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Jiao Z, Hu P, Xu H, Wang Q. Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications. ACS CHEMICAL HEALTH & SAFETY 2020. [DOI: 10.1021/acs.chas.0c00075] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Zeren Jiao
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Pingfan Hu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Hongfei Xu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Qingsheng Wang
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
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2
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The importance of mathematical modelling in chemical risk assessment and the associated quantification of uncertainty. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.comtox.2018.12.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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3
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Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicol Lett 2017; 275:57-66. [DOI: 10.1016/j.toxlet.2017.03.023] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 03/24/2017] [Accepted: 03/24/2017] [Indexed: 01/13/2023]
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4
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Wang CC, Lin YC, Wang SS, Shih C, Lin YH, Tung CW. SkinSensDB: a curated database for skin sensitization assays. J Cheminform 2017; 9:5. [PMID: 28194231 PMCID: PMC5285290 DOI: 10.1186/s13321-017-0194-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Accepted: 01/23/2017] [Indexed: 12/13/2022] Open
Abstract
Skin sensitization is an important toxicological endpoint for chemical hazard determination and safety assessment. Prediction of chemical skin sensitizer had traditionally relied on data from rodent models. The development of the adverse outcome pathway (AOP) and associated alternative in vitro assays have reshaped the assessment of skin sensitizers. The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance. Current computational models to predict skin sensitization mainly based on in vivo assays without incorporating alternative in vitro assays. However, there are few freely available databases integrating both the in vivo and the in vitro skin sensitization assays for development of AOP-based skin sensitization prediction models. To facilitate the development of AOP-based prediction models, a skin sensitization database named SkinSensDB has been constructed by curating data from published AOP-related assays. In addition to providing datasets for developing computational models, SkinSensDB is equipped with browsing and search tools which enable the assessment of new compounds for their skin sensitization potentials based on data from structurally similar compounds. SkinSensDB is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.
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Affiliation(s)
- Chia-Chi Wang
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan.,PhD Program in Toxicology, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan.,National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, 35053 Taiwan.,Institute of Environmental Engineering, National Sun Yat-sen University, Kaohsiung, 80424 Taiwan
| | - Ying-Chi Lin
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan.,PhD Program in Toxicology, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan
| | - Shan-Shan Wang
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan
| | - Chieh Shih
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan
| | - Yi-Hui Lin
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan
| | - Chun-Wei Tung
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan.,PhD Program in Toxicology, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan.,National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, 35053 Taiwan.,Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan
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5
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Donis-González IR, Guyer DE, Pease A. Postharvest noninvasive assessment of undesirable fibrous tissue in fresh processing carrots using computer tomography images. J FOOD ENG 2016. [DOI: 10.1016/j.jfoodeng.2016.06.024] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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6
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Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules. PLoS One 2016; 11:e0155419. [PMID: 27271321 PMCID: PMC4896476 DOI: 10.1371/journal.pone.0155419] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 04/28/2016] [Indexed: 11/29/2022] Open
Abstract
Introduction Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage. Results The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably). Conclusions Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.
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7
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Khulal U, Zhao J, Hu W, Chen Q. Comparison of different chemometric methods in quantifying total volatile basic-nitrogen (TVB-N) content in chicken meat using a fabricated colorimetric sensor array. RSC Adv 2016. [DOI: 10.1039/c5ra25375f] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
PSO-SVMR is an efficient chemometric tool to quantify TVB-N content in chicken.
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Affiliation(s)
- Urmila Khulal
- School of Food & Biological Engineering
- Jiangsu University
- Zhenjiang 212013
- P. R. China
| | - Jiewen Zhao
- School of Food & Biological Engineering
- Jiangsu University
- Zhenjiang 212013
- P. R. China
| | - Weiwei Hu
- School of Food & Biological Engineering
- Jiangsu University
- Zhenjiang 212013
- P. R. China
| | - Quansheng Chen
- School of Food & Biological Engineering
- Jiangsu University
- Zhenjiang 212013
- P. R. China
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Jang JW, Cho NC, Min SJ, Cho YS, Park KD, Seo SH, No KT, Pae AN. Novel Scaffold Identification of mGlu1 Receptor Negative Allosteric Modulators Using a Hierarchical Virtual Screening Approach. Chem Biol Drug Des 2015; 87:239-56. [DOI: 10.1111/cbdd.12654] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Revised: 07/15/2015] [Accepted: 08/18/2015] [Indexed: 11/28/2022]
Affiliation(s)
- Jae Wan Jang
- Center for Neuro-Medicine; Brain Science Institute; Korea Institute of Science and Technology (KIST); Hwarangno 14-gil 5 Seongbuk-gu, Seoul 136-791 Korea
- Department of Biological Chemistry; School of Science; Korea University of Science and Technology; 52 Eoeun dong Yuseong-gu, Daejeon 305-333 Korea
| | - Nam-Chul Cho
- Center for Neuro-Medicine; Brain Science Institute; Korea Institute of Science and Technology (KIST); Hwarangno 14-gil 5 Seongbuk-gu, Seoul 136-791 Korea
- Department of Biotechnology; Yonsei University; Seodaemun-gu, Seoul 120-749 Korea
| | - Sun-Joon Min
- Department of Applied Chemistry; Hanyang University; Ansan, Gyeonggi-do 15588 Korea
| | - Yong Seo Cho
- Center for Neuro-Medicine; Brain Science Institute; Korea Institute of Science and Technology (KIST); Hwarangno 14-gil 5 Seongbuk-gu, Seoul 136-791 Korea
- Department of Biological Chemistry; School of Science; Korea University of Science and Technology; 52 Eoeun dong Yuseong-gu, Daejeon 305-333 Korea
| | - Ki Duk Park
- Center for Neuro-Medicine; Brain Science Institute; Korea Institute of Science and Technology (KIST); Hwarangno 14-gil 5 Seongbuk-gu, Seoul 136-791 Korea
- Department of Biological Chemistry; School of Science; Korea University of Science and Technology; 52 Eoeun dong Yuseong-gu, Daejeon 305-333 Korea
| | - Seon Hee Seo
- Center for Neuro-Medicine; Brain Science Institute; Korea Institute of Science and Technology (KIST); Hwarangno 14-gil 5 Seongbuk-gu, Seoul 136-791 Korea
| | - Kyoung Tai No
- Department of Biotechnology; Yonsei University; Seodaemun-gu, Seoul 120-749 Korea
| | - Ae Nim Pae
- Center for Neuro-Medicine; Brain Science Institute; Korea Institute of Science and Technology (KIST); Hwarangno 14-gil 5 Seongbuk-gu, Seoul 136-791 Korea
- Department of Biological Chemistry; School of Science; Korea University of Science and Technology; 52 Eoeun dong Yuseong-gu, Daejeon 305-333 Korea
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Zhao Y, Huang Y, Zhang X, Zhang S. A quantitative prediction of the viscosity of ionic liquids using Sσ-profilemolecular descriptors. Phys Chem Chem Phys 2015; 17:3761-7. [DOI: 10.1039/c4cp04712e] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A QSPR study of ILs using MLR and SVM algorithms based on COSMO-RS molecular descriptors (Sσ-profile).
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Affiliation(s)
- Yongsheng Zhao
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Key Laboratory of Green Process and Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
| | - Ying Huang
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Key Laboratory of Green Process and Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
| | - Xiangping Zhang
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Key Laboratory of Green Process and Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
| | - Suojiang Zhang
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Key Laboratory of Green Process and Engineering
- Institute of Process Engineering
- Chinese Academy of Sciences
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Lee S, Dong DX, Jindal R, Maguire T, Mitra B, Schloss R, Yarmush M. Predicting full thickness skin sensitization using a support vector machine. Toxicol In Vitro 2014; 28:1413-23. [PMID: 25025180 PMCID: PMC4470375 DOI: 10.1016/j.tiv.2014.07.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 06/26/2014] [Accepted: 07/03/2014] [Indexed: 11/21/2022]
Abstract
To assess the public's propensity for allergic contact dermatitis (ACD), many alternatives to in vivo chemical screening have been developed which generally incorporate a small panel of cell surface and secreted dendritic cell biomarkers. However, given the underlying complexity of ACD, one cell type and limited cellular metrics may be insufficient to predict contact sensitizers accurately. To identify a molecular signature that can further characterize sensitization, we developed a novel system using RealSkin, a full thickness skin equivalent, in co-culture with MUTZ-3 derived Langerhan's cells. This system was used to distinguish a model moderate pro-hapten isoeugenol (IE) and a model strong pre-hapten p-phenylenediamine (PPD) from irritant, salicylic acid (SA). Commonly evaluated metrics such as CD86, CD54, and IL-8 secretion were assessed, in concert with a 27-cytokine multi-plex screen and a functional chemotaxis assay. Data were analyzed with feature selection methods using ANOVA, hierarchical cluster analysis, and a support vector machine to identify the best molecular signature for sensitization. A panel consisting of IL-12, IL-9, VEGF, and IFN-γ predicted sensitization with over 90% accuracy using this co-culture system analysis. Thus, a multi-metric approach that has the potential to identify a molecular signature may be more predictive of contact sensitization.
