SG-LSTM-FRAME: a computational frame using sequence and geometrical information via LSTM to predict miRNA–gene associations
Abstract
Motivation MircroRNAs (miRNAs) regulate target genes and are responsible for lethal diseases such as cancers. Accurately recognizing and identifying miRNA and gene pairs could be helpful in deciphering the mechanism by which miRNA affects and regulates the development of cancers. Embedding methods and deep learning methods have shown their excellent performance in traditional classification tasks in many scenarios. But not so many attempts have...
Paper Details
Title
SG-LSTM-FRAME: a computational frame using sequence and geometrical information via LSTM to predict miRNA–gene associations
Published Date
Mar 16, 2020
Journal
Volume
22
Issue
2
Pages
2032 - 2042
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Notes
History