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Machine Learning-based Prediction and Characterization of Drug-drug Interactions

Yella, Jaswanth
http://orcid.org/0000-0002-0750-6157
http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613

Abstract Details

2018, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Polypharmacy is the simultaneous combination of two or more drugs at a time, which is unavoidable in the elderly population as they often suffer from multiple complex conditions. A drug-drug interaction (DDI) is a change in the effect of a drug due to polypharmacy. Identifying and characterizing the DDIs is important to avoid hazardous complications and also would help reduce development costs for de novo drug discovery. An in-silico method to predict these DDIs a priori using the existing drug profiles can help mitigate not only the DDI-related adverse event risks but also reduce health care costs. In this thesis, drug related feature data such as pathways, targets, SMILES, MeSH, Indications, adverse events, and contraindications are collected from various sources. Drug-drug similarity for individual feature is calculated and integrated along with DDI labels collected from Drugs.com for 10,67,991 interactions. To handle the high imbalance of labels in the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. Then using the final dataset, a computational machine learning framework is developed to evaluate the classifier performance across multiple datasets and identify the best performing classifier. Random Forest is identified as the best predictive model in this thesis when compared with 5 other classifiers using 5-fold stratified cross-validation. DDI severity characterization is performed using Random Forest for multi-class classification where the labels are safe, minor, moderate and major DDI. The results show that the framework can identify the DDIs and characterize the severity of pairwise drug feature-similarity data, and can therefore be useful in drug development and pharmacovigilance studies.
Anil Jegga, D.V.M. (Committee Chair)
Raj Bhatnagar, Ph.D. (Committee Member)
Ali Mina, Ph.D. (Committee Member)
108 p.
Computer Science
Machine Learning; Drug-Drug Interactions; Similarity-based learning; Class Imbalance; Pharmacovigilance; Supervised Learning

Recommended Citations

Citations

  • Yella, J. (2018). Machine Learning-based Prediction and Characterization of Drug-drug Interactions [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613

    APA Style (7th edition)

  • Yella, Jaswanth. Machine Learning-based Prediction and Characterization of Drug-drug Interactions. 2018. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613.

    MLA Style (8th edition)

  • Yella, Jaswanth. "Machine Learning-based Prediction and Characterization of Drug-drug Interactions." Master's thesis, University of Cincinnati, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613

    Chicago Manual of Style (17th edition)

ucin154399419112613
392
© , some rights reserved.Creative Commons License Machine Learning-based Prediction and Characterization of Drug-drug Interactions by Jaswanth Yella is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.