Interactive Toxicogenomics: Gene set discovery, clustering and analysis in Toxygates

Nyström-Persson, Johan; Natsume-Kitatani, Yayoi; Igarashi, Yoshinobu; Satoh, Daisuke; Mizuguchi, Kenji

doi:10.1038/s41598-017-01500-1

Cited by 13 publications

(8 citation statements)

References 35 publications

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“…Many authors' consensus is that identification of subsets of DCCs which have similar mechanism of action over the respective subsets of genes as well as identification of the biomarker genes and their regulatory DCCs are the main objectives of toxicogenomic studies as well as drug development process (Madeira and Oliveira, 2004;Afshari et al, 2011;Nyström-Persson et al, 2017;Hasan et al, 2018;Hasan et al, 2019b). The HC algorithm is more popular and widely used method which can be used for clustering genes and DCCs.…”

Section: Discussionmentioning

confidence: 99%

“…The fold change gene expression (FCGE) data which computed from this experiment using the equations (1) and (2) are used in most of the toxicogenomics studies. Because FCGE dataset directly reflects the treatment effects (Nyström-Persson et al, 2013;Chung et al, 2015;Nyström-Persson et al, 2017;Hasan et al, 2018Hasan et al, , 2019. The fold change gene expression for the ℎ ( = 1, 2, ⋯ ) sample and for single time point can be computed from the gene expression data of this experiment using the equations:…”

Section: Description Of Toxicogenomic Datamentioning

confidence: 99%

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Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

Hasan

Badsha

Mollah

2020

Preprint

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Toxicogenomics combines high throughput molecular technologies with statistical and machine learning approaches to discover a similar group of doses of chemical compounds (DCCs) and genes to explore toxicogenomic biomarkers and their regulatory DCCs. This is also very important in the toxicity study of environmental stressors, synthetic chemicals and drug discovery and development process. Different clustering algorithms are concerned with the discovering of interesting clusters/groups of row or column entities of a dataset. Among those hierarchical clustering (HC) and logistic probabilistic hidden variable model (LPHVM) can identify toxicogenomic biomarkers and their regulatory DCCs forming co-cluster. However, the HC method is very sensitive to outlying observations. On the other hand, though LPHVM is a robust approach, it consumes more time for calculation since it is Expectation-Maximization (EM) based iterative approach. Additionally, the LPHVM creates artificiality problem taking absolute value of the data matrix. Therefore, to overcome these problems in this paper, we proposed a robust hierarchical co-clustering (RHCOC) algorithm to co-cluster genes and DCCs simultaneously with a view to explore toxicogenomic biomarkers and their regulatory DCCs. The performance of the proposed RHCOC algorithm over the github (https://github.com/mdbahadur/rhcoclust).

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Section: Discussionmentioning

confidence: 99%

Section: Description Of Toxicogenomic Datamentioning

confidence: 99%

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

Hasan

Badsha

Mollah

2020

Preprint

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“…The main objective of performing cluster analysis with transcriptomic data is to group together genes that share the same pattern of expression but differ from the genes in other clusters. The main assumption is that the genes in the same cluster may be involved in similar or related biological functions [117][118][119].…”

Section: Clusteringmentioning

confidence: 99%

“…The resulting clusters were mostly consistent with prior toxicological knowledge. Nystrom-Persson et al [117] applied hierarchical clustering (Ward's method with Pearson distance) to the toxicogenomics database Open TG-GATEs to study the hepatotoxicity of pirinixic acid. Hasan et al [127] applied a number of hierarchical clustering configurations to the Japanese Toxicogenomics Project dataset to detect toxic DDs and their associated biomarker genes.…”

Section: Clusteringmentioning

confidence: 99%

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

et al. 2020

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Transcriptomics data are relevant to address a number of challenges in Toxicogenomics (TGx). After careful planning of exposure conditions and data preprocessing, the TGx data can be used in predictive toxicology, where more advanced modelling techniques are applied. The large volume of molecular profiles produced by omics-based technologies allows the development and application of artificial intelligence (AI) methods in TGx. Indeed, the publicly available omics datasets are constantly increasing together with a plethora of different methods that are made available to facilitate their analysis, interpretation and the generation of accurate and stable predictive models. In this review, we present the state-of-the-art of data modelling applied to transcriptomics data in TGx. We show how the benchmark dose (BMD) analysis can be applied to TGx data. We review read across and adverse outcome pathways (AOP) modelling methodologies. We discuss how network-based approaches can be successfully employed to clarify the mechanism of action (MOA) or specific biomarkers of exposure. We also describe the main AI methodologies applied to TGx data to create predictive classification and regression models and we address current challenges. Finally, we present a short description of deep learning (DL) and data integration methodologies applied in these contexts. Modelling of TGx data represents a valuable tool for more accurate chemical safety assessment. This review is the third part of a three-article series on Transcriptomics in Toxicogenomics.

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“…Most of the studies were based on microarray technology ( Mei et al, 2010 ), even though newer technologies, such as high-throughput sequencing (RNA-seq), are already in use in other research areas. Such transcriptomic profiles have previously been used for predicting toxic drug effects ( Gusenleitner et al, 2014 ; Kohonen and Parkkinen, 2017 ; Nystrom-Persson et al, 2017 ; Rueda-Zarate et al, 2017 ), but further analysis for identifying the functional and molecular mechanisms behind the toxic effects is still much needed.…”

Section: Introductionmentioning

confidence: 99%

Network and Pathway Analysis of Toxicogenomics Data

Barel

Herwig

2018

Front. Genet.

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Toxicogenomics is the study of the molecular effects of chemical, biological and physical agents in biological systems, with the aim of elucidating toxicological mechanisms, building predictive models and improving diagnostics. The vast majority of toxicogenomics data has been generated at the transcriptome level, including RNA-seq and microarrays, and large quantities of drug-treatment data have been made publicly available through databases and repositories. Besides the identification of differentially expressed genes (DEGs) from case-control studies or drug treatment time series studies, bioinformatics methods have emerged that infer gene expression data at the molecular network and pathway level in order to reveal mechanistic information. In this work we describe different resources and tools that have been developed by us and others that relate gene expression measurements with known pathway information such as over-representation and gene set enrichment analyses. Furthermore, we highlight approaches that integrate gene expression data with molecular interaction networks in order to derive network modules related to drug toxicity. We describe the two main parts of the approach, i.e., the construction of a suitable molecular interaction network as well as the conduction of network propagation of the experimental data through the interaction network. In all cases we apply methods and tools to publicly available rat in vivo data on anthracyclines, an important class of anti-cancer drugs that are known to induce severe cardiotoxicity in patients. We report the results and functional implications achieved for four anthracyclines (doxorubicin, epirubicin, idarubicin, and daunorubicin) and compare the information content inherent in the different computational approaches.

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Interactive Toxicogenomics: Gene set discovery, clustering and analysis in Toxygates

Cited by 13 publications

References 35 publications

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

Robust Hierarchical Co-clustering to Explore Toxicogenomic Biomarkers and Their Regulatory Doses of Chemical Compounds

Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment

Network and Pathway Analysis of Toxicogenomics Data

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