Result: Ten quick tips for deep learning in biology.

Title:
Ten quick tips for deep learning in biology.
Authors:
Lee, Benjamin D., Gitter, Anthony, Greene, Casey S., Raschka, Sebastian, Maguire, Finlay, Titus, Alexander J., Kessler, Michael D., Lee, Alexandra J., Chevrette, Marc G., Stewart, Paul Allen, Britto-Borges, Thiago, Cofer, Evan M., Yu, Kun-Hsing, Carmona, Juan Jose, Fertig, Elana J., Kalinin, Alexandr A., Signal, Brandon, Lengerich, Benjamin J., Triche Jr., Timothy J., Boca, Simina M.
Source:
PLoS Computational Biology; 3/24/2022, Vol. 18 Issue 3, p1-20, 20p, 1 Chart, 1 Graph
Subject Terms:
DEEP learning, REINFORCEMENT learning, ARTIFICIAL neural networks, LIFE sciences, HIGH performance computing, NATURAL language processing
Database:
Complementary Index

Further Information

Examples of such data include classified or confidential data, biological data related to trade secrets, medical records, or other personally identifiable information [[117]]. Springer Science and Business Media LLC; 2021. doi: 10.1007/978-981-15-3383-9 54 14 Raschka S, Patterson J, Nolet C. Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence. Another example is provided by Koutsoukas and colleagues, who benchmarked several traditional machine learning approaches against deep neural networks for modeling bioactivity data on moderately sized datasets [[53]]. Machine learning is a modern approach to problem-solving and task automation. However, methods cannot overcome substantial data shortages in many practical scenarios, and recent research investigating machine learning methods in neuroimaging studies of depression suggests that high prediction accuracies obtained from small datasets may be caused by misestimation due to insufficient test dataset sizes [[21]]. [Extracted from the article]

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