A novel integrated action crossing method for drug-drug interaction prediction in non-communicable diseases.

Journal: Computer methods and programs in biomedicine

Published Date: Sep 1, 2018

Abstract

BACKGROUND AND OBJECTIVE: Drug-drug interaction (DDI) is one of the main causes of toxicity and treatment inefficacy. This work focuses on non-communicable diseases (NCDs), the non-transmissible and long-lasting diseases since they are the leading cause of death globally. Drugs that are used in NCDs increase the probability of DDIs as a result of long time usage. This work proposes an Integrated Action Crossing (IAC) method that is effective in predicting the NCDs DDIs based on pharmacokinetic (PK) mechanism.

Authors

Sathien Hunta

School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand.
Thongchai Yooyativong

School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand.
Nattapol Aunsri

School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand; Brain Science and Engineering Innovation Research Unit, Mae Fah Luang University, Chiang Rai, Thailand. Electronic address: nattapol.aun@mfu.ac.th.

Keywords

Algorithms Area Under Curve Cluster Analysis Computer Simulation Cytochrome P-450 Enzyme System Databases, Factual Drug Interactions False Positive Reactions Humans Machine Learning Neural Networks, Computer Noncommunicable Diseases Probability Quantitative Structure-Activity Relationship Reproducibility of Results Simvastatin Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (30119852)

A novel integrated action crossing method for drug-drug interaction prediction in non-communicable diseases.

Abstract

Authors

Keywords

External Resources

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