Pairwise Drug Interaction Prediction, Integrating, Clustering, and Classification
| dc.contributor.author | Hameed, P. N. | |
| dc.date.accessioned | 2021-01-29T08:29:34Z | |
| dc.date.available | 2021-01-29T08:29:34Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Investigating drug interactions is vital when administrating multiple drugs for patients. However, experimentally-based drug interaction prediction consumes a large investment of money and time. Computational-based drug interaction prediction has shown significant benefits during the last two decades. Supervised and unsupervised machine learning approaches are frequently used to classify drug interactions based on drug characteristics. However, drug interactions cannot be classified only based on homogeneous properties as they have their limitations. Hence, investigating computational methods for heterogeneous data integration becomes necessary. Moreover, employing a representative training sample is crucial for obtaining a better classification of drug interactions. Though there are standard data on harmful drug interactions, there are no standard data for non-harmful drug interactions. Thus, investigating methods to find representative negatives is crucial. The proposed approach has two folds: (i) using an unsupervised two-tiered clustering approach for drug-pair clustering and (ii) using supervised classification for drug interaction classification. This study consided chemical, disease, protein, and side effects characteristics of drugs providing an opportunity to demonstrate drug characteristics from those four perspectives. The two-tiered clustering approach was used in the first fold that enables drug-pair clustering as well as heterogeneous data integration. The clustered result can be used to infer plausible negatives for drug interaction classification. In the second phase, binary classifiers such as Support Vector Machine, Logistic Regression, and Random Forest can be used. Applying an ensemble learning model integrating with the results of multiple classifiers could further improve the clinical significance of the predicted drug interactions. Keywords: Drug interactions, Heterogeneous data integration | en_US |
| dc.identifier.isbn | 9789550481293 | |
| dc.identifier.uri | http://www.erepo.lib.uwu.ac.lk/bitstream/handle/123456789/5703/proceeding_oct_08-180.pdf?sequence=1&isAllowed=y | |
| dc.language.iso | en | en_US |
| dc.publisher | Uva Wellassa University of Sri Lanka | en_US |
| dc.relation.ispartofseries | ;International Research Conference | |
| dc.subject | Computer Science | en_US |
| dc.subject | Information Science | en_US |
| dc.subject | Computing and Information Management | en_US |
| dc.subject | Health Science | en_US |
| dc.title | Pairwise Drug Interaction Prediction, Integrating, Clustering, and Classification | en_US |
| dc.title.alternative | International Research Conference 2020 | en_US |
| dc.type | Other | en_US |
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