Large-scale prediction and testing of drug activity on side-effect targets
Author
Abstract
Suggested Citation
DOI: 10.1038/nature11159
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Qiyao Luo & Liang Zhao & Jianxing Hu & Hongwei Jin & Zhenming Liu & Liangren Zhang, 2017. "The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
- Zheni Zeng & Yuan Yao & Zhiyuan Liu & Maosong Sun, 2022. "A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Choo Jun Tan & Siew Chin Neoh & Chee Peng Lim & Samer Hanoun & Wai Peng Wong & Chu Kong Loo & Li Zhang & Saeid Nahavandi, 2019. "Application of an evolutionary algorithm-based ensemble model to job-shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 879-890, February.
- Armaghan W Naik & Joshua D Kangas & Christopher J Langmead & Robert F Murphy, 2013. "Efficient Modeling and Active Learning Discovery of Biological Responses," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
- Qing Ye & Chang-Yu Hsieh & Ziyi Yang & Yu Kang & Jiming Chen & Dongsheng Cao & Shibo He & Tingjun Hou, 2021. "A unified drug–target interaction prediction framework based on knowledge graph and recommendation system," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
- Julian E Fuchs & Susanne von Grafenstein & Roland G Huber & Christian Kramer & Klaus R Liedl, 2013. "Substrate-Driven Mapping of the Degradome by Comparison of Sequence Logos," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-15, November.
- Ruibo Zhang & Daniel Nolte & Cesar Sanchez-Villalobos & Souparno Ghosh & Ranadip Pal, 2024. "Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
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:nat:nature:v:486:y:2012:i:7403:d:10.1038_nature11159. 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.
We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.