Abstract: Cancer development is a multi-step process that leads to uncontrolled tumor cell growth. Multiple signaling cascades are involved, some are activated while other pathways are suppressed. To fathom these processes, biomedical researchers use models of biological systems to integrate diverse types of information. This ranges from multiple high-throughput datasets and functional annotations to expert knowledge about biochemical reactions and biological pathways. Such integrative systems are used to develop new hypotheses, and answer complex questions in precision medicine such as what factors cause disease; which patients are at high risk; will patients respond to a given treatment; how to rationally select a combination therapy to individual patient, etc. Precision medicine needs to be data-driven, and corresponding analyses comprehensive and systematic. We will not find new treatments if only testing known targets and studying characterized pathways. Thousands of potentially important proteins remain poorly characterized. Computational biology methods, including machine learning, data mining and visualization, can help fill this gap with accurate predictions, making disease modeling more comprehensive. Intertwining computational prediction and modeling with biological experiments will lead to more useful findings faster and more economically. These computational predictions already significantly improved human interactome coverage relevant to both basic and cancer biology, and importantly, helped us to identify, validate and characterize prognostic signatures, and identify potential novel treatments. Combined, these results may lead to unraveling mechanism_of_action for therapeutics, re-positioning existing drugs for novel use and, prioritizing multiple candidates based on predicted toxicity, identifying groups of patients that may benefit from treatment and those where a given drug would be ineffective.

Bio:Igor Jurisica is Tier I Canada Research Chair in Integrative Cancer Informatics, Senior Scientist at Princess Margaret Cancer Centre, Prof at U Toronto and Visiting Scientist at IBM CAS. He is also an Adjunct Prof at the School of Computing and Pathology & Molecular Medicine at Queen's U, Computer Science at York U, and an Honorary Professor at Shanghai Jiao Tong University. Since 2015, he has also served as Chief Scientist at the Creative Destruction Lab, Rotman School of Management. His research focuses on integrative computational biology and the representation, analysis and visualization of high-dimensional data to identify prognostic and predictive signatures, drug mechanism_of_action and in-silico repurposing of drugs. Interests include prediction and analysis of protein interactions networks, modeling signaling cascades and high-throughput protein crystallography. He has published extensively on data mining, visualization and cancer informatics, including multiple papers in Science, Nature, Nature Medicine, Nature Methods, J Clinical Oncology, and has over 8,806 citations since 2010. He has been included in Thomson Reuters 2015 & 2014 list of Highly Cited Researchers, and The World's Most Influential Scientific Minds: 2014 Report.



Predictive Analytics in Complex Dynamic Networks
Zoran Obradovic
Temple University, USA

Abstract: Predictive modeling in complex networks is a challenging problem due to partially observed node attributes and links that often evolve over time. Additional challenges involve presence of multiple types of links among nodes that should be considered jointly where various nodes have different temporal dynamics. In this talk we will present an overview of the results of our ongoing big data project aimed to address some of these challenges by developing effective methods for structured regression with propagating uncertainty in evolving networks. The proposed methods will be discussed in context of applications to predicting admission and mortality rate for high impact diseases at a large number of hospitals.

Bio: Zoran Obradovic is an elected member of the Academia Europaea (the Academy of Europe), a L.H. Carnell Professor of Data Analytics at Temple University, Professor in the Department of Computer and Information Sciences with a secondary appointment in Statistics, and is the Director of the Data Analytics and Biomedical Informatics Center. He is the executive editor at the journal on Statistical Analysis and Data Mining, which is the official publication of the American Statistical Association and is an editorial board member at eleven journals. He is the chair at the SIAM Activity Group on Data Mining and Analytics and was co-chair for 2013 and 2014 SIAM International Conference on Data Mining and was the program or track chair at many data mining and biomedical informatics conferences. His work is published in more than 300 articles and is cited more than 15,000 times (H-index 48). For more details see http://www.dabi.temple.edu/~zoran/



Big Data Incremental Learning
Zhi-Hua Zhou
Nanjing University, China

Abstract: Traditional learning approaches usually try to collect all available data and then train a model. This becomes more and more infeasible in big data applications. Many popular loss functions in machine learning are non-linear, non-smooth and non-convex, leading to difficult optimization problems. With big data, the optimization becomes even more challenging because of the concerns of computational, storage, communication costs, etc., and it is desired to do incremental learning, without accessing the whole data. In this talk we will introduce some studies along this direction.

Bio: Zhi-Hua Zhou is a Professor and Founding Director of the LAMDA Group at Nanjing University. He authored the book "Ensemble Methods: Foundations and Algorithms", and published more than 100 papers in top-tier journals and conference proceedings. His work have received more than 18,000 citations, with a h-index of 66. He also holds 14 patents and has good experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, the IEEE CIS Outstanding Early Career Award, the Microsoft Professorship Award, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, Associate Editor-in-Chief of Science China, and on editorial boards for twelve journals. He is the founder of ACML (Asian Conference on Machine Learning) and chair of various conferences including the IJCAI 2015 machine learning track chair and ICDM 2015 Program chair. He is an ACM Distinguished Scientist, IEEE Fellow, IAPR Fellow and CCF Fellow.