KDD2013

by Shobeir Fakhraei, Louiqa Raschid and Lise Getoor

by Sabeur Aridhi, Haitham Sghaier, Mondher Maddouri and Engelbert Mephu Nguifo

The causal chain of events that lead to the development of complex diseases such as schizophrenia remains elusive. Such diseases are complex, resulting from the interplay of potentially hundreds (or thousands) of genetic loci and environmental factors. Genetic and environmental perturbations induce changes in the molecular interactions of cellular pathways whose collective effect may become clear through the organized structure of multiscale biological networks. We have developed a novel systems approach to study psychiatric disorders such as schizophrenia that models the global molecular, functional, and structural changes in the affected brain that in turn can lead us to the root causes of the disease. To characterize the molecular, cellular, and physiological systems associated with common human diseases, we constructed gene regulatory networks, functional and structural MRI based networks, high-content phenotypic networks and then integrated these network models across all of the data modalities generated across multiple human cohorts comprised of several thousand individuals. Because DNA variation was systematically assessed across all cohorts, it provides a common set of perturbations that can be leveraged to not only infer causal relationships among different molecular and higher order traits, but that can help link networks at different scales (e.g., molecular and imaging) across cohorts. Through this integrative network-based approach, we rank-order the resulting network structures for relevance to different diseases, highlighting both known and novel biological pathways involved in disease pathogenesis and progression. We demonstrate that the causal network structures we construct from this big data integration exercise is a useful predictor of response to gene perturbations and presents a novel framework to test models of disease mechanisms underlying disease. We further demonstrate that our approach can offer novel insights for drug discovery programs aimed at treating disease by screening our disease-associated networks against molecular signatures induced by marketed and novel compounds across a number of cell-bases systems, including those derived from stem cells isolated from patients with disease.

by  : Raed I. Seetan, Anne M. Denton, Omar Al-Azzam, Ajay Kumar, M. Javed Iqbal and Shahryar F. Kianian

Prof. Predrag Radivojac, Indiana University will deliver a talk titled State-of-the-art in protein function prediction. His summary of the talk: In this talk I will first provide the significance and computational problem formulation of protein function prediction. I will then present details of the first Critical Assessment of Functional Annotation (CAFA) experiment, where we evaluated state-of-the-art in the field. We provided evidence that modern methods significantly outperform simple BLAST alignments but that there is significant need and room for improvement. I will lay out possible avenues for improvements and accuracy assessment of function prediction proposed by my research group. Finally, I will briefly discuss the CAFA 2013-2014 challenge whose start is anticipated for Summer 2013.

Ananth Grama, Purdue University will deliver a talk titled Systems Biology of Cellular Aging and Age-Related Degeneracies. His summary of the talk: Cellular aging is a multi-factorial complex phenotype, characterized by the accumulation of damaged cellular components over the organism's life-span. The progression of aging depends on both the increasing rate of damage to DNA, RNA, proteins, and cellular organelles, as well as the gradual decline of the cellular defense mechanisms against stress. This can ultimately lead to a dysfunctional cell, with a higher risk factor for a number of diseases, including cancers, cardiovascular disease, and multiple neurodegenerative disorders. With a view to uncovering the pathways associated with aging, and their role in age-related degeneracies, we have developed a number of algorithms and statistical models that integrate and analyze disparate data over human and yeast interactomes. In this talk, we present two recent results: (i) we demonstrate the use of directed random walks in uncovering the downstream effectors of Target of Rapamycin (TOR), a highly conserved protein kinase that plays a key role in the aging process of various organisms; and (ii) we build tissue-specific networks for human cells and develop a complete framework for projecting these tissue-specific networks on to the yeast interactome. The goals of this effort are many-fold -- strong alignments indicate tissues for which yeast is a good model organism (in terms of underlying biochemistry), alignments reveal specific pathways that are well conserved, and they serve as a first step in understanding the etiology of age-related degeneracies.