"Understanding Evolution through Massive Data and Computational Method Development"

Abstract: Evolution is the framework within biological research is best addressed. In this talk, I will describe how advances in computer science and statistics, combined with the increasing availability of genomic data, have the potential to lead to dramatic advances in biological understanding.

Bio: Tandy Warnow (PhD Berkeley) is the Founder Professor and Associate Head of Computer Science at the University of Illinois at Urbana-Champaign, where she is also an affiliate in Mathematics, Statistics, Bioengineering, Electrical and Computer Engineering, and three biology departments. Her research combines computer science, statistics, and discrete mathematics, focusing on developing improved models and algorithms for reconstructing complex and large-scale evolutionary histories in biology and historical linguistics. Her awards include the NSF Young Investigator Award (1994), the David and Lucile Packard Foundation Award (1996), a Radcliffe Institute Fellowship (2003), and the John Simon Guggenheim Foundation Fellowship (2011). She was elected a Fellow of the Association for Computing Machinery (ACM) in 2015 and of the International Society for Computational Biology (ISCB) in 2017.

"A Data-driven Journey in Macromolecular Structure, Dynamics, and Function"

Abstract: The pioneering work of Sir Alan Turing in his 1952 paper “The chemical basis of morphogenesis” led to decades of scientific enquiry demonstrating just how fundamental form and changes to form are to function and function modulation, whether in understanding phase transitions, complex networks, or the molecular basis of cellular processes. In this talk, I will summarize computational research in my laboratory on modeling form, changes to form, and its role in macromolecular (dys)function. I will present discriminative and generative models that harness the growing volume of heterogeneous biological data, as well as scientific knowledge in molecular biology and biophysics, to predict molecular function from sequence or structure, to build, visualize, and summarize increasingly-detailed representations of molecular energy landscapes and equilibrium structural dynamics, and even discover and categorize mechanisms via which DNA mutations alter protein dynamics and function in various human disorders.

Bio: Amarda Shehu (PhD Rice University) is a Professor in the Department of Computer Science in the Volgenau School of Engineering with affiliated appointments in the Department of Bioengineering and School of Systems Biology at George Mason University. She is also Co-Director of the Center for Advancing Human-Machine Partnerships (CAHMP), a Transdisciplinary Center for Advanced Study at George Mason University. Shehu's research bridges computer and information science, engineering, and the life sciences. Her laboratory has made many contributions in bioinformatics and computational biology elucidating the relationship between macromolecular sequence, structure, dynamics, and function. Shehu is the recipient of an NSF CAREER award, and her research is regularly supported by various NSF programs, including Information Integration and Informatics, Robust Intelligence, Computing Core Foundations, and Software Infrastructure, as well as various state and private research awards. Shehu is also the recipient of the Mason University Teaching Excellence Award, the Mason Emerging Researcher/Scholar/Creator Award, and the Mason OSCAR Undergraduate Mentor Excellence Award. Shehu currently serves as Program Director in the Information Integration and Informatics Program at the National Science Foundation.

"Integrative Text Mining and Semantic Computing for Data-Driven Biomedical Knowledge Discovery"

Abstract: In this talk, Dr. Wu will cover research in integrative literature mining, data mining and semantic computing for knowledge discovery. To realize the value of genomic scale data, her team has developed a semantic computing framework connecting text mining, data mining and biomedical ontologies. Natural language processing and machine learning approaches are employed for information extraction from the literature, along with an automated workflow for large-scale text analytics across documents. The ontological framework allows computational reasoning, and through federated SPARQL queries, it connects complex entities and relations such as gene variants, protein post-translational modifications and diseases mined from heterogeneous knowledge sources. Scientific use cases demonstrate data-driven discovery of gene-disease-drug relationships that may facilitate disease understanding and drug target identification for diseases ranging from Alzheimer's’ to COVID-19.

