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

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

Bibliographic Details
Main Author: Yella, Jaswanth
Language:English
Published: University of Cincinnati / OhioLINK 2018
Subjects:
Computer Science
Machine Learning
Drug-Drug Interactions
Similarity-based learning
Class Imbalance
Pharmacovigilance
Supervised Learning
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613
id ndltd-OhioLink-oai-etd.ohiolink.edu-ucin154399419112613
record_format oai_dc
spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1543994191126132021-08-03T07:09:15Z Machine Learning-based Prediction and Characterization of Drug-drug Interactions Yella, Jaswanth Computer Science Machine Learning Drug-Drug Interactions Similarity-based learning Class Imbalance Pharmacovigilance Supervised Learning 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. 2018 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613 http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613 unrestricted This thesis or dissertation is protected by copyright: some rights reserved. It is licensed for use under a Creative Commons license. Specific terms and permissions are available from this document's record in the OhioLINK ETD Center.
collection NDLTD
language English
sources NDLTD
topic Computer Science
Machine Learning
Drug-Drug Interactions
Similarity-based learning
Class Imbalance
Pharmacovigilance
Supervised Learning
spellingShingle Computer Science
Machine Learning
Drug-Drug Interactions
Similarity-based learning
Class Imbalance
Pharmacovigilance
Supervised Learning
Yella, Jaswanth
Machine Learning-based Prediction and Characterization of Drug-drug Interactions
author Yella, Jaswanth
author_facet Yella, Jaswanth
author_sort Yella, Jaswanth
title Machine Learning-based Prediction and Characterization of Drug-drug Interactions
title_short Machine Learning-based Prediction and Characterization of Drug-drug Interactions
title_full Machine Learning-based Prediction and Characterization of Drug-drug Interactions
title_fullStr Machine Learning-based Prediction and Characterization of Drug-drug Interactions
title_full_unstemmed Machine Learning-based Prediction and Characterization of Drug-drug Interactions
title_sort machine learning-based prediction and characterization of drug-drug interactions
publisher University of Cincinnati / OhioLINK
publishDate 2018
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin154399419112613
work_keys_str_mv AT yellajaswanth machinelearningbasedpredictionandcharacterizationofdrugdruginteractions
_version_ 1719455041745584128

Similar Items