Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/155091
Title: RNA alternative splicing prediction with discrete compositional energy network
Authors: Chan, Alvin
Korsakova, Anna
Ong, Yew-Soon
Winnerdy, Fernaldo Richtia
Lim, Kah Wai
Phan, Anh Tuan
Keywords: Computer and Information Science
Medicine, Health and Life Sciences
Issue Date: 2021
Source: Chan, A., Korsakova, A., Ong, Y., Winnerdy, F. R., Lim, K. W. & Phan, A. T. (2021). RNA alternative splicing prediction with discrete compositional energy network. Proceedings of the Conference on Health, Inference, and Learning (CHIL '21), 193-203. https://dx.doi.org/10.1145/3450439.3451857
Project: NRFNRFI2017-09
Abstract: A single gene can encode for different protein versions through a process called alternative splicing. Since proteins play major roles in cellular functions, aberrant splicing profiles can result in a variety of diseases, including cancers. Alternative splicing is determined by the gene's primary sequence and other regulatory factors such as RNA-binding protein levels. With these as input, we formulate the prediction of RNA splicing as a regression task and build a new training dataset (CAPD) to benchmark learned models. We propose discrete compositional energy network (DCEN) which leverages the hierarchical relationships between splice sites, junctions and transcripts to approach this task. In the case of alternative splicing prediction, DCEN models mRNA transcript probabilities through its constituent splice junctions' energy values. These transcript probabilities are subsequently mapped to relative abundance values of key nucleotides and trained with ground-truth experimental measurements. Through our experiments on CAPD1, we show that DCEN outperforms baselines and ablation variants.
URI: https://hdl.handle.net/10356/155091
ISBN: 9781450383592
DOI: 10.1145/3450439.3451857
DOI (Related Dataset): 10.21979/N9/FFN0XH
Rights: © 2021 Copyright held by the owner/author(s). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Conference Papers

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