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Full-Text Articles in Computational Biology
Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone
Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone
Complex Biosystems PhD Program: Dissertations
The task of gene prediction has been largely stagnant in algorithmic improvements compared to when algorithms were first developed for predicting genes thirty years ago. Rather than iteratively improving the underlying algorithms in gene prediction tools by utilizing better performing models, most current approaches update existing tools through incorporating increasing amounts of extrinsic data to improve gene prediction performance. The traditional method of predicting genes is done using Hidden Markov Models (HMMs). These HMMs are constrained by having strict assumptions made about the independence of genes that do not always hold true. To address this, a Convolutional Neural Network (CNN) …
Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott
Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott
Theses and Dissertations--Mathematics
Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley …