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Poster
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs
Seunghyun Park · Seonwoo Min · Hyun-Soo Choi · Sungroh Yoon

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #75 #None

MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) which play key roles in post-transcriptional gene regulation. Direct identification of mature miRNAs is infeasible due to their short lengths, and researchers instead aim at identifying precursor miRNAs (pre-miRNAs). Many of the known pre-miRNAs have distinctive stem-loop secondary structure, and structure-based filtering is usually the first step to predict the possibility of a given sequence being a pre-miRNA. To identify new pre-miRNAs that often have non-canonical structure, however, we need to consider additional features other than structure. To obtain such additional characteristics, existing computational methods rely on manual feature extraction, which inevitably limits the efficiency, robustness, and generalization of computational identification. To address the limitations of existing approaches, we propose a pre-miRNA identification method that incorporates (1) a deep recurrent neural network (RNN) for automated feature learning and classification, (2) multimodal architecture for seamless integration of prior knowledge (secondary structure), (3) an attention mechanism for improving long-term dependence modeling, and (4) an RNN-based class activation mapping for highlighting the learned representations that can contrast pre-miRNAs and non-pre-miRNAs. In our experiments with recent benchmarks, the proposed approach outperformed the compared state-of-the-art alternatives in terms of various performance metrics.

Author Information

Seunghyun Park (NAVER Corp.)

Clova AI in NAVER Corp.

Seonwoo Min (Seoul National University)
Hyun-Soo Choi (Seoul Nation University)
Sungroh Yoon (Seoul National University)

Dr. Sungroh Yoon is Associate Professor of Electrical and Computer Engineering at Seoul National University, Korea. Prof. Yoon received the B.S. degree from Seoul National University, South Korea, and the M.S. and Ph.D. degrees from Stanford University, CA, respectively, all in electrical engineering. He held research positions with Stanford University, CA, Intel Corporation, Santa Clara, CA, and Synopsys, Inc., Mountain View, CA. He was an Assistant Professor with the School of Electrical Engineering, Korea University, from 2007 to 2012. He is currently an Associate Professor with the Department of Electrical and Computer Engineering, Seoul National University, South Korea. Prof. Yoon is the recipient of 2013 IEEE/IEIE Joint Award for Young IT Engineers. His research interests include deep learning, machine learning, data-driven artificial intelligence, and large-scale applications including biomedicine.

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