Cargando…
Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering
Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6723056/ https://www.ncbi.nlm.nih.gov/pubmed/31398888 http://dx.doi.org/10.3390/medicina55080451 |
_version_ | 1783448679717273600 |
---|---|
author | Hasan, Mohammad Nazmol Malek, Masuma Binte Begum, Anjuman Ara Rahman, Moizur Mollah, Md. Nurul Haque |
author_facet | Hasan, Mohammad Nazmol Malek, Masuma Binte Begum, Anjuman Ara Rahman, Moizur Mollah, Md. Nurul Haque |
author_sort | Hasan, Mohammad Nazmol |
collection | PubMed |
description | Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. Methods and materials: There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. Results: We observed that Word’s HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co-clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator-activated receptor signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. Conclusions: Overall, the methods proposed in this article, co-cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation. |
format | Online Article Text |
id | pubmed-6723056 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-67230562019-09-10 Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering Hasan, Mohammad Nazmol Malek, Masuma Binte Begum, Anjuman Ara Rahman, Moizur Mollah, Md. Nurul Haque Medicina (Kaunas) Article Background and objectives: Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (expectation-maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. Methods and materials: There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. Results: We observed that Word’s HC method with each of Euclidean, Manhattan, and Minkowski distance measures produces better clustering/co-clustering results. For an example, in the case of the glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the peroxisome proliferator-activated receptor signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. Conclusions: Overall, the methods proposed in this article, co-cluster the genes and DDs as well as detect biomarker genes and their regulatory DDs simultaneously consuming less time compared to other mentioned methods. The results produced by the proposed methods have been validated by the available literature and functional annotation. MDPI 2019-08-08 /pmc/articles/PMC6723056/ /pubmed/31398888 http://dx.doi.org/10.3390/medicina55080451 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hasan, Mohammad Nazmol Malek, Masuma Binte Begum, Anjuman Ara Rahman, Moizur Mollah, Md. Nurul Haque Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering |
title | Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering |
title_full | Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering |
title_fullStr | Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering |
title_full_unstemmed | Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering |
title_short | Assessment of Drugs Toxicity and Associated Biomarker Genes Using Hierarchical Clustering |
title_sort | assessment of drugs toxicity and associated biomarker genes using hierarchical clustering |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6723056/ https://www.ncbi.nlm.nih.gov/pubmed/31398888 http://dx.doi.org/10.3390/medicina55080451 |
work_keys_str_mv | AT hasanmohammadnazmol assessmentofdrugstoxicityandassociatedbiomarkergenesusinghierarchicalclustering AT malekmasumabinte assessmentofdrugstoxicityandassociatedbiomarkergenesusinghierarchicalclustering AT begumanjumanara assessmentofdrugstoxicityandassociatedbiomarkergenesusinghierarchicalclustering AT rahmanmoizur assessmentofdrugstoxicityandassociatedbiomarkergenesusinghierarchicalclustering AT mollahmdnurulhaque assessmentofdrugstoxicityandassociatedbiomarkergenesusinghierarchicalclustering |