deepMiRGene: Deep Neural Network based Precursor microRNA Prediction

By Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon. 2016

50 citations recorded by the source corpus.

Since microRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation, miRNA identification is one of the most essential problems in computational biology. miRNAs are usually short in length ranging between 20 and 23 base pairs. It is thus often difficult to distinguish miRNA-encoding sequences from other non-coding RNAs and pseudo miRNAs that have a similar length, and most previous studies have recommended using precursor miRNAs instead of mature miRNAs for robust detection. A great number of conventional machine-learning-based classification methods have been proposed, but they often have the serious disadvantage of requiring manual feature engineering, and their performance is limited as well. In this paper, we propose a novel miRNA precursor prediction algorithm, deepMiRGene, based on recurrent neural networks, specifically long short-term memory networks. deepMiRGene automatically learns suitable features from the data themselves without manual feature engineering and constructs a model that can successfully reflect structural characteristics of precursor miRNAs. For the performance evaluation of our approach, we have employed several widely used evaluation metrics on three recent benchmark datasets and verified that deepMiRGene delivered comparable performance among the current state-of-the-art tools.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Two recently published tools use different recurrent neural network-based architectures to perform miRNA and target prediction with solely sequence data as input [185,186].”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, methodology): “L: 0.58, PFM: 0.77, PFM: 0.59; PBM: 0.58, EN: 0.71, and MDS: 0.64]) [98]. And LSTM outperformed SVM methods (LSTM: 0.93 vs. [Boost-SVM: 0.89, CSHMM: 0.65, SVM-LSS: 0.79, SVM-MFE: 0.86, and RBS: 0.8]) [135]. 1DREAM5 challenge: http://dreamchallenges.org/ and [121,194]. 13/32 Performance (%) Performan”
Deep learning in bioinformatics (2016, methodology): “Omics sequencing data (DNA-seq, RNA-seq, ChIP-seq, DNase-seq) features from genomic sequence position specific scoring matrix (PSSM) physicochemical properties (steric parameter, volume) Atchley factors (FAC) 1-dimensional structural properties contact map (distance of amino acid pairs in 3D structu”
Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review (2021, unknown): “Although not able to directly converse with users, DeepTarget [64] and deepMirGene [65] are capable of performing miRNA and target predictions using expression data with higher accuracy compared with non–deep learning models.”
Deep Learning in Mining Biological Data (2020, methodology): “In other words, RNN learns a distribution over classes for a sequence of inputs (e.g., x 1 , x 2 , . . . , x T ).”