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Keynote Speeches


(1) Title: Obstacles and Options for Big-Data Applications in Biomedicine: The role of standards and normalizations

Christopher G. Chute, MD, DrPH

Professor of Medical Informatics,

Mayo Clinic College of Medicine, and IHI Fellow,

University of Minnesota, MN




Advances in computing capabilities are palpably evident throughout many industries manifest by
unprecedented, large-scale data integration and inferencing. Branded as “big-data” in many
cases, the question of whether such techniques can leverage advances in biomedicine and clinical
practice are obvious. High-throughput clinical analytics, synthesizing genomic and clinical
attributes of a particular patient, portends predictive models that can directly influence
clinical care decisions. However, to make this widely shared vision practical and scalable,
barriers attributable to data heterogeneity dominate. Methods and strategies to increase the
comparability and consistency of healthcare related data will be discussed.




Dr. Chute received his undergraduate and medical training at Brown University, internal medicine residency at Dartmouth, and doctoral training in Epidemiology at Harvard. He is Board Certified in Internal Medicine, and a Fellow of the American College of Physicians, the American College of Epidemiology, and the American College of Medical Informatics. He became founding Chair of Biomedical Informatics at Mayo in 1988, stepping down after 20 years in that role. He is now Professor of Medical Informatics and Section Head. He is PI on a large portfolio of research including the HHS/Office of the National Coordinator (ONC) SHARP (Strategic Health IT Advanced Research Projects) on Secondary EHR Data Use, the ONC Beacon Community (Co-PI), the LexGrid projects, Mayo’s CTSA Informatics, and several NIH grants including one of the eMERGE centers from NGHRI, which focus upon genome wide association studies against shared phenotypes derived from electronic medical records. Dr. Chute serves as Vice Chair of the Mayo Clinic Data Governance for Health Information Technology Standards, and on Mayo’s enterprise IT Oversight Committee. He is presently Chair, ISO Health Informatics Technical Committee (ISO TC215) and Chairs the World Health Organization (WHO) ICD-11 Revision. He also serves on the Health Information Technology Standards Committee for the Office of the National Coordinator in the US DHHS, and the HL7 Advisory Board. Recently held positions include Chair of the Biomedical Computing and Health Informatics study section at NIH, Chair of the Board of the HL7/FDA/NCI/CDISC BRIDG project, on the Board of the Clinical Data Interchange Standards Consortium (CDISC), ANSI Health Information Standards Technology Panel (HITSP) Board member, Chair of the US delegation to ISO TC215 for Health Informatics, Convener of Healthcare Concept Representation WG3 within the (TC215), Co-chair of the HL7 Vocabulary Committee, Chair of the International Medical Informatics Association (IMIA) WG6 on Medical Concept Representation, American Medical Informatics Association (AMIA) Board member, and multiple other NIH biomedical informatics study sections as chair or member.



(2)  Protein Structure Determination On Demand


Prof. Ming Li

   Canada Research Chair in Bioinformatics
School of Computer Science
University of Waterloo


Protein structure prediction by computers at best may serve as a screening method, and the current high-throughput protein structure determination methods are costly and will never exhaust all proteins. A complementary approach is "protein structure determination on demand", say in a week. We will discuss two approaches that would realize this goal: automatic protein structure determination using NMR data and mass spectrometry data.


Ming Li is a Canada Research Chair in Bioinformatics and a University Professor at the University of Waterloo. He is a fellow of the Royal Society of Canada, ACM, and IEEE. He is a recipient of E.W.R. Steacie Fellowship Award in 1996, the 2001 Killam Fellowship, and the 2010 Killam Prize. Together with Paul Vitanyi they have co-authored the book "An Introduction to Kolmogorov Complexity and Its Applications". He is a co-managing editor of Journal of Bioinformatics and Computational Biology.



(3) The CellOrganizer Project: An Open Source System to Learn Image-derived Models of Subcellular Organization over Time and Space


