Marco F Ramoni
Harvard Medical School - Massachusetts Institute of Technology
Network models are today extensively used to encode, extract, and process biomedical information. Thanks to over two decades of research in artificial intelligence, statistics and decision theory, methods abound to automatically extract network models from large databases. Over the past few years, network models have become particularly popular in genetic research, where they have been able to deliver amenable models of large databases generated by high-throughput techniques. This talk focuses on the use rather than the generation of these network models in genomics, proteomics, and clinical research. We will start by describing the use of these networks to develop predictive models for complex genetic traits, such as stroke and adult-onset asthma. We will then show how the topological analysis of these networks can be used to identify regulatory structures, such as feed back loops, that play a central role in the control of critical cellular processes, such as the cell cycle. We will then generalize these results into an information-theoretic framework to identify a broader class of topological features in a network and we will show how this framework can be used to identify targets and develop screening methods to measure the efficacy of anti-cancer compounds. Finally, we will describe how these networks can be used in clinical forecasting to predict the likelihood that a drug will receive FDA approval on the basis of early clinical trial data and we will show that that application of the model would reduce save an average of $283 million per successful new drug, and increase revenues by an average of $160 million per Phase III trial during the drug's first seven years on the market.
Bio Marco F Ramoni is Assistant Professor of Pediatrics and Medicine (Bioinformatics) at Harvard Medical School, Assistant Professor of Health Sciences and Technology at Harvard University-- Massachusetts Institute of Technology Division of Health Sciences and Technologies, and Assistant Professor of Oral Medicine, Infection and Immunity at the Harvard School of Dental Medicine. He is also the Director of the Biomedical Cybernetics Laboratory at the Harvard Partners Center for Genetics and Genomics, where he also serves as Associate Director of Bioinformatics, and the Director of the Training Fellowship in Biomedical Informatics at Children”Ēs Hospital Boston. A bioengineer by training, he has spent most of his career working at the intersection between Artificial Intelligence and Statistics. His research interests focus on the development of novel analysis methods for genomic, gene expression and proteomic data and their application to the identification of genomic markers of complex diseases including asthma, cancer, and stroke and the discovery of genetic drug targets and pharmacogenomic screening of compounds.