Title |
Deep learning-based transcriptome data classification for drug-target interaction prediction
|
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Published in |
BMC Genomics, September 2018
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DOI | 10.1186/s12864-018-5031-0 |
Pubmed ID | |
Authors |
Lingwei Xie, Song He, Xinyu Song, Xiaochen Bo, Zhongnan Zhang |
Abstract |
The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. In this paper, we modeled the prediction of drug-target interactions as a binary classification task. Using transcriptome data from the L1000 database of the LINCS project, we developed a framework based on a deep-learning algorithm to predict potential drug target interactions. Once fully trained, the model achieved over 98% training accuracy. The results of our research demonstrated that our framework could discover more reliable DTIs than found by other methods. This conclusion was validated further across platforms with a high percentage of overlapping interactions. Our model's capacity of integrating transcriptome data from drugs and genes strongly suggests the strength of its potential for DTI prediction, thereby improving the drug discovery process. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Germany | 1 | 20% |
United States | 1 | 20% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 111 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 21 | 19% |
Researcher | 15 | 14% |
Student > Bachelor | 12 | 11% |
Other | 8 | 7% |
Student > Master | 8 | 7% |
Other | 12 | 11% |
Unknown | 35 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 20 | 18% |
Biochemistry, Genetics and Molecular Biology | 18 | 16% |
Medicine and Dentistry | 8 | 7% |
Engineering | 7 | 6% |
Agricultural and Biological Sciences | 6 | 5% |
Other | 12 | 11% |
Unknown | 40 | 36% |