“Constructing Predictive Models of Human Diseases via the Integration of Panomic and Clinical Data to Better Diagnose and Treat Patients”
Emil Eric Schadt, Ph.D.
2011 Recipient, A. M. Turing Award
Post-lecture discussion panelists: Beth Simone Noveck, Director of The Governance Lab at NYU-Wagner, Ellen J. Scherl, M. D., Jill Roberts Professor of Clinical Medicine at the Sanford I. Weill College of Cornell University
Sixteenth Annual Lynford Lecture
Introductory Remarks and Panel Introduction by Jeffrey Lynford
March 12, 2015
Fellow Trustees, Provost McLaughlin, President Sreenivasan, Professors Chudnovsky, faculty, students and honored guests including former New York City Council Member The Honorable Eric Gioia, Executive Director of the Port Authority Patrick Foye, two visiting NYU-Abu Dhabi students, Coe Sibanda and Yilkal Abe, and my godson, Mark Sfreddo, a sophomore from John Jay High School in Katonah, NY: Since 1998 IMAS has convened a symposium annually to invite a leading scientist, engineer or thinker to explain his or her latest research on various topics from String theory to cyber-defense to financial markets behavior to genomics. Among our past lecturers, we have had the privilege of learning from three Nobel Laureates, two A.M. Turing Award recipients and one Fields Medalist. In 1999 the second Lynford Lecture was delivered by J. Craig Venter, a pioneer in DNA sequencing. At that time his mission was to decode the human genome by 2001!
As you know, today we are pleased to welcome Dr. Eric Schadt. He is a graduate of California Polytechnic State University, UC Davis and UCLA. His PhD research combined his interests in both molecular biology and biomathematics. In 2011 he founded the Icahn Institute for Genomics and Multi-scale Biology at Mount Sinai Hospital’s School of Medicine. His professional work now combines mathematics and computational biology and he is at the forefront of new ways to unlock the complexities of human health and biology.
This afternoon we are also joined by two distinguished interlocutors: Professor Beth Simone Noveck from NYU/Poly and Dr. Ellen J. Scherl from Weill Cornell Medical College. After Dr. Schadt’s lecture they will convene as a panel to ask Dr. Schadt questions about his remarks today. More on these panelists later.
What is Multi-scale Biology and how does this relate to NYU/Poly students? The simple answer can be found in just two words: “Big Data.” At NYU/Poly our students and faculty, including the Professors David and Gregory Chudnovsky, work to conceive the algorithms and to develop the electrical engineering techniques and technologies, that are utilized to uncover large hidden values from massive, diverse and complex data-sets.
Techniques and technologies that may be utilized by Dr. Schadt in his recent efforts at the Icahn Institute. Techniques and technologies that can assist in the generation and integration of biological, clinical, and environmental data to understand human diseases on a network level.
Dr. Schadt collects massive quantities of data to innovate new approaches for information-driven medicine. These innovations are being utilized to diagnose and treat patients afflicted with cancer, infectious diseases, Alzheimer’s and psychiatric disorders. It requires advanced computer models to process multiple layers of biological data, including gene expression, metabolite, DNA, and protein information. Then he combines this data with clinical data, predictive modeling, and probabilistic analysis to attempt to unlock the complex mechanisms of a disease.
In 2014 his Institute announced The Resilience Project. This is a plan to genotype up to 1 million people with the goal of identifying the rare biological mechanisms that keep people healthy when they have genetic variants that should cause disease. It will scan the genomes of healthy people age 30 and older who contribute their DNA to the effort with an initial focus on 127 diseases.
Now to conclude with several words of balance and caution about Big Data, inductive reasoning and privacy.
Inductive reasoning is the process of arriving at a conclusion based on a set of observations and this process almost always is the way we form ideas about things. Once those ideas form, then we can systematically determine whether our initial ideas were right, wrong, or somewhere in between.
Currently conventional scientific research is based on direct experimentation and the limiting factors are the relevant data that can confirm or deny an initial hypothesis. In the biosciences, a new theory has been postulated: that information provided by Big Data without a prior hypothesis is complementary to the previously established approaches based on experimentation. In this massive approach, it is the formulation of a relevant hypothesis to explain the data,that is the limiting factor. The search logic is reversed and the limits of induction must be considered. Perhaps Dr. Scherl will contrast for us her experiences in medical research with Dr. Schadt’s approaches.
Privacy advocates are concerned about the threat to privacy represented by increasing storage and integration of personally identifiable information; expert panels have released various policy recommendations to conform practice to expectations of privacy. Maybe Professor Noveck, as founder and head of the White House Open Government Initiative, can give us her thinking about how Big Data may conflict with privacy concerns around the world.
Now let’s welcome Dr. Schadt to the podium.