Ten quick tips for deep learning in biology

Download full text from publisher

File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009803
Download Restriction: no

---

File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1009803&type=printable
Download Restriction: no

---

File URL: https://libkey.io/10.1371/journal.pcbi.1009803?utm_source=ideas
LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
---><---

References listed on IDEAS

as

HTML HTML with abstract plain text plain text with abstract BibTeX RIS (EndNote, RefMan, ProCite) ReDIF JSON

1. Andreas Mardt & Luca Pasquali & Hao Wu & Frank Noé, 2018. "VAMPnets for deep learning of molecular kinetics," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
2. Alec A. K. Nielsen & Christopher A. Voigt, 2018. "Deep learning to predict the lab-of-origin of engineered DNA," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
3. Andreas Mardt & Luca Pasquali & Hao Wu & Frank Noé, 2018. "Author Correction: VAMPnets for deep learning of molecular kinetics," Nature Communications, Nature, vol. 9(1), pages 1-1, December.

Full references (including those not matched with items on IDEAS)

Most related items

These are the items that most often cite the same works as this one and are cited by the same works as this one.

1. Shams Mehdi & Pratyush Tiwary, 2024. "Thermodynamics-inspired explanations of artificial intelligence," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
2. Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
3. Corneel Casert & Isaac Tamblyn & Stephen Whitelam, 2024. "Learning stochastic dynamics and predicting emergent behavior using transformers," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
4. Giacomo Janson & Gilberto Valdes-Garcia & Lim Heo & Michael Feig, 2023. "Direct generation of protein conformational ensembles via machine learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
5. Yuxuan Zhuang & Rebecca J. Howard & Erik Lindahl, 2024. "Symmetry-adapted Markov state models of closing, opening, and desensitizing in α 7 nicotinic acetylcholine receptors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
6. Joshua S. North & Christopher K. Wikle & Erin M. Schliep, 2023. "A Review of Data‐Driven Discovery for Dynamic Systems," International Statistical Review, International Statistical Institute, vol. 91(3), pages 464-492, December.
7. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
8. Trayder Thomas & Benoît Roux, 2021. "Tyrosine kinases: complex molecular systems challenging computational methodologies," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(10), pages 1-13, October.
9. Oliver M. Crook & Kelsey Lane Warmbrod & Greg Lipstein & Christine Chung & Christopher W. Bakerlee & T. Greg McKelvey & Shelly R. Holland & Jacob L. Swett & Kevin M. Esvelt & Ethan C. Alley & William , 2022. "Analysis of the first genetic engineering attribution challenge," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
10. Tomasz Śmiałkowski & Andrzej Czyżewski, 2022. "Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters," Energies, MDPI, vol. 15(24), pages 1-23, December.

More about this item

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1009803. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.