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Multi-modal Data Integration and Causal Inference in Systems Medicine

 

The advancement of technologies for high-throughput collection of personal data, including lifestyle, clinical and biomedical data, has inadvertently transformed biology and medicine. Integrating and co-analyzing these different data streams has become the research bottleneck and, in all likelihood, will be a central research topic for the next decade. My group has historically worked on the development of statistical and computational methods to identify key molecules (genes, microRNAs, etc) that affect disease onset and progression. More recently, we became interested in how we can combine the power of genomics with the rich medical data that are available. In this talk, I will present some of our recent efforts on causal modeling over mixed data types (continuous and discrete variables) and how to apply them to address important biomedical and clinical questions in chronic diseases and cancer diagnosis and therapy.

Originally published on December 9, 2020

Lecture Presenter

Takis Benos, PhD

Takis Benos, PhD

Professor and Vice Chair, Department of Computational and Systems Biology
University of Pittsburgh School of Medicine

Takis Benos received his undergraduate degree in Mathematics but later he was attracted to Molecular Biology. He earned a PhD degree for his work on the molecular cloning and phylogenetic characterization of alcohol dehydrogenase genes in Diptera. His post-graduate work included genome analysis and annotation of Drosophila melanogaster with Michael Ashburner (EMBL-EBI, Cambridge, U.K.) and the development of probabilistic algorithms for modelling protein-DNA interactions with Gary Stormo (Washington University in St. Louis, St. Louis, MO). In 2002 he joined University of Pittsburgh and he is currently a tenured Full Professor and Vice Chair for Academic Affairs at the Department of Computational and Systems Biology (primary appointment), the Department of Biomedical Informatics (secondary appointment), School of Medicine and the Department of Computer Science (secondary appointment), Faculty of Arts and Sciences, University of Pittsburgh. He also holds a joint appointment at the University of Pittsburgh Cancer Institute (UPCI).

Dr. Benos’ research interests are in the area of computational biology and in particular in the integration of –omics data with clinical variables and the study of gene regulatory networks and their association to disease. In the past, his group has developed models of transcription factor interactions to DNA and miRNA interactions to mRNAs and algorithms to identify subnetwork motifs associated to chronic diseases. His work has been published in peer-reviewed journals such as Nature, Science, PNAS, PLoS Computational Biology, Genome Research, Genome Biology, etc. His work has been presented in many international conferences and he has been invited to give talks in many Universities in United States and Europe. In April 2014, he co-organized and co-chaired the RECOMB 2014 conference in Pittsburgh, which is one of the three largest computational biology conferences. Dr. Benos has been continuously funded through his own NIH and NSF grants since 2003 and he has established a large number of collaborations.

Objectives

After this presentation, participants will be able to:

  • Describe the advantages and disadvantages of correlations, regression analyses, and machine learning
  • Discuss the probabilistic graphical models (PGMs) approach
  • Demonstrate the utility of multi-modal data integration using examples of lung cancer detection, PARP1 SNP rs1805401 in cancer patients, and laboratory results for COPD patients

Sponsored by:

University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories