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International Imaging Genetics Conference (www.imaginggenetics.uci.edu): This conference happens every January at UCI, and is open to everyone interested in the field of imaging genetics, its development and methods, and its applications.

Please select a campus to see the list of scheduled events.

• UC Davis

  • UC DAVIS RESEARCHER RECEIVES $2.6 MILLION GRANT TO STUDY MOST COMMON HUMAN GENETIC DELETION
    The National Institutes of Health (NIH) has awarded Tony J. Simon, pediatric cognitive neuroscientist with the UC Davis M.I.N.D. Institute, a five-year, $2.6 million grant to study the syndrome associated with single most common genetic deletion in humans — chromosome 22q11.2 deletion syndrome (22q11.2DS). The deletion can lead to cognitive impairments and result in a broad range of disorders, including autism, attention-deficit hyperactivity disorder (ADHD) and schizophrenia.

• UC Irvine

  • UCI ICTS Brain Map Lecture Series
    The ICTS Neuroimaging Unit presents a series of monthly Brain Map Talks on a wide range of different aspects of human brain mapping. Talks are held at various UCI main campus locations.
  • UCI DEPARTMENT OF STATISTICS SEMINAR SERIES
    The Lasso: Some Novel Algorithms and Applications
    Rob Tibshirani, Associate Chairman and Professor Department of Health Research and Policy Department of Statistics Stanford University
    4/28/2009: 11:00am-12:00pm - Donald Bren Hall 2011

    I will discuss some procedures for modeling high-dimensional data, based on L1 (lasso) -style penalties. I will describe pathwise coordinate descent algorithms for the lasso, which are remarkably fast and facilitate application of the methods to very large datasets for the first time. I will then give examples of new applications of the methods to microarray classification, undirected graphical models for cell pathways, and the fused lasso for signal detection, including comparative genomic hybridization.
  • UCI ICTS BRAIN MAPPING SEMINAR
    Neurorobotics, Neuromodulation, and Modeling Cognitive Function
    Jeff Krichmar, Ph.D., Assistant Professor, Dept. Cognitive Sciences
    4/10/2009: 12:00pm-1:00pm - Social Science Plaza A 2112

    I will discuss some procedures for modeling high-dimensional data, based on L1 (lasso) -style penalties. I will describe pathwise coordinate descent algorithms for the lasso, which are remarkably fast and facilitate application of the methods to very large datasets for the first time. I will then give examples of new applications of the methods to microarray classification, undirected graphical models for cell pathways, and the fused lasso for signal detection, including comparative genomic hybridization.
  • UCI DEPARTMENT OF STATISTICS SEMINAR SERIES
    High Dimensional Statistics in Genomics: Some Problems and Solutions
    Hongzhe Li, Department of Biostatistics & Epidemiology University of Pennsylvania
    4/7/2009: 11:00am-12:00pm - Donald Bren Hall 2011

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.

• UC Los Angeles

  • No events scheduled at this time.

• UC San Diego

  • SALK INSTITUTE FOR BIOLOGICAL STUDIES
    Optogenetics: Development & Application
    Karl Deisseroth, M.D., Ph.D., Assistant Professor, Bioengineering; Assistant Professor, Psychiatry & Behavioral Science, Stanford University
    5/21/2009: 4:00pm - Frederic de Hoffmann Auditorium

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.
  • SALK INSTITUTE FOR BIOLOGICAL STUDIES
    Non-random Segregation of Sister Chromatids in Murine Colon Cells
    Peter Lansdorp, M.D., Ph.D., Professor of Medicine & Senior Scientist, The BC Cancer Research Centre, Canada
    5/14/2009: 4:00pm - Frederic de Hoffmann Auditorium

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.
  • SALK INSTITUTE FOR BIOLOGICAL STUDIES
    Single Molecule Views of Molecular Motors Moving on RNA
    Taekjip Ha, Ph.D., Investigator, HHMI, Professor of Physics & Biophysics, University of Illinois at Urbana-Champaign
    5/7/2009: 4:00pm - Frederic de Hoffmann Auditorium

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.
  • SALK INSTITUTE FOR BIOLOGICAL STUDIES
    Protein Identification by MS: 2D or not 2D...
    Wolfgang Fischer, Ph.D., Director, Mass Spectrometry Center, Salk Institute for Biological Studies
    4/30/2009: 12:00pm - Trustees Room

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.
  • SALK INSTITUTE FOR BIOLOGICAL STUDIES
    A Quantitative Proteomic Approach to Understand Protein Phosphorylation During Brain Development
    Lujian Lian, Ph.D., Manager, Proteomic Mass Spectrometry Lab, The Scripps Research Institute
    4/30/2009: 12:00pm - Trustees Room

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.
  • SALK INSTITUTE FOR BIOLOGICAL STUDIES
    Metabolic Flexibility & Suspended Animation
    Mark Roth, Principal Investigator, Division of Basic Sciences, Fred Hutchinson Cancer Research Center
    4/30/2009: 4:00pm - Frederic de Hoffmann Auditorium

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.
  • SALK INSTITUTE FOR BIOLOGICAL STUDIES
    Alternative Lengthening of Telomeres in Mammalian Cells
    Roger R. Reddel, Ph.D., Sir Lorimer Dods Professor, Director, Children's Medical Research Institute, University of Sydney, Australia
    4/23/2009: 4:00pm - Frederic de Hoffmann Auditorium

    Large-scale systematic genomic datasets have been generated to inform our biological understanding of both the normal workings of organisms in biology and disrupted pathways which cause human disease. The integrative analysis of these vast amounts of diverse types of quantitative data, which has become an increasingly important part of genomics and systems biology research, poses many interesting statistical problems, largely driven by the complex inter-relationships between these high dimensional genomic measurements. In this talk, I will present three problems in genomics research that require the development of new statistical methods: (1) identification of active transcription factors in microarray time-course experiments; (2) identification of sub-networks that are associated with clinical outcomes; and (3) identification of genetic variants that explain higher-order gene co-expression modules. I will present several regularized estimation methods to address these questions and demonstrate their applications using real data examples. I will also discuss some theoretical properties of these procedures.

• UC San Francisco
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