deepTarget: End-to-end Learning Framework for microRNA Target Prediction using Deep Recurrent Neural Networks

By Byunghan Lee, Jung-un Baek, Seunghyun Park, Sungroh Yoon. 2016

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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, background): “presented a deep RNN framework for automatic miRNA target prediction [100].”
Deep Learning in Mining Biological Data (2020, methodology): “Lasagne supports CNN, RNN, and LSTM.”
Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications (2018, methodology): “This makes LSTM suitable for processing tasks entailing sequence processing, such as in case of speech recognition [23], [24], natural language processing [25], [26], -omics sciences [27], [28], automatic control [29], [30], and others.”
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 prediction using expression data with higher accuracy compared to non-deep learning models.”
Deep Learning and Its Applications in Biomedicine (2018, background): “As a result, they achieved 25% increase in F-measure compared to existing alternative methods [153,154].”
Deep learning in bioinformatics (2016, methodology): “[107] proposed a microRNA target prediction method, which exploits unsupervised pre-training with RNN based AE, and achieved a>25% increase in F-measure compared to the existing alternatives.”