Machine Learning for Classification and Genome Annotations

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This proposal entitled "Machine Learning for Classification and Genome Annotations" builds on my previous research program funded by NSERC entitled "Computational Intelligence and Applications in Bioinformatics". It has 2 components: 1) Self-adjusting Evolutionary Neural Networks and Applications in Computational Biology Many problems in Computational Biology involve classification, such as classifying cell samples into malignant (cancer) or benign (normal). For such tasks, Neural Networks can be developed and employed. Neural Networks offer a great general framework for machine learning, including classification, image segmentation and understanding. While the neural network field is currently a hot research area, with many successes in image processing and computer vision, relatively little attention has been paid to the learning of activation functions for individual layers or nodes. For this purpose, we propose EvoDNN, an evolutionary deep neural network that employs an evolutionary algorithm to evolve deep heterogeneous feed-forward neural networks. While the majority of current feed-forward neural networks employ user defined homogeneous activation functions, EvoDNN creates heterogeneous multi-layer networks where each neuron's activation function is not statically defined by the user, but dynamically optimized during evolution. One advantage offered by EvoDNN is that the activation functions do not need to be differentiable. This feature gives users a great degree of flexibility over which activation functions to employ. 2) Machine Learning Methods for Semi Automated Genome Annotation (SAGA) A second topic of this proposal is related to machine learning in the area of genome annotation. Semi automated genome annotation (SAGA) methods are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing based assays of epigenomic activity (such as ChIP-seq measurements of histone modification and transcription factor binding), and output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm, that uses a nonnegative Kalman filter state space model to efficiently annotate the genome with chromatin state features. We intend to show that chromatin state features from epigenome-ssm are more useful for several downstream applications than both continuous and discrete alternatives.