期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 18, 期 5, 页码 1801-1810出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.3017386
关键词
Stakeholders; Job shop scheduling; Routing; Schedules; Medical services; Delays; Phage-host interaction; bioinformatics; sequence analysis; deep learning; pattern recognition; multi-drug resistance
类别
资金
- National Natural Science Foundation of China [61672037, 21601001, 11835014, U19A2064]
- Anhui Provincial Outstanding Young Talent Support Plan [gxyqZD2017005]
- Young Wanjiang Scholar Program of Anhui Province
- Recruitment Program for Leading Talent Team of Anhui Province [2019-16]
- China Postdoctoral Science Foundation [2018M630699]
- Anhui Provincial Postdoctoral Science Foundation [2017B325]
- Key Project of Anhui Provincial Education Department [KJ2017ZD01]
- National Health and Medical Research Council of Australia (NHMRC) [1144652, 1127948]
- National Institute of Allergy and Infectious Diseases of the National Institutes of Health [R01 AI111965]
- Major Inter-Disciplinary Research (IDR) Grant - Monash University
The research team developed a deep learning-based tool called PredPHI that can predict the host of phages, providing a new treatment method for infections caused by multi-drug resistant bacteria.
Multi-drug resistance (MDR) has become one of the greatest threats to human health worldwide, and novel treatment methods of infections caused by MDR bacteria are urgently needed. Phage therapy is a promising alternative to solve this problem, to which the key is correctly matching target pathogenic bacteria with the corresponding therapeutic phage. Deep learning is powerful for mining complex patterns to generate accurate predictions. In this study, we develop PredPHI (Predicting Phage-Host Interactions), a deep learning-based tool capable of predicting the host of phages from sequence data. We collect >3000 phage-host pairs along with their protein sequences from PhagesDB and GenBank databases and extract a set of features. Then we select high-quality negative samples based on the K-Means clustering method and construct a balanced training set. Finally, we employ a deep convolutional neural network to build the predictive model. The results indicate that PredPHI can achieve a predictive performance of 81 percent in terms of the area under the receiver operating characteristic curve on the test set, and the clustering-based method is significantly more robust than that based on randomly selecting negative samples. These results highlight that PredPHI is a useful and accurate tool for identifying phage-host interactions from sequence data.
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