Prof.   Robert F. Murphy

                                  Lane Center for Computational Biology and Department of Biological Sciences

                                              Carnegie Mellon University, Pittsburgh, Pennsylvania, USA


                                              Faculty of Biology and Freiburg Institute for Advanced Studies

                                                  Albert Ludwig University of Freiburg, Freiburg, Germany




The CellOrganizer project (
http://cellorganizer.org) provides open source tools for learning generative models of cell organization directly from images and for synthesizing cell images (or other representations) from one or more of those models.  Model learning captures variation among cells in a collection of images. Images used for model learning and instances synthesized from models can be two- or three-dimensional static images or movies.  Current components of CellOrganizer can learn models of cell shape, nuclear shape, chromatin texture, vesicular organelle number, size, shape and position, and microtubule distribution.  These models can be conditional upon each other: for example, for a given synthesized cell instance, organelle position will be dependent upon the cell and nuclear shape of that instance.  The models can be parametric, in which a choice is made about an explicit form to represent a particular structure, or non-parametric, in which distributions are learned empirically.  One of the main uses of the system is in support of cell simulations: models learned from separate experiments can be combined into one or more synthetic cell instances that are output in a form compatible with cell simulation engines such as MCell, Virtual Cell and Smoldyn.  Another important application of the system is in comparison of target patterns and perturbagen effects in high content screening and analysis.  This is currently done using numerical features, but these are difficult to compare across different microscope systems or cell types since features can be affected by changes in more than one aspect of cell organization. More robust comparisons can be made using generative model parameters, since these can distinguish effects on cell size or shape from effects on organelle pattern. Ultimately, it is anticipated that collaborative efforts by many groups will enable creation of image-derived generative models that permit accurate modeling of cell behaviors, and that can be used to drive experimentation to improve them through active learning.



Robert F. Murphy is the Ray and Stephanie Lane Professor of Computational Biology and Professor of Biological Sciences, Biomedical Engineering, and Machine Learning at Carnegie Mellon University, and Director (Department Head) of the Lane Center for Computational Biology in the School of Computer Science.  He is also Honorary Professor of Biology at the Albert Ludwig University of Freiburg, Germany, a Fellow of the American Institute for Medical and Biological Engineering, and the recipient of an Alexander von Humboldt Foundation Senior Research Award. He is Past-President of the International Society for Advancement of Cytometry, and is a member of the National Advisory General Medical Sciences Council and the NIH Council of Councils.  He has published over 190 research papers in the areas of cell and computational biology.

Dr. Murphy’s career has centered on combining fluorescence-based cell measurement methods with quantitative and computational methods. In the mid 1990’s, his group pioneered the application of machine learning methods to high-resolution fluorescence microscope images depicting subcellular location patterns.  His current research interests include image-derived models of cell organization and active machine learning approaches to experimental biology.

(4) Protein 3D Structure from Genomic Sequences and Application to Cancer Genomics


Chris Sander

                                                                Director, Computational Biology Center

                                                               Memorial Sloan-Kettering Cancer Center

                                                                                   New York City



Amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold proteins, including transmembrane proteins. Addressing a fundamental challenge in computational molecular biology, a new prediction method (EVold) applies a maximum entropy approach to infer evolutionary couplings between sequence positions from correlated mutations in the multiple sequence alignment of a protein family. When translated to distance constraints, such residue-residue couplings are sufficient to generate good all-atom models of proteins from different fold classes, ranging in size from 50 to more than 300 residues. We use the technique to predict previously unknown 3D structures of large transmembrane proteins of biomedical interest, from their sequences alone. We show how the method can plausibly predict oligomerization, functional sites, and conformational changes in transmembrane proteins. Project co-leader: Debora Marks, Harvard Medical School; co-authors (alphabetical): Lucy Colwell, Thomas Hopf, Andrea Pagnani, Burkhard Rost, Robert Sheridan, Riccardo Zecchina. See http://bit.ly/tob48p (PDF) and www.evfold.org. The discovered evolutionary couplings provide insight into essential interactions constraining protein evolution and, with the rapid rise in large-scale sequencing, are likely to facilitate a comprehensive survey of the universe of protein structures by a combination computational and experimental technology. Applications to cancer genomics relate to the interpretation of the functional impact of cancer-related mutations and the design of targeted therapeutics.



Chris Sander is acknowledged as an initiating leader in the field of computational biology, an interdisciplinary field that aims to solve important problems in biology using techniques of mathematics, physics, engineering, and computer science. He is Head of the Computational Biology Center at Memorial Sloan Kettering Cancer Center and Tri-Institutional professor at Rockefeller and Cornell Universities.

Sander's current research interests are in computational genomics and systems biology, with a focus on network pharmacology and the development of targeted combinatorial therapy in cancer. His group uses the results of high-throughput sequencing to compute protein 3D structures and functional sites; and studies the regulation of gene expression by small RNAs. In 2012, he is active in the International Cancer Genomics Consortium, the NIH Cancer Genome Atlas Project, the NCI Integrative Cancer Biology Program and a leader in the bioPAX and PathwayCommons community
efforts to create an open-source information resource for biological pathways. He has published more than 250 peer-reviewed articles in physics and biology (http://bit.ly/Uk990K) with an h-index of 100.

Previously, Sander co-founded the research section of the European Bioinformatics Institute in Cambridge, England, and was founding chair of the department of Biocomputing at the European Molecular Biology Laboratory in Heidelberg. He is a Fellow of the International Society for Computational Biology.

Invited Talks

To be release soon (about 4 invited speakers)


Last update: 17 August 2012