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Affiliation(s)
- Serom Lee
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States
| | - David Xu Dong
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States
| | - Rohit Jindal
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States
| | - Tim Maguire
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States
| | - Bhaskar Mitra
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States
| | - Rene Schloss
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States
| | - Martin Yarmush
- Department of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, NJ 08854, United States; Center for Engineering in Medicine and the Department of Surgery, Massachusettes General Hospital and the Shriners Burns Hospital, Boston, MA 02114, United States.
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11
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SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. ScientificWorldJournal 2014; 2014:795624. [PMID: 25295306 PMCID: PMC4175386 DOI: 10.1155/2014/795624] [Citation(s) in RCA: 168] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2014] [Revised: 08/05/2014] [Accepted: 08/05/2014] [Indexed: 12/04/2022] Open
Abstract
Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.
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12
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Leiva-Valenzuela GA, Aguilera JM. Automatic detection of orientation and diseases in blueberries using image analysis to improve their postharvest storage quality. Food Control 2013. [DOI: 10.1016/j.foodcont.2013.02.025] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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13
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Martin TM, Grulke CM, Young DM, Russom CL, Wang NY, Jackson CR, Barron MG. Prediction of Aquatic Toxicity Mode of Action Using Linear Discriminant and Random Forest Models. J Chem Inf Model 2013; 53:2229-39. [DOI: 10.1021/ci400267h] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Todd M. Martin
- National Risk Management Research
Laboratory, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United
States
| | - Christopher M. Grulke
- National Exposure
Research Laboratory, U.S. Environmental Protection Agency, Research Triangle
Park, North Carolina 27711, United States
| | - Douglas M. Young
- National Risk Management Research
Laboratory, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United
States
| | - Christine L. Russom
- National Health and Environmental
Effects Research Laboratory, U.S. Environmental Protection Agency, 6201 Congdon Boulevard, Duluth, Minnesota 55804,
United States
| | - Nina Y. Wang
- National
Center for Environmental
Assessment, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United
States
| | - Crystal R. Jackson
- National Health
and Environmental
Effects Research Laboratory, U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida
32561, United States
| | - Mace G. Barron
- National Health
and Environmental
Effects Research Laboratory, U.S. Environmental Protection Agency, 1 Sabine Island Drive, Gulf Breeze, Florida
32561, United States
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15
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Gamble A, Babbar-Sebens M. On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River basin, Indiana, USA. ENVIRONMENTAL MONITORING AND ASSESSMENT 2012; 184:845-875. [PMID: 21455628 DOI: 10.1007/s10661-011-2005-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2010] [Accepted: 03/14/2011] [Indexed: 05/30/2023]
Abstract
Mechanistic hydrologic and water quality models provide useful alternatives for estimating water quality in unmonitored streams. However, developing these elaborate models for large watersheds can be time-consuming and expensive, in addition to challenges that arise during calibration when there is limited spatial and/or temporal monitored in-stream water quality data. The main objective of this research was to investigate different approaches for developing multivariate analysis models as alternative methods for rapidly assessing relationships between spatio-temporal physical attributes of the watershed and water quality conditions in monitored streams, and then using the developed relationships for estimating water quality conditions in unmonitored streams. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. Overall, the non-linear techniques for classification outperformed the linear techniques with an average cross-validation accuracy of 79.7%. Additionally, the geometric mean based models outperformed models based on other statistical indicators with an average cross-validation accuracy of 80.2%. Dividing the data into annual and quarterly datasets also offered important insights into the behavior of certain water quality variables impacted by seasonal variations. The research provides useful guidance on the use and interpretation of the various statistical estimates and statistical models for multivariate water quality analyses.
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Affiliation(s)
- Andrew Gamble
- Department of Earth Science, Indiana University Purdue University Indianapolis, 723 W. Michigan St., Indianapolis, IN 46202, USA
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Hemmateenejad B, Mehdipour A, Deeb O, Sanchooli M, Miri R. Toward an Optimal Approach for Variable Selection in Counter-Propagation Neural Networks: Modeling Protein-Tyrosine Kinase Inhibitory of Flavanoids Using Substituent Electronic Descriptors. Mol Inform 2011; 30:939-49. [DOI: 10.1002/minf.201100081] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2011] [Accepted: 09/29/2011] [Indexed: 11/11/2022]
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17
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Hao M, Li Y, Wang Y, Zhang S. A classification study of human β 3-adrenergic receptor agonists using BCUT descriptors. Mol Divers 2011; 15:877-87. [DOI: 10.1007/s11030-011-9321-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2011] [Accepted: 05/17/2011] [Indexed: 10/18/2022]
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Jahandideh S, Abdolmaleki P. Prediction of melatonin excretion patterns in the rat exposed to ELF magnetic fields based on support vector machine and linear discriminant analysis. Micron 2010; 41:882-5. [PMID: 20554210 DOI: 10.1016/j.micron.2010.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2010] [Revised: 04/03/2010] [Accepted: 04/05/2010] [Indexed: 10/19/2022]
Abstract
Bioeffects of magnetic field exposure have been motivated accomplishing various studies. However, no consensus or guideline is available for experimental designs relating exposure conditions as yet. In the present work, in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF), linear discriminate analysis (LDA) and support vector machines (SVMs) were utilized. Subsequently, performances of LDA and SVMs were compared through resubstitution and jackknife tests on a database containing 33 experiments. Predictor variables were more effective parameters including frequency, polarization, exposure duration and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, matthew's correlation coefficient (MCC) and normalized percentage better than random (S) were used to evaluate the performance of models. The LDA as a conventional model obtained poor prediction performance. On the other hand, SVMs as a more powerful model, which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed 0.38 value of MCC through jackknife test that confirms the higher reliability of the SVMs.
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Affiliation(s)
- Samad Jahandideh
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
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Gunturi SB, Theerthala SS, Patel NK, Bahl J, Narayanan R. Prediction of skin sensitization potential using D-optimal design and GA-kNN classification methods. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2010; 21:305-335. [PMID: 20544553 DOI: 10.1080/10629361003773955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Modelling of skin sensitization data of 255 diverse compounds and 450 calculated descriptors was performed to develop global predictive classification models that are applicable to whole chemical space. With this aim, we employed two automated procedures, (a) D-optimal design to select optimal members of the training and test sets and (b) k-Nearest Neighbour classification (kNN) method along with Genetic Algorithms (GA-kNN Classification) to select significant and independent descriptors in order to build the models. This methodology helped us to derive multiple models, M1-M5, that are stable and robust. The best among them, model M1 (CCR(train) = 84.3%, CCR(test) = 87.2% and CCR(ext) = 80.4%), is based on six neighbours and nine descriptors and further suggests that: (a) it is stable and robust and performs better than the reported models in literature, and (b) the combination of D-optimal design and GA-kNN classification approach is a very promising approach. Consensus prediction based on the models M1-M5 improved the CCR of training, test and external validation datasets by 3.8%, 4.45% and 3.85%, respectively, over M1. From the analysis of the physical meaning of the selected descriptors, it is inferred that the skin sensitization potential of small organic compounds can be accurately predicted using calculated descriptors that code for the following fundamental properties: (i) lipophilicity, (ii) atomic polarizability, (iii) shape, (iii) electrostatic interactions, and (iv) chemical reactivity.
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Affiliation(s)
- S B Gunturi
- Innovation Labs Hyderabad, Tata Consultancy Services Limited, #1, Software Units Layout, Madhