# Deep Learning in Bioinformatics

arXiv: https://arxiv.org/abs/1603.06430 · HTML: https://mlsanity.com/paper/1603.06430/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2016.

## Built on by (most cited first, 30 of 48)

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- [Physics informed neural networks for continuum micromechanics](https://mlsanity.com/paper/2110.07374/) (2021)
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- [A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications](https://mlsanity.com/paper/2408.02686/) (2024)
- [Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey](https://mlsanity.com/paper/2203.06951/) (2022)
- [SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2305.08316/) (2023)
- [Physics-informed active learning with simultaneous weak-form latent space dynamics identification](https://mlsanity.com/paper/2407.00337/) (2024)
- [BridgeDPI: A Novel Graph Neural Network for Predicting Drug-Protein Interactions](https://mlsanity.com/paper/2101.12547/) (2021)
- [Large Language Models in Plant Biology](https://mlsanity.com/paper/2401.02789/) (2024)
- [Calibrating multi-dimensional complex ODE from noisy data via deep neural networks](https://mlsanity.com/paper/2106.03591/) (2021)
- [Deep Algorithm Unrolling for Biomedical Imaging](https://mlsanity.com/paper/2108.06637/) (2021)
- [Deep learning models are not robust against noise in clinical text](https://mlsanity.com/paper/2108.12242/) (2021)
- [CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference](https://mlsanity.com/paper/2508.17077/) (2025)
- [Structured and Fast Optimization: The Kronecker SGD Algorithm](https://mlsanity.com/paper/2305.08001/) (2023)
- [Deep Learning, Natural Language Processing, and Explainable Artificial Intelligence in the Biomedical Domain](https://mlsanity.com/paper/2202.12678/) (2022)
- [Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems](https://mlsanity.com/paper/2305.07135/) (2023)
- [Utilizing deep learning models for the identification of enhancers and super-enhancers based on genomic and epigenomic features](https://mlsanity.com/paper/2401.07470/) (2024)
- [Comprehensive benchmarking of large language models for RNA secondary structure prediction](https://mlsanity.com/paper/2410.16212/) (2024)
- [SLA2P: Self-supervised Anomaly Detection with Adversarial Perturbation](https://mlsanity.com/paper/2111.12896/) (2021)
- [A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods](https://mlsanity.com/paper/2112.10372/) (2021)
- [Physics-Informed Machine Learning of Argon Gas-Driven Melt Pool Dynamics](https://mlsanity.com/paper/2307.12304/) (2023)
- [Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook](https://mlsanity.com/paper/2310.03086/) (2023)
- [Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data](https://mlsanity.com/paper/2405.18929/) (2024)
- [Analysis of Vision-based Abnormal Red Blood Cell Classification](https://mlsanity.com/paper/2106.00389/) (2021)
- [TargetNet: Functional microRNA Target Prediction with Deep Neural Networks](https://mlsanity.com/paper/2107.11381/) (2021)
- [SIBILA: A novel interpretable ensemble of general-purpose machine learning models applied to medical contexts](https://mlsanity.com/paper/2205.06234/) (2022)
- [Towards Fine-Dining Recipe Generation with Generative Pre-trained Transformers](https://mlsanity.com/paper/2209.12774/) (2022)
- [Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses](https://mlsanity.com/paper/2304.12858/) (2023)
- [A One-Class Classifier for the Detection of GAN Manipulated Multi-Spectral Satellite Images](https://mlsanity.com/paper/2305.11795/) (2023)
- [Exploring the Relationship: Transformative Adaptive Activation Functions in Comparison to Other Activation Functions](https://mlsanity.com/paper/2402.09249/) (2024)

## Abstract (arXiv)

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.