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Michael T. Youmans
BME Ph.D. Proposal Presentation
Date: Thursday, May 11th 2017
Time: 10:00 AM
Location: Engineered Biosystems Building Room 3029
Advisor:
Dr. Peng Qiu
Thesis Committee:
Dr. Eberhard Voit
Dr. Zsolt Kira
Dr. Wendy Kelly
Dr. Jeffrey Skolnick
Dr. Robert Lee
Title: APPLICATION OF RECURRENT NEURAL NETWORKS AND SEQUENTIAL GENERATIVE NETWORKS TO THE IDENTIFICATION AND GENERATION OF ANTIBACTERIAL PEPTIDES
Abstract:
There is a growing need to deal with increasing rates of resistance to antibiotics among pathogenic bacteria. The development of resistance in bacteria to current antibiotics poses a global health hazard. Antibacterial peptides are an active area of current research that may aid in the development of new methods to deal with pathogenic bacteria. Machine learning strategies offer an efficient strategy to identify potential antibacterial peptide candidates that can accelerate what can be done through experimental testing alone. Many of the current machine learning methodologies employed to identify antibacterial peptides rely on constructing a finite length feature vector that is based on amino acid level features. Since peptides may contain different numbers of amino acids, it is not obvious how best to take amino acid features and turn them into a feature vector representing the entire peptide. Many methods for constructing such features search for periodic patterns in the amino acid level features and then use a scalar representing the strength of the periodic pattern to create feature for the whole peptide. This approach can be hit or miss as it is difficult to know which periodic patterns are relevant to the classification or regression task in advance. In this work recurrent neural networks that can take in variable length sequences of amino acid features and automatically extract a feature representation that is appropriate for the given machine learning task will be used. In addition various sequential generative models will be applied to the task of generating novel antibacterial peptides that are in theory drawn from the same distribution as the training data.
