Deep learning in bioinformatics

By Seonwoo Min, Byunghan Lee, Sungroh Yoon. 2016

1,486 citations recorded by the source corpus.

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.

Subfields: Molecular Biology

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Citations in the corpus

Financial Time Series Prediction Using Deep Learning (2017, methodology): “lly applied to a gamut of problems such as computer vision [16, 17], automatic speech recognition [18, 19], natural language processing [20, 21, 22], handwriting recognition [23], and bio-informatics [24], to name a few, outperforming contemporary state-ofthe-art schemes. Yet, the use of DL in ﬁnanc”
NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018, background): “Since then, deep learning techniques have affected the application of machine learning in biology expanding the ability of prediction tools to produce more accurate results on complex datasets (11-18).”
NSML: Meet the MLaaS platform with a real-world case study (2018, background): “(Yamada et al., 2018; Xiong et al., 2018; Liang et al., 2018; Yu et al., 2018; Min et al., 2017) As the applications of the deep learning grow, the complexity of the tasks for designing and tuning the neural network architectures increases as well as the size of models.”
Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment (2019, positive): “…(see for examples 16-19 ) is being exploited much more efficiently thanks to new developments in Artificial Intelligence Deep Learning techniques, 20,21 enabling the prediction of the 3D structure of proteins from sequence information alone, as most recently demonstrated in the CASP13 ab-initio…”
A Review on Human Activity Recognition Using Vision-Based Method (2017, background): “Basically, the deep learning architectures can be categorized into four groups, namely deep neural networks (DNNs), convolutional neural networks (ConvNets or CNNs), recurrent neural networks (RNNs), and some emergent architectures [110].”
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets. (2017, methodology): “Deep Learning has been relatively widely used for bioinformatics 36 and computational biology 37 .”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “Undoubtedly, research in deep learning applications and methods is expected to grow, especially in in view of documented advances across the spectrum of healthcare data, including EHR [260], genomic [261], [262], physiological parameters [263], and natural language data processing [264].”
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX) (2018, methodology): “Recently, we introduced k-mer-based embedding of biological sequences using skip-gram neural network and negative sampling [5], which became popular for protein feature extraction and has been extended for various classifications of biological sequences [37, 38, 39, 40, 41, 42, 43].”
Deep Learning in Spiking Neural Networks (2018, neutral): “biomedicine and bioinformatics [10], [11], temporal data”
Capturing Car-Following Behaviors by Deep Learning (2018, methodology): “Deep learning has been applied to various fields (including computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics) [36]–[38], where they have been shown”
Deep-ECG: Convolutional Neural Networks for ECG biometric recognition (2019, background): “Only a few studies have considered the of deep learning strategies for ECG analysis, on the classiﬁcation of heartbeats in healthy non-healthy [30] using techniques such as CNNs [24, autoencoders [34], or deep belief networks [18]. of our knowledge, only the work in [11] uses a deep technique for…”
Machine learning in critical care: state-of-the-art and a sepsis case study (2018, background): “These fields include bioinformatics [9, 10], genetics and genomics [11, 12], clinical applications [13], medical decision support and clinical diagnosis [14, 15], oncology [16, 17], psychiatry and neurological disorders [18, 19], or cytopathology [20].”
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification (2018, background): “Deep learning has been explored as a competitive methodology across fields such as fluid mechanics [21], hydrology [22,23], bioinformatics [24], high energy physics [25] and other.”
Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “Reference Key words Summary [82] Machine learning, Genomic medicine A review of machine learning tasks and related datasets in genomic medicine, not specific to deep learning [5] Regulatory genomics, Cellular imaging A review of deep learning’s applications in computational biology, with an emphasis”
Spherical convolutions and their application in molecular modelling (2017, background): “…have demonstrated interesting examples in a broad selection of the natural sciences, ranging from physics (Aurisano et al., 2016; Mills et al., 2017) to biology (Wang et al., 2016; Min et al., 2017), in many cases showing convolutional neural networks to substantially outperform existing methods.”
Graph Neural Networks and Their Current Applications in Bioinformatics (2021, background): “Although previous studies have surveyed deep learning applications in bioinformatics (Wood and Hirst, 2004; Min et al., 2016; Sun et al., 2019) recently published a review paper of GCN in bioinformatics, these studies are confined to a narrow field such as drug discovery in reference.”