Dataset Information

Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps.

ABSTRACT: ATP binding cassette (ABC) transporters play a pivotal role in drug elimination, particularly on several types of cancer in which these proteins are overexpressed. Due to their promiscuous ligand recognition, building computational models for substrate classification is quite challenging. This study evaluates the use of modified Self-Organizing Maps (SOM) for predicting drug resistance associated with P-gp, MPR1 and BCRP activity. Herein, we present a novel multi-labelled unsupervised classification model which combines a new clustering algorithm with SOM. It significantly improves the accuracy of substrates classification, catching up with traditional supervised machine learning algorithms. Results can be applied to predict the pharmacological profile of new drug candidates during the drug development process.

SUBMITTER: Estrada-Tejedor R

PROVIDER: S-EPMC5931609 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps.

Estrada-Tejedor Roger R Ecker Gerhard F GF

Scientific reports 20180501 1

ATP binding cassette (ABC) transporters play a pivotal role in drug elimination, particularly on several types of cancer in which these proteins are overexpressed. Due to their promiscuous ligand recognition, building computational models for substrate classification is quite challenging. This study evaluates the use of modified Self-Organizing Maps (SOM) for predicting drug resistance associated with P-gp, MPR1 and BCRP activity. Herein, we present a novel multi-labelled unsupervised classifica ...[more]

PMID: 29717183

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Project description:BackgroundParallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.ResultsThe method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten of thousands of genes to a few thousand metagenes, each representing a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of genes related to specific molecular processes in the respective tissue. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering are better represented and provide better signal-to-noise ratios if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues broadly into three clusters containing nervous, immune system and the remaining tissues.ConclusionsThe SOM technique provides a more intuitive and informative global view of the behavior of a few well-defined modules of correlated and differentially expressed genes than the separate discovery of the expression levels of hundreds or thousands of individual genes. The program is available as R-package 'oposSOM'.

Dataset Information

Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps.

Publications

Predicting drug resistance related to ABC transporters using unsupervised Consensus Self-Organizing Maps.

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