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University of Melbourne

1. Hameed, Pathima Nusrath. In silico methods for drug repositioning and drug-drug interaction prediction.

Degree: 2018, University of Melbourne

Drug repositioning and drug-drug interaction (DDI) prediction are two fundamental applications having a large impact on drug development and clinical care. Drug repositioning aims to identify new uses for existing drugs. Moreover, understanding harmful DDIs is essential to enhance the effects of clinical care. Exploring both therapeutic uses and adverse effects of drugs or a pair of drugs have significant benefits in pharmacology. The use of computational methods to support drug repositioning and DDI prediction enable improvements in the speed of drug development compared to in vivo and in vitro methods. This thesis investigates the consequences of employing a representative training sample in achieving better performance for DDI classification. The Positive-Unlabeled Learning method introduced in this thesis aims to employ representative positives as well as reliable negatives to train the binary classifier for inferring potential DDIs. Moreover, it explores the importance of a finer-grained similarity metric to represent the pairwise drug similarities. Drug repositioning can be approached by new indication detection. In this study, Anatomical Therapeutic Chemical (ATC) classification is used as the primary source to determine the indications/therapeutic uses of drugs for drug repositioning. This thesis presents a two-tiered clustering approach for obtaining pairwise drug similarity and heterogeneous drug data integration which is employed for large-scale drug repositioning. Moreover, this thesis demonstrates subnetwork identification as a suitable approach for new indication detection for existing drugs. Subnetwork identification method identifies a subgraph from a large drug similarity network, connecting a set of given drugs known as ‘terminals’. In this study, the ‘terminals’ are selected according to the ATC classification system; hence meaningful subnetworks are identified. The proposed subnetwork identification method is employed to infer drug repositioning candidates for cardiovascular diseases and diseases related to the nervous system. New target detection for existing drugs is also beneficial for drug repositioning. This thesis proposes a useful computational method for target clustering which is extended to identify new drug-target relationships. It demonstrates the significance of integrating dimensionality reduction and outlier detection to overcome the limitations arising from the incomplete drug-target interaction data. The clinical significance and literature-based evidence illustrate the relevance of the proposed methods. The proposed methods can be employed in other similar applications where applicable.

Subjects/Keywords: drug repurposing; biomedical informatics; DDI prediction; drug-target prediction; heterogeneous data integration; cluster evaluation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hameed, P. N. (2018). In silico methods for drug repositioning and drug-drug interaction prediction. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/219484

Chicago Manual of Style (16th Edition):

Hameed, Pathima Nusrath. “In silico methods for drug repositioning and drug-drug interaction prediction.” 2018. Doctoral Dissertation, University of Melbourne. Accessed April 11, 2021. http://hdl.handle.net/11343/219484.

MLA Handbook (7th Edition):

Hameed, Pathima Nusrath. “In silico methods for drug repositioning and drug-drug interaction prediction.” 2018. Web. 11 Apr 2021.

Vancouver:

Hameed PN. In silico methods for drug repositioning and drug-drug interaction prediction. [Internet] [Doctoral dissertation]. University of Melbourne; 2018. [cited 2021 Apr 11]. Available from: http://hdl.handle.net/11343/219484.

Council of Science Editors:

Hameed PN. In silico methods for drug repositioning and drug-drug interaction prediction. [Doctoral Dissertation]. University of Melbourne; 2018. Available from: http://hdl.handle.net/11343/219484

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