinical outcomes such as hospital-wide mortality. I am also interested in innovating new approaches to using the EMR as a tool to support clinical resource management and safe patient handoffs, and am leading efforts to evaluate the potential of PHR's as a tool to facilitate participatory medicine. Finally, I collaborate with colleagues at the Stanford Center for Biomedical Informatics Research (BMIR) to use the wealth of information in our hospital EMR to generate new knowledge about our patients.
Henry Lowe. Primary research interests are in the areas of clinical and research information systems design, development and evaluation including multimedia clinical systems, integration of data to support patient care and clinical research, biomedical terminologies, automated indexing of biomedical documents, cancer information systems and biomedical data security.
Sandy Napel. Research focuses on CT and other medical imaging modalities. Our lab is currently interested in efficient and reproducible methods of extracting and visualizing medical information from the thousands of images typically generated by one or more radiological exams performed for each patient.
Richard Olshen. Research is in statistics and their applications to medicine and biology. Many efforts have concerned tree-structured algorithms for classification, regression, survival analysis, and clustering.
Art Owen. Research centers on four areas: technology assessment with a special emphasis on cost-effectiveness analysis, the application of decision theory to clinical and health policy problems, the development of methods for creation of practice guidelines, and decision support.
Douglas K. Owens. Research concerns health policy, clinical policy, and the development of analytic methods for evaluating policy questions. Particular interest in technology assessment and the application of decision theory to clinical/health policy problems. Secial interest in questions related to disease caused by the human immunodeficiency virus (HIV) and cardiovascular disease.
Vijay Pande. My research interests lie at the intersection of machine learning, Bayesian statistics, atomistic simulation, bioinformatics, and cheminformatics methods and its application to problems of linking drug efficacy and side effects to geneomics and systems biology. My group also has expertise in related synergistic areas, such as theoretical physical chemistry, structural biology, computer science, and large-scale distributed computing. By combining our methods with the Folding@home distributed computing project (currently the most powerful supercomputer in the world, with almost 10 petaflops of performace), we have a unique opportunity to push the state of the art in these and related areas. Finally, via collaborations with biotechs, pharmaceutical companies, and experimental groups interested in drug design, we can directly test our predictions, thus strengthening our methods as well as the direct impact of our results.
Dmitri Petrov. Three main topics are studied in the lab: 1) mutation and evolution of global genomic properties, 2) evolution and population dynamic of transposable elements in eukaryotes, and 3) evolution and population dynamic of transposable elements in eukaryotes.
Sylvia Plevritis. Research program focuses on computational modeling of cancer biology and cancer outcomes. We develop stochastic models of the natural history of cancer based on clinical research data. We predict population-level cancer outcomes under different screening and treatment interventions. We also analyze genomic and proteomic data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.
Ingmar H. Riedel-Kruse . Our lab is focused at two topics: (1) Engineering (and programming) biological games and proving their utility for education and large scale science. (2) Quantitative and modeling approaches to decipher the biophysics and genetic network dynamics underlying vertebrate development and pattern formation - with a longer term interest in tissue engineering. We have a variety of rotation projects; and based on your specific interest you can use and learn a number of techniques, such as zebrafish, micro-fluidics, programming, theory, molecular and cell biology, imaging - or any combination thereof.
Chiara Sabatti. My research focus is on developing statistical methods for the analysis of high throughput genomics data. Areas of particular interests at the moment are: association mapping of multiple related phenotypes, DNA copy number variant detection, analysis of rare variants in population isolates and reconstruction of gene regulatory networks.
Gavin Sherlock. Research uses experimental laboratory and computational approaches to solve biological problems. We are using microarray technology to define all transcripts in the yeast genome, and to understand the changes in genome architecture and the transcriptome that occur in yeast as they evolve in vitro. We are also developing novel yeast strains for use in biofuel production, with the aim of being able to ferment five-carbon sugars such as xylose, as well as 6-carbon sugars into ethanol. In addition, we developed and run the Stanford Microarray Database, the Tuberculosis Database, the Candida Genome Database, and SOURCE. Finally, we also write software for the analysis and visualization of microarray data, including GO::TermFinder, Caryoscope, and GeneXplorer.
Arend Sidow. Current projects are in developmental genomics (mouse), gene regulation and chromatin function (mouse and human), cancer genomics (human), and inherited rare disorders (human).
