Disease Modeling via Large Scale Network Analysis Report Title
Disease Modeling via Large Scale Network Analysis Report Title A central goal of genetics is to learn how the genotype of an organism determines its phenotype. We address the implicit problem of predicting the association of genes with phenotypes or traits. Our primary goal is to develop pragmatic data analytic methods for linking specific genes to traits and diseases, especially polygenic traits, which are the most challenging. We are also interested in developing theoretical guarantees for the methods. In the past, we have developed predictive methods general enough to apply to potentially any genetic trait, varying from plant traits relevant to desirable agricultural properties to important human diseases. Our methods, Katz on heterogeneous network and CATAPULT[1] for predicting gene disease associations were published during the last project period in the PLOS One journal. The biological problem has also led us to pursue a significant problem in machine learning. One of the fundamental questions in machine learning relating to the classification problem is if we can efficiently learn classifiers that can provably achieve low misclassification rates in the presence of certain type of random label noise in the training data. We have answered the question in affirmative in our recent paper[2] and in particular, developed important theoretical results and robust algorithms for dealing with random label noise in classification. Our CATAPULT system employs "biased" support vector machines (SVM) to cope with the lack of negative examples. Biased SVMs have been empirically shown to be successful for similar tasks, but there has not been any theoretical study previously. In the last project period, we were able to show that they exhibit a certain noise tolerance property, a novel result in machine learning. We have also developed a novel matrix completion method called Inductive Matrix Completion to the problem of predicting genedisease associations [3] that combines multiple types of features for diseases and genes for discovering new gene disease associations. While the primary objective is the biological problem, the mathematical models and techniques developed for the problem are expected to shed light on some open problems related to PU(Positive Unlabeled) learning, such as (a) multiple sources in PU learning, (b) multi task PU learning, and (c) theoretical guarantees for PU learning algorithms. (a) Papers published in peer reviewed journals (N/A for none) Enter List of papers submitted or published that acknowledge ARO support from the start of the project to the date of this printing. List the papers, including journal references, in the following categories: