Josh Stuart, Ph.D.

Professor, Biomolecular Engineering Department, University of California Santa Cruz, “Unmasking All Forms of Cancer: Toward integrated maps of all tumor subtypes at 11:00 am on Thursday, February 16, 2017, in Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum.

jstuart

Abstract: The varieties of cancer seem numberless. From classic tell-tale genomic alterations like the Philadelphia chromosome in CML, to the recurrent and specific amino acid V600E BRAF mutations in melanoma, from HER2 amplifications in some breast cancers, to hypermutated tumors in colorectal cancers linked to epigenetic changes. Are tumors that arise in different tissues distinct? Is every patient’s tumor distinct? Or are there underlying connections to help construct a molecular taxonomy of cancer’s forms?

In this talk, I will present results from the TCGA Pan-Cancer analysis project to investigate cancer’s forms in the most comprehensive study of tumor subtypes attempted to date. We derived a map of tumor classes encompassing an integrated view of six different omics datasets. While most tumors (90%) cluster with others from the same tissue of origin, a significant fraction (10%) are reclassified into groups of multiple tissue types. Data on patient outcomes suggests the reclassification could provide important information to consider for treatment. I will also present novel pathway analysis methods and landscape visualization techniques that help probe further into these results.

Biography:  Dr. Stuart received his PhD in Biomedical Informatics at Stanford University.  He has expertise in developing computational models to integrate multiple sources of information and a background in machine-learning applied to high-throughput datasets. He has recently developed pathway-based models to integrate multiple sources of gene activity to predict alterations and clinical outcomes in tumor samples. He co-leads the Pan-Cancer working groups for the Cancer Genome Atlas project and the International Cancer Genomics Consortium, co-directs the UCSC-Buck institute genome data analysis center, directs a big data resource for NCI to develop high-level information from CGHub raw DNA/RNA sequence data, and leads the pathway analysis for a prostate cancer Stand Up To Cancer and Prostate Cancer Foundation “Dream Team.”

Marloes Maathuis, Ph.D.

Professor, Department of Statistics, ETH Zurich, Switzerland, High-Dimensional Consistency in Score-Based and Hybrid Structure Learning ,” at 9:00 am on Friday, November 11, 2016, in Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum.

maathiusAbstract: The main approaches for learning Bayesian networks can be classified as constraint-based, score-based or hybrid methods. Although high-dimensional consistency results are available for the constraint-based PC algorithm, such results have been lacking for score-based and hybrid methods, and most hybrid methods are not even proved to be consistent in the classical setting where the number of variables remains fixed. We study the score-based Greedy Equivalence Search (GES) algorithm, as well as hybrid algorithms that are based on GES. We show that such hybrid algorithms can be made consistent in the classical setting by using an adaptive restriction on the search space. This leads to the adaptively restricted GES (ARGES) algorithm. Moreover, we prove consistency of GES and ARGES for certain sparse high-dimensional scenarios.   This is joint work with Preetam Nandy and Alain Hauser

Biography:  Dr. Maathuis’ research areas include causal inference, graphical models, high-dimensional statistics, asymptotics, and application of statistics.  She is currently associate editor for the Annals of Statistics, Biometrika, and the Scandinavian Journal of Statistics.

 


Computational Causal Discovery - Feb 2015

Clark Glymour , PhD, Alumni University Professor of Philosophy, Carnegie Mellon University“

Machine Learning for Predictive Phenotyping from EHR Data

C. David Page, PhD , Professor of Biostatistics & Medical Informatics, University of Wisconsin Madison

Clustering Heterogeneous Samples During Model Selection - April 2015

Kathleen Gates, PhD, Assistant Professor of Quantitative Psychology, University of North Carolina, Chapel Hill

In Search of a Common Scale for Causal Fusion in Neuroimaging - May 2015
Very High Dimensional Causal Structure and Markov Boundary Discovery: Key Algorithmic Developments and the Insights Gained about the R&D Process - Sept 2015

Constantin Aliferis, MD, PhD, FACMI, Director of the Institute for Health Informatics at the University of Minnesota

Integrative Causality Tests for Complex Diseases - Oct 2015
Algorithmic Methods in Computational and Systems Biology - Dec 2015

Teresa M. Przytycka, PhD, Senior Investigator, National Center for Biotechnology Information, National  Institutes of Health

Learning in Signaling Networks - Dec 2015

Karen Sachs, PhD , Research Scientist in the School of Medicine, Stanford University

General Purpose Satisfiability Solvers for Causal Discovery - Jan 2016

Frederick Eberhardt, PhD, Professor of Philosophy, Division of the Humanities and Social Sciences, Caltech

A Single Cell Approach to Network Rewiring - Feb 2016

Dana Pe’er, Ph.D., Associate Professor of Biological Sciences and Computer Science, Department of Systems Biology, Columbia University

Logic-Based Causal Discovery for Heterogeneous Datasets - Aug 2016

Ioannis Tsamardinos, PhD, Associate Professor, Department of Computer Science, University of Crete, Greece.

Learning Causal Effects: Bridging Instruments and Backdoors - Sept 2016

Ricardo Silva, Ph.D., Lecturer, Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London.