Alternative splicing allows for gene products having various functions and increases protein diversity. The percent spliced in (PSI) indicates the efficiency of splicing a specific exon into the transcript population of a gene(1). In this project we attempt to predict the PSI of a set of exons using various gene regulators using a deep neural network and a shallow wide neural network. A better performance from neural networks is achieved thorough hyper parameter search which is capped by the computational power.
Title
Predicting Percent Spliced In (PSI) in Alternative Splicing Using Deep Networks
Published
2016-07-06
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2016-127
Type
Text
Extent
20 p
Archive
The Engineering Library
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