Very Large Scale ReliefF for Genome-Wide Association Analysis
Computational Intelligence in Bioinformatics and Computational Biology, 2008. CIBCB'08. IEEE Symposium on, , 112-119, 2008
Abstract: The genetic causes of many monogenic diseases have already been discovered. However, most common diseases are actually the result of complex nonlinear interactions between multiple genetic and environmental components. There is thus a pressing need for new computational methods capable of detecting nonlinearly interacting single nucleotide polymorphism (SNPs) that are associated with disease, from amidst up to hundreds of thousands of candidate SNPs. Recently, some progress has been made using feature selection algorithms based on weights from the ReliefF data mining algorithm on sets of up to 1500 SNPs. However, the accuracy of ReliefF does not scale up to the sizes needed for truly large genome-scale SNP association studies. We propose a population-based variant dubbed VLSReliefF, which mitigates this performance drop by stochastically applying ReliefF to SNP subsets, and then assigning each SNP the maximum ReliefF weight it achieved in any subset. A heuristic method is proposed for determining the optimal subset size as a function of heritability, sample size, and order of interactions. The method is validated using a variety of computational experiments on synthetic datasets of up to 100,000 SNPs.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).