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Classification of Highly Unbalanced CYP450 Data of Drugs Using Cost Sensitive Machine Learning Techniques
- Source :
- ChemInform. 38
- Publication Year :
- 2007
- Publisher :
- Wiley, 2007.
-
Abstract
- In this paper, we study the classifications of unbalanced data sets of drugs. As an example we chose a data set of 2D6 inhibitors of cytochrome P450. The human cytochrome P450 2D6 isoform plays a key role in the metabolism of many drugs in the preclinical drug discovery process. We have collected a data set from annotated public data and calculated physicochemical properties with chemoinformatics methods. On top of this data, we have built classifiers based on machine learning methods. Data sets with different class distributions lead to the effect that conventional machine learning methods are biased toward the larger class. To overcome this problem and to obtain sensitive but also accurate classifiers we combine machine learning and feature selection methods with techniques addressing the problem of unbalanced classification, such as oversampling and threshold moving. We have used our own implementation of a support vector machine algorithm as well as the maximum entropy method. Our feature selection is based on the unsupervised McCabe method. The classification results from our test set are compared structurally with compounds from the training set. We show that the applied algorithms enable the effective high throughput in silico classification of potential drug candidates.
- Subjects :
- Databases, Factual
Active learning (machine learning)
Computer science
General Chemical Engineering
Drug Evaluation, Preclinical
Linear classifier
Feature selection
Semi-supervised learning
Library and Information Sciences
Machine learning
computer.software_genre
Relevance vector machine
Cytochrome P-450 Enzyme System
Artificial Intelligence
Oversampling
Throughput (business)
Structured support vector machine
Chemistry
business.industry
Online machine learning
General Chemistry
General Medicine
Class (biology)
Computer Science Applications
Data set
ComputingMethodologies_PATTERNRECOGNITION
Computational learning theory
Pharmaceutical Preparations
Cheminformatics
Test set
Costs and Cost Analysis
Key (cryptography)
Data mining
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 15222667 and 09317597
- Volume :
- 38
- Database :
- OpenAIRE
- Journal :
- ChemInform
- Accession number :
- edsair.doi.dedup.....eaabb2d81d34d3dee9885f7074bc01d6