MD, PhD, FACP, FACMI, as President and Chief Executive Officer of American Medical Informatics Association
Title of the Keynote:
From Meaningful use to Precision Medicine: Lessons Learned
The meaningful use program, part of the US stimulus bill in 2009, accelerated the adoption of electronic medical records in the US, and set the stage for new payment models in the affordable care act. In this presentation, I will briefly introduce the basic framework of the meaningful use program, and then frame 5 lessons to learn from the experience. I'll then describe the short term challenges, and discuss future trends that will impact the work that we must do today.
Doug Fridsma, MD, PhD, FACP, FACMI, is the President and Chief Executive Officer of AMIA, a membership society representing 5000 professional and student informaticians and their interests and activities in academe, industry, government and nonprofit organizations.
Dr. Fridsma is an expert in informatics, interoperability, standards, and health IT (including meaningful use). His understanding of the science and application of informatics and experience as practitioner and policymaker give him a depth of knowledge well-suited to the critical challenge of transforming health and health care.
Prior to joining AMIA, Dr. Fridsma was the Chief Science Officer for the Office of the National Coordinator for Health Information Technology, responsible for the portfolio of technical resources needed to support the meaningful use program and health information technology interoperability. While at ONC, he developed the standards and interoperability framework to accelerate the development of technical specifications for interoperability, and in collaboration with the NIH and other federal agencies, was instrumental in establishing the key priorities in the PCOR Trust fund. Prior to ONC, Dr. Fridsma held academic appointments at the University of Pittsburgh, Arizona State University, University of Arizona, and Mayo Clinic, and had a part-time clinical practice at the Mayo Clinic Scottsdale. He has served as a board member of HL7 and the Clinical Data Interchange Standards Consortium (CDISC) where he was instrumental in developing standards that bridge clinical care and clinical research.
About AMIAAMIA, the leading professional association for informatics professionals, is the center of action for 5,000 informatics professionals from more than 65 countries. As the voice of the nation’s top biomedical and health informatics professionals, AMIA and its members play a leading role in assessing the effect of health innovations on health policy, and advancing the field of informatics. AMIA actively supports five domains in informatics: translational bioinformatics, clinical research informatics, clinical informatics, consumer health informatics, and public health informatics. More about the American Medical Informatics Association is online at amia.org. Follow @AMIAinformatics @AMIApolicy
More information can be found at https://www.amia.org/news-and-publications/press-release/amia-welcomes-douglas-b-fridsma-md-phd-new-president-and-ceo
Professor of Applied Math and Computer Science at MIT, and head of the Computation and Biology group at MIT's Computer Science and AI Lab
Title of the Keynote:
Computational biology in the 21st century: Algorithms that scale
The last two decades have seen an exponential increase in genomic and biomedical data, which will soon outstrip advances in computing power. Extracting new science from these massive datasets will require not only faster computers; it will require algorithms that scale sublinearly in the size of the datasets. We introduce a novel class of algorithms that are able to scale with the entropy and low fractal dimension of the dataset by taking advantage of the unique structure of massive biological data. These algorithms can be used to address large-scale challenges in genomics, metagenomics and chemogenomics.
Bonnie Berger, Professor of Applied Mathematics and Computer Science. Among the first algorithmic computer scientists to enter the field of computational molecular biology, making fundamental contributions to structural bioinformatics, viral shell assembly and mis-assembly, comparative genomics, protein networks, and theoretical models of protein folding. Played a major role in shaping these fields as a mentor to young researchers. Influenced many subareas of computational biology, largely via algorithmic insights; introduced probabilistic modeling to protein fold recognition; founded and developed conservation-based methods for comparative genomics; and solved a difficult theoretical problem central to the biophysics and protein folding communities. Showed how to globally align protein interaction networks using an eigenvalue formulation, and used these alignments to uncover functionally related proteins across model organisms. Recently invented "compressive genomics" and demonstrated that algorithms that compute directly on compressed data allow the analysis of genomic data sets to keep pace with data generation. Close collaborations with experimental biologists have guided her computational work and given it significant impact in the laboratory. Hundreds of biology labs rely on software she developed and made freely available. Member, American Academy of Arts and Sciences. Fellow, International Society for Computational Biology. Fellow, Association for Computing Machinery. Named one of Technology Review's inaugural 100 Young Innovators of 1999. In 2011, selected to deliver the Margaret Pittman Director's Lecture at the National Institutes of Health.
More information can be found at http://people.csail.mit.edu/bab/
Professor of Computer Science at the Università di Milano-Bicocca
Title of the Keynote:
What is a gene? How and why computer science helps in answering this Question?
Large-scale genomic and trascriptomic analysis in eukaryotic genome projects have produced surprising results that have strongly challenged the traditional understanding of genes. In particular, the gene-centric view of molecular biology has been changed in favour of considering function products (proteins and ncRNAs) rather than genes.
This novel view is a consequence of some important findings obtained by a widespread use of software tools specifically developed to accomplish the important task of predicting the gene structure from large-scale gene products (i.e. cDNA, EST sequences and RNA-seq). These tools, combined with high-throughput transcriptomics, have allowed the exploration of the rich and variegate effects of alternative splicing, the basic operation that is used by eukaryotic cells to reorganize the information encoded by the genes.
Today, the understanding of alternative splicing is crucial to analyze gene function in both healthy and sick cells, towards the goals of discovering which function products are responsible of the onset of some important human diseases, and of reversing and contrasting the negative effects of those products.
In this talk, we will discuss the question posed in the title, mainly in the context of the massive production of next-generation sequencing data and their use in explaining the effects of alternative splicing on the gene structure. We will introduce some challenging computational problems and algorithmic solutions. We will show that some open problems involve the solution of crucial computational issues that are recurrent in various fields of computational biology such as de novo assembly, indexing and graph theory.
Paola Bonizzoni is full professor at the Department of Informatics, Systems and Communication (DISCo) at the University of Milano-Bicocca in Milan, Italy. She received a PhD. in Computer Science from the University of Milan. She is currently the head of AlgoLab (Experimental Algorithmics). Her research focuses mainly on computational problems in computational biology, both from a theoretical perspective (modelling, computational complexity, approximation and fixed-parameter solution) and an experimental point of view. She is also interested in combinatorial problems on sequences and graphs. In particular she has contributed to the development of ASPIc, a program for gene structure prediction due to alternative splicing that is used to generated a database of human isoforms annotation (ASPIcDB). Her research on modelling alternative splicing and transcripts predictions has been recently focused on next-generation sequencing technologies. Current research also includes the development of methods for haplotype assembly and phasing in structured populations, indexing of short reads and paired-ends and their use in the assembly of string graphs, graph theoretical problems related to transcriptomics and to the development of algorithms for reconstructing near perfect phylogenies.
More information can be found at http://algolab.eu/people/bonizzoni/