Bio: Dr. Cathy Wu is the Unidel Edward G. Jefferson Chair in Engineering and Computer Science at the University of Delaware. She has conducted bioinformatics research for 25 years and has led/co-led major bioinformatics resources including the international UniProt Consortium. Recognized as a “Highly Cited Researcher” (top 1%) for six consecutive years, she has published more than 270 peer-reviewed papers with an h-index of 65. Dr. Wu serves as the founding director of the Center for Bioinformatics and Computational Biology and the Data Science Institute at UD, a nucleating effort to catalyze interdisciplinary research and address big data problems across fields impacting our society.

"Towards real-time computational epidemiology"

Abstract: COVID-19 pandemic represents an unprecedented global crisis. Its global economic, social and health is already staggering and will continue to grow. Computation and, more broadly, computational thinking plays a multi-faceted role in supporting global real-time epidemic science especially because controlled experiments are impossible in epidemiology. High performance computing, data science and new sources of massive amounts of data from device-mediated interactions have created unprecedented opportunities to prevent, detect and respond to pandemics.

In this talk, using COVID-19 as an exemplar, I will describe how scalable computing, AI and data science can play an important role in advancing real-time epidemic science.

Bio: Madhav Marathe a Distinguished Professor in Biocomplexity, the division director of the Networks, Simulation Science and Advanced Computing Division at the Biocomplexity Institute and Initiative, and a Professor in the Department of Computer Science at the University of Virginia. His research interests are in network science, computational epidemiology, AI, foundations of computing, socially coupled system science and high performance computing. Before joining UVA, he held positions at Virginia Tech and Los Alamos National Laboratory. He is a Fellow of the IEEE, ACM, SIAM and AAAS.

"Modeling and learning how cancer cells respond differently to oxidative stress"

Abstract: One area of translational biology with strong potential for knowledge-guided machine learning is cancer. We know that cancer is caused by gene mutations and that these mutations lead to changes in gene expression and protein activity. Further, the cancer community has collected data on mutations and gene-expression signatures for tens of thousands of cancer cases. In my talk, I will discuss ways in which these data can be used to amplify the scope of physical models that encode specific chemical-reaction mechanisms within a cancer cell. We have built a model for how cells detoxify the oxidative stresses that cancer cells accumulate as a result of uncontrolled proliferation and applied this model to patient-specific molecular profiles from The Cancer Genome Atlas (Science Signaling 13:eaba4200 [2020]). The complexity of model simulations is such that it creates the need to consider machine-learning frameworks that can interpretably relate cancer-specific gene-expression patterns to the model simulation results they give rise to.

Bio: Kevin Janes a professor in the Departments of Biomedical Engineering and Biochemistry & Molecular Genetics at the University of Virginia. Dr. Janes has been recognized as a Pew Scholar, a Packard Fellow, a Kavli Fellow, and a recipient of the NIH Director’s New Innovator Award. He is a member of the Board of Reviewing Editors for Science Signaling and a member of the Editorial Boards for Biophysical Journal and Cell Systems. He serves as a member of the Executive–Steering Committees for two NIH training grants in Cancer and Biomedical Data Sciences and is the acting co-Director of NCI Cancer Systems Biology Consortium. He was elected as a Fellow of the American Institute of Medical and Biological Engineering in 2020.

"Meta Learning for Cancer Prediction"

Abstract: Survival analysis, which predicts the time to event (e.g., stage conversion), provides a solution to handle the right censored data. The main challenge is the existence of censored data, in which the events of stage conversion are not observed. To handle censored data, traditional survival models most widely used in literature include Kaplan-Meier (KM) estimator and parametric censored regression models. Recently, multi-task survival model has been proposed to model specific discrete time prediction, without the above assumptions.

Some recent approaches apply deep neural network into well-studied statistical models to improve feature extraction, while almost all the models still need to assume some distribution functions. However, the methods may give sub-optimal results when the underlying assumptions are violated. In this talk, I will discuss the potential of applying a deep learning model to directly learn a survival distribution from data, which can better estimate the survival behaviour of patients.

Bio: Aidong Zhang is a William Wulf Faculty Fellow and Professor of Computer Science and Biomedical Engineering in the School of Engineering and Applied Sciences at University of Virginia. She is also affiliated with the Data Science Institute at University of Virginia. Her research interests focus on data mining, machine learning, bioinformatics and health informatics.