AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.
Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178347&type=printable |
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| author | Jiansong Fang Ling Wang Yecheng Li Wenwen Lian Xiaocong Pang Hong Wang Dongsheng Yuan Qi Wang Ai-Lin Liu Guan-Hua Du |
| author_facet | Jiansong Fang Ling Wang Yecheng Li Wenwen Lian Xiaocong Pang Hong Wang Dongsheng Yuan Qi Wang Ai-Lin Liu Guan-Hua Du |
| author_sort | Jiansong Fang |
| collection | DOAJ |
| description | Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective. |
| format | Article |
| id | doaj-art-1e7edf3cba8a42dcaa9b553c478a4bc6 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1e7edf3cba8a42dcaa9b553c478a4bc62025-08-20T02:45:28ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01125e017834710.1371/journal.pone.0178347AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.Jiansong FangLing WangYecheng LiWenwen LianXiaocong PangHong WangDongsheng YuanQi WangAi-Lin LiuGuan-Hua DuAlzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178347&type=printable |
| spellingShingle | Jiansong Fang Ling Wang Yecheng Li Wenwen Lian Xiaocong Pang Hong Wang Dongsheng Yuan Qi Wang Ai-Lin Liu Guan-Hua Du AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. PLoS ONE |
| title | AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. |
| title_full | AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. |
| title_fullStr | AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. |
| title_full_unstemmed | AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. |
| title_short | AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease. |
| title_sort | alzhcpi a knowledge base for predicting chemical protein interactions towards alzheimer s disease |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178347&type=printable |
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