Biological sequence modeling using Deep Learning is coming at 10/10/2016 - 4:00pm
DEAR 118 Mon, 10/10/2016 - 4:00pm David Hendrix Abstract: The rapid growth in the number of sequenced genomes and available transcriptomics data demands new solutions that can scale to this magnitude and beyond. There is evidence that long noncoding RNAs outnumber protein-coding genes, but at the same time more and more presumed noncoding transcripts are found to harbor short open reading frames that confound current computational coding potential evaluation tools. Similarly, the analysis of gene expression data often focuses on the most differentially expressed genes, which may not identify subtle patterns. Although these questions have been well studied, current models are buckling under the weight of the growing annotation data. These challenges can be addressed through the development of high-order, multi-layered, deep learning algorithms. We present a recurrent neural network model for transcripts and for the detection of coding signals. We also present a Stacked Denoising Autoencoder for the analysis of gene expression data. Bio: [node:field-speaker-bio:text] Read more: http://eecs.oregonstate.edu/colloquium/biological-sequence-modeling-usin... [1] [1] http://eecs.oregonstate.edu/colloquium/biological-sequence-modeling-using-deep-learning
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