Hua Tang. Genetic variation does not only underlie phenotypic diversity among individuals, but also documents the evolutionary history of a species. Research in our laboratory aims to uncover the evolutionary forces that have shaped the patterns of genetic variation in humans, to elucidate the genetics basis of complex traits, and to shed light on the mechanisms that lead to diverse phenotypes and disparate disease risk among populations. We approach these questions by developing statistical and computational approaches, by analyzing large-scale genomic data, and by collaborating with experts in a variety of fields.
Robert Tibshirani. Research is in applied statistics and biostatistics. Our lab specializes in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis.
Samson Tu. Modeling of biomedical ontologies and clinical guidelines and protocols, development of knowledge-based systems, knowledge representation, databases, temporal database and temporal reasoning, protocol-based health care.
Michael G. Walker. Research interests include the genetics of disease, intelligence, and aging. He also provides statistics consulting to biotechnology and medical device companies and consults to venture capital companies evaluating investments in these areas.
Lei Xing. Medical imaging informatics, image reconstruction, Image-guided intervention, CT, MRI and radionuclide imaging (PET/CT, SPECT/CT), intensity modulated radiation therapy (IMRT), treatment planning and plan optimization, image segmentation and deformable registration, tele-radiology/treatment planning, radiobiology modeling, biologically conformable radiation therapy (BCR), application of molecular imaging to radiation oncology.
Collaborating Faculty
Euan Ashley. The Ashley lab is focused on understanding the integrative function of the heart. Specifically, we are interested in myocardial adaptation, the process by which the heart adapts to exercise or disease. We are fascinated by network biology and although some of our questions can be answered entirely in silico, for most questions we turn to the wet lab to explore the biology of key genes and signaling modules. We use cell systems, transgenic models and microsurgical models of disease. A major part of our effort is in human genetic variation. With Stanford collaborators, we are developing methods for the clinical application of whole genome sequencing. In addition, we lead the Stanford Center for Inherited Cardiovascular Disease focusing in particular on cardiomyopathy and channelopathy. Another major interest is the apelin-APJ signaling system. We were among the first to describe the significance of this G-protein coupled receptor system for heart failure and cardiovascular disease. In all our work, we take a translational focus, and we are actively moving these insights from the bench to the clinic.
Stanley N. Cohen. The collection and interpretation of large amounts of data obtained from DNA and protein microarrays has become an important approach toward understanding the biological regulatory circuits that control gene expression. In the prevailing paradigm, clusters of genes that show common patterns of expression on microarrays are identified computationally and relationships among these genes are inferred by the experimenter in part by using his/her prior knowledge.
Ronald Davis. Our lab is using Saccharomyces cerevisiae and Human to conduct whole genome analysis projects. The yeast genome sequence has approximately 6,000 genes. We have made a set of haploid and diploid strains (21,000) containing a complete deletion of each gene. In order to facilitate whole genome analysis each deletion is molecularly tagged with a unique 20-mer DNA sequence. This sequence acts as a molecular bar code and makes it easy to identify the presence of each deletion.
Joshua E. Elias. Developing new mass spectrometry-based experimental and computational tools that advance the field of proteomics, and applying them to a variety of important biomedical paradigms, including cancer, aging, and stem cell biology.
Will Greenleaf. Our lab focuses on developing methods to probe the genome and epigenome at the single-cell and single-molecule levels. Our efforts are split between building new tools to leverage the power of high-throughput sequencing and cutting-edge microscopies, and bringing these new technologies to bear against basic biological questions of genomic and epigenomic variation.
Mark A. Hlatky. Main research work is in "outcomes research", especially examining the field of cardiovascular medicine. Particular areas of interest are the integration of economic and quality of life data into randomized clinical trials, evidence-based medicine, decision models, and cost-effectiveness analysis. I am also interested in the application of novel genetic, biomarker, and imaging tests to assess risk and guide clinical management of coronary artery disease.
Hanlee Ji. Research group is focused on the genomic analysis of cancer with several general goals. 1) Developing novel, cost-effective genomic technologies involving next generation DNA sequencing for application in cancer mutation characterization and genomic biomarker discovery. My group is focused on integrating genomic analysis with clinical issues in oncology and integration into oncology clinical trials. 2) Developing computational analytical methods for next generation sequencing and application in genomic diagnostic technologies. 3) Applying functional comparative genomic approaches to identify genes involved in genomic instability in colorectal carcinoma and understanding these genes role in biological networks.
Peter Karp. Research areas of bioinformatics databases, metabolic-pathway bioinformatics, and database interoperation.
Karla Kirkegaard. Lab investigates the cell biology, genetics and biochemistry of RNA viral propagation, using poliovirus as a model system. For many subcellular viruses and parasites, RNA, not DNA, is the carrier of genetic information. Poliovirus serves as a model to increase our understanding of positive-strand RNA viruses for which no vaccine is available and which remain a significant health hazard: examples include other picornaviruses, such as rhinoviruses, coxsackieviruses and the deadly enterovirus 71, as well as more distantly related positive-strand RNA viruses such as hepatitis C and Dengue fever.
Thomas Krummel. Surgical Innovation, Simulation and Virtual Reality in Surgical Education, Fetal Healing-Cellular and Biochemical Mechanisms.
Jin Billy Li. The landscape of RNA editing in the transcriptomes The main interest of Jin Billy Li's lab is to identify and interpret the RNA editing sites using a variety of approaches including genomics, technology development, and computational biology. RNA editing is a phenomenon where genomically encoded information is changed in the RNA. Adenosine-to-Inosine (A-to-I) editing is the most common type of editing, and is achieved by enzymes called Adenosine deaminase acting on RNA (ADAR). RNA editing is critical because ADAR knockout mice die before or shortly after birth. Despite the fact that RNA editing was first discovered over twenty years ago, it has been surprisingly underappreciated and under explored. Very few RNA editing sites had been discovered in humans, mainly due to technological barriers. We recently expanded the RNA "editome" to about 400 sites by computational prediction followed by targeted next generation sequencing (Li et al., Science 2009, 324:1210-1213). This, however, is probably just tip of the iceberg. Our lab will continue the discovery of the RNA editing sites in the transcriptomes of human and may model organisms, as well as various disorders such as autism and cancers. Our main approach is next generation sequencing and computational data analysis. Bioinformatics skills are also needed in a genome-wide association study to link genetic variations with the RNA editing level of a nearby editing site. In a longer term, we aim to perform functional genomic screening of these newly identified RNA editing sites.
Vinod Menon. Experimental and theoretical systems neuroscience: Cognitive neuroscience; Cognitive development; Psychiatric neuroscience; Functional brain imaging; Dynamical basis of brain function; Nonlinear dynamics of neural systems .
Garry Nolan. The lab focuses on signaling in the immune system. Autoimmunity, cancer, leukemia and systems biology are prominent in our studies. We use Flow Cytometry (FACS) of phosphoprotein activation states in single cells in cancer and autoimmune disease (recent CELL and Science papers), machine learning of signaling status. We are using these techniques to study B and T cell signaling, dendritic cell function, and other immune parameters by analysis of biochemical functions at the single cell level.
Ross D. Schacter. Dr. Schacter's early work developed a method for purchasing an expert's forecast that encourages accurate revelation of the expert's beliefs as probabilities. His interest in medical decision analysis led to joint work on scheduling patients for follow-up bladder cancer therapy. In recent years, his research has focused on the representation, manipulation, and analysis of uncertainty and probabilistic reasoning in decision systems. As part of this work, he developed the DAVID influence diagram processing system for the Macintosh. He has worked closely with many students in Bioinformatics, where he holds a courtesy appointment.
Robert Shafer. Research is on the mechanisms and consequences of HIV evolution with an emphasis on HIV drug resistance. Maintains an online database (HIV Drug Resistance Database ) designed to provide a publicly available resource for those performing HIV drug resistance surveillance, interpreting HIV drug resistance tests, and developing new antiretroviral drugs.
Michael P. Snyder. Snyder laboratory the first to perform a large-scale functional genomics project in any organism, and currently carries out a variety of projects in the areas of genomics and proteomics both in yeast and humans. These include the large-scale analysis of proteins using protein microarrays and the global mapping of the binding sites of chromosomal proteins. His laboratory built the first proteome chip for any organism and the first high resolution tiling array for the entire human genome.
Julie Theriot. Research concentrates on interactions between infectious bacteria and the human host cell actin cytoskeleton. Listeria monocytogenes and Shigella flexneri are unrelated food-borne bacterial pathogens that share a common mechanism of invasion and actin-dependent intercellular spread in epithelial cells. Our studies fall into three broad areas: the biochemical basis of actin-based motility by these bacteria, the biophysical mechanism of force generation, and the evolutionary origin of pathogenesis.
Paul (PJ) Utz. Research goal is to develop a better understanding of the pathogenic mechanisms underlying systemic lupus erythematosus (SLE) and other autoimmune diseases by exploring signaling pathways in blood cells, autoantibody production by B lymphocytes, and novel therapeutics targeting these and related pathways.