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DeepTox: Toxicity Prediction using Deep Learning

Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Sepp Hochreiter • Frontiers in Environmental Science (2016)

The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 differen...

OPENALEX cc-by DOI: https://doi.org/10.3389/fenvs.2015.00080

Time-series forecasting with deep learning: a survey

Bryan Y. Lim, Stefan Zohren • Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (2021)

Numerous deep learning architectures have been developed to accommodate the diversity of time-series datasets across different domains. In this article, we survey common encoder and decoder designs used in both one-step-ahead and multi-hori...

OPENALEX Open Access DOI: https://doi.org/10.1098/rsta.2020.0209

Deep Learning: A Review

Rocio Vargas, Amir Hosein Mosavi, Ramon Ruiz • Preprints.org (2018)

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level i...

OPENALEX cc-by DOI: https://doi.org/10.20944/preprints201810.0218.v1

Deep Learning for Hyperspectral Image Classification: An Overview

Shutao Li A, Weiwei Song, Leyuan Fang, Yushi Chen et al. • IEEE Transactions on Geoscience and Remote Sensing (2019)

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine ...

OPENALEX Open Access DOI: https://doi.org/10.1109/tgrs.2019.2907932

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi • IEEE Communications Surveys & Tutorials (2019)

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traff...

OPENALEX Open Access DOI: https://doi.org/10.1109/comst.2019.2904897

Deep Learning for Computer Vision: A Brief Review

Athanasios S. Voulodimos, Nikolaos D. Doulamis, Anastasios D. Doulamis, Eftychios E. Protopapadakis • Computational Intelligence and Neuroscience (2018)

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief ...

OPENALEX cc-by DOI: https://doi.org/10.1155/2018/7068349 PMC: 5816885

Opportunities and obstacles for deep learning in biology and medicine

Travers H. Ching, Daniel Scott Himmelstein, Brett K. Beaulieu‐Jones, Alexandr A. Kalinin et al. • Journal of The Royal Society Interface (2018)

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and m...

OPENALEX cc-by DOI: https://doi.org/10.1098/rsif.2017.0387 PMC: 5938574

Application of Deep Learning in Food: A Review

Lei Zhou, Chu Zhang, Fei Liu, Zhengjun Qiu et al. • Comprehensive Reviews in Food Science and Food Safety (2019)

Deep learning has been proved to be an advanced technology for big data analysis with a large number of successful cases in image processing, speech recognition, object detection, and so on. Recently, it has also been introduced in food sci...

OPENALEX DOI: https://doi.org/10.1111/1541-4337.12492

Deep Learning in Ultrasound Imaging

Ruud J. G. van Sloun, Regev Cohen, Yonina C. Eldar • Proceedings of the IEEE (2019)

In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on ...

OPENALEX Open Access DOI: https://doi.org/10.1109/jproc.2019.2932116

Deep learning in optical metrology: a review

Chao Zuo, Jiaming Qian, Shijie Feng, Wei Yin et al. • Light Science & Applications (2022)

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, n...

OPENALEX cc-by DOI: https://doi.org/10.1038/s41377-022-00714-x PMC: 8866517

A Review of Machine Learning and Deep Learning Applications

Pramila P. Shinde, Seema Shah • International Conference on Computing Communication Control and automation (2018)

Machine learning is one of the fields in the modern computing world. A plenty of research has been undertaken to make machines intelligent. Learning is a natural human behavior which has been made an essential aspect of the machines as well...

OPENALEX DOI: https://doi.org/10.1109/iccubea.2018.8697857

The rise of deep learning in drug discovery

Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona et al. • Drug Discovery Today (2018)

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.drudis.2018.01.039

Overview of deep learning

Xuedan Du, Yinghao Cai, Shuo Wang, Leijie Zhang • Youth Academic Annual Conference of Chinese Association of Automation (YAC) (2016)

In recent years, deep learning has achieved great success in many fields, such as computer vision and natural language processing. Compared to traditional machine learning methods, deep learning has a strong learning ability and can make be...

OPENALEX DOI: https://doi.org/10.1109/yac.2016.7804882

Applications of Deep Learning in Biomedicine

Polina Mamoshina, Armando S. Vieira, Eugene Lane, Alex A. Zhavoronkov • Molecular Pharmaceutics (2016)

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this ...

OPENALEX DOI: https://doi.org/10.1021/acs.molpharmaceut.5b00982

Deep Learning in Medical Image Analysis

Dinggang G. Shen, Guorong Wu A, Heung‐Il Suk • Annual Review of Biomedical Engineering (2017)

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical image...

OPENALEX Open Access DOI: https://doi.org/10.1146/annurev-bioeng-071516-044442 PMC: 5479722

Survey on deep learning with class imbalance

Justin M. Johnson, Taghi M. Khoshgoftaar • Journal Of Big Data (2019)

The purpose of this study is to examine existing deep learning techniques for addressing class imbalanced data. Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in...

OPENALEX cc-by DOI: https://doi.org/10.1186/s40537-019-0192-5

Deep learning for sentiment analysis: A survey

Lei Zhang, Shuai Wang, Bing Liu • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2018)

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state‐of‐the‐art prediction results. Along with the success of deep learning in many appl...

OPENALEX Open Access DOI: https://doi.org/10.1002/widm.1253