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Susmi SJ. Asso approach for classifying gene expression data based on optimal features. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Gene expression profiles are sequences of numbers, and the need to analyze them has now increased significantly. Gene expression data contain a large number of genes and models used for cancer classification. As the wealth of these data being produced, new prediction, classification and clustering techniques are applied to the analysis of the data. Although there are a number of proposed methods with good results, there is still limited diagnostics and a lot of problems still to be solved. To solve the difficulty, in this paper, an efficient gene expression data classification is proposed. To predict the cancer class of patients from the gene expression profile, this paper presents a novel classification framework in the manner of three steps namely, Pre-processing, feature selection and classification. In pre-processing, missing value is filled and redundant data are removed. To attain the enhanced classification outcomes, the important features are selected from the database with the help of Adaptive Salp Swarm Optimization (ASSO) algorithm. Then, the selected features are given to the multi kernel SVM (MKSVM) to classify the gene expression data namely, BRCA, KIRC, COAD, LUAD and PRAD. The performance of proposed methodology is analyzed in terms of different metrics namely, accuracy, sensitivity and specificity. The performance of proposed methodology is 4.5% better than existing method in terms of accuracy.
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Scholz S, Nichols JW, Escher BI, Ankley GT, Altenburger R, Blackwell B, Brack W, Burkhard L, Collette TW, Doering JA, Ekman D, Fay K, Fischer F, Hackermüller J, Hoffman JC, Lai C, Leuthold D, Martinovic-Weigelt D, Reemtsma T, Pollesch N, Schroeder A, Schüürmann G, von Bergen M. The Eco-Exposome Concept: Supporting an Integrated Assessment of Mixtures of Environmental Chemicals. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2022; 41:30-45. [PMID: 34714945 PMCID: PMC9104394 DOI: 10.1002/etc.5242] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/20/2021] [Accepted: 10/26/2021] [Indexed: 05/04/2023]
Abstract
Organisms are exposed to ever-changing complex mixtures of chemicals over the course of their lifetime. The need to more comprehensively describe this exposure and relate it to adverse health effects has led to formulation of the exposome concept in human toxicology. Whether this concept has utility in the context of environmental hazard and risk assessment has not been discussed in detail. In this Critical Perspective, we propose-by analogy to the human exposome-to define the eco-exposome as the totality of the internal exposure (anthropogenic and natural chemicals, their biotransformation products or adducts, and endogenous signaling molecules that may be sensitive to an anthropogenic chemical exposure) over the lifetime of an ecologically relevant organism. We describe how targeted and nontargeted chemical analyses and bioassays can be employed to characterize this exposure and discuss how the adverse outcome pathway concept could be used to link this exposure to adverse effects. Available methods, their limitations, and/or requirement for improvements for practical application of the eco-exposome concept are discussed. Even though analysis of the eco-exposome can be resource-intensive and challenging, new approaches and technologies make this assessment increasingly feasible. Furthermore, an improved understanding of mechanistic relationships between external chemical exposure(s), internal chemical exposure(s), and biological effects could result in the development of proxies, that is, relatively simple chemical and biological measurements that could be used to complement internal exposure assessment or infer the internal exposure when it is difficult to measure. Environ Toxicol Chem 2022;41:30-45. © 2021 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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Affiliation(s)
- Stefan Scholz
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Address correspondence to
| | - John W. Nichols
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Beate I. Escher
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Environmental Toxicology, Center for Applied Geoscience, Eberhard Karls University Tubingen, Tubingen, Germany
| | - Gerald T. Ankley
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Rolf Altenburger
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Institute for Environmental Research, Biologie V, RWTH Aachen University, Aachen, Germany
| | - Brett Blackwell
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Werner Brack
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Department of Evolutionary Ecology and Environmental Toxicology, Faculty of Biological Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Lawrence Burkhard
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Timothy W. Collette
- Office of Research and Development, Ecosystem Processes Division, US Environmental Protection Agency, Athens, Georgia
| | - Jon A. Doering
- National Research Council, US Environmental Protection Agency, Duluth, Minnesota
| | - Drew Ekman
- Office of Research and Development, Ecosystem Processes Division, US Environmental Protection Agency, Athens, Georgia
| | - Kellie Fay
- Office of Pollution Prevention and Toxics, Risk Assessment Division, US Environmental Protection Agency, Washington, DC
| | - Fabian Fischer
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
| | | | - Joel C. Hoffman
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | - Chih Lai
- College of Arts and Sciences, University of Saint Thomas, St. Paul, Minnesota, USA
| | - David Leuthold
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
| | | | | | - Nathan Pollesch
- Office of Research and Development, Great Lakes Ecology and Toxicology Division, US Environmental Protection Agency, Duluth, Minnesota
| | | | - Gerrit Schüürmann
- Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany
- Institute of Organic Chemistry, Technische Universitat Bergakademie Freiberg, Freiberg, Germany
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Serra A, Fratello M, Cattelani L, Liampa I, Melagraki G, Kohonen P, Nymark P, Federico A, Kinaret PAS, Jagiello K, Ha MK, Choi JS, Sanabria N, Gulumian M, Puzyn T, Yoon TH, Sarimveis H, Grafström R, Afantitis A, Greco D. Transcriptomics in Toxicogenomics, Part III: Data Modelling for Risk Assessment. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E708. [PMID: 32276469 PMCID: PMC7221955 DOI: 10.3390/nano10040708] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 03/25/2020] [Accepted: 03/26/2020] [Indexed: 12/30/2022]
Abstract
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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Affiliation(s)
- Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Luca Cattelani
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Irene Liampa
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Georgia Melagraki
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Pekka Kohonen
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Penny Nymark
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
| | - Pia Anneli Sofia Kinaret
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
| | - Karolina Jagiello
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - My Kieu Ha
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Jang-Sik Choi
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Natasha Sanabria
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
| | - Mary Gulumian
- National Institute for Occupational Health, Johannesburg 30333, South Africa; (N.S.); (M.G.)
- Haematology and Molecular Medicine Department, School of Pathology, University of the Witwatersrand, Johannesburg 2050, South Africa
| | - Tomasz Puzyn
- QSAR Lab Ltd., Aleja Grunwaldzka 190/102, 80-266 Gdansk, Poland; (K.J.); (T.P.)
- University of Gdansk, Faculty of Chemistry, Wita Stwosza 63, 80-308 Gdansk, Poland
| | - Tae-Hyun Yoon
- Center for Next Generation Cytometry, Hanyang University, Seoul 04763, Korea; (M.K.H.); (J.-S.C.); (T.-H.Y.)
- Department of Chemistry, College of Natural Sciences, Hanyang University, Seoul 04763, Korea
- Institute of Next Generation Material Design, Hanyang University, Seoul 04763, Korea
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece; (I.L.); (H.S.)
| | - Roland Grafström
- Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; (P.K.); (P.N.); (R.G.)
- Division of Toxicology, Misvik Biology, 20520 Turku, Finland
| | - Antreas Afantitis
- Nanoinformatics Department, NovaMechanics Ltd., Nicosia 1065, Cyprus; (G.M.); (A.A.)
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, FI-33014 Tampere, Finland; (A.S.); (M.F.); (L.C.); (A.F.); (P.A.S.K.)
- BioMediTech Institute, Tampere University, FI-33014 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
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Ferreira DW, Allard P. Models of germ cell development and their application for toxicity studies. ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2015; 56:637-49. [PMID: 25821157 PMCID: PMC4586303 DOI: 10.1002/em.21946] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Revised: 02/15/2015] [Accepted: 02/21/2015] [Indexed: 05/19/2023]
Abstract
Germ cells are unique in their ability to transfer genetic information and traits from generation to generation. As such, the proper development of germ cells and the integrity of their genome are paramount to the health of organisms and the survival of species. Germ cells are also exquisitely sensitive to environmental influences although the testing of germ cell toxicity, especially in females, has proven particularly challenging. In this review, we first describe the remarkable odyssey of germ cells in mammals, with an emphasis on the female germline, from their initial specification early during embryogenesis to the generation of mature gametes in adults. We also describe the current methods used in germ cell toxicity testing and their limitations in examining the complex features of mammalian germ cell development. To bypass these challenges, we propose the use of alternative model systems such as Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and in vitro germ cell methods that have distinct advantages over traditional toxicity models. We discuss the benefits and limitations of each approach, their application to germ cell toxicity studies, and the need for computational approaches to maximize the usefulness of these models. Together, the inclusion of these alternative germ cell toxicity models will be invaluable for the examination of stages not easily accessible in mammals as well as the large scale, high-throughput investigation of germ cell toxicity.
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Affiliation(s)
- Daniel W Ferreira
- Institute for Society and Genetics, Department of Environmental Health Sciences, University of California, Los Angeles, California
| | - Patrick Allard
- Institute for Society and Genetics, Department of Environmental Health Sciences, University of California, Los Angeles, California
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