Parallel Machine Learning Algorithms In Bioinformatics And Global Optimization
This is a dissertation in three parts, in each we explore the development and analysis of a parallel statistical or machine learning algorithm and its implementation. First, we examine the Assembly Likelihood Evaluation (ALE) framework. This algorithm defines a rigorous statistical likelihood metric used to validate and score genome and metagenome assemblies. This algorithm can be used to identify specific errors within assemblies and their locations; enable comparison between assemblies allowing for optimization of the assembly process; and using re-sequencing data, detect structural variations. Second, we develop an algorithm for Expected Parallel Improvement (EPI). This optimization method allows us to optimally sample many points concurrently from an expensive to evaluate and unknown function. Instead of sampling sequentially, which can be inefficient when the available resources allow for simultaneous evaluation, EPI identifies the best set of points to sample next, allowing multiple samplings to be performed in unison. Finally, we explore Velvetrope: a parallel, bitwise algorithm for finding homologous regions within sequences. This algorithm employs a two-part filter between sequences. It first finds offsets where two sequences share a higher than expected amount of identity. It then filters areas within these offsets with higher than expected identity. The resulting positions along each sequence represent regions of statistically significant similarity.
bioinformatics; parallel algorithms; machine learning
Selman, Bart; Strogatz, Steven H
Ph.D. of Applied Mathematics
Doctor of Philosophy
dissertation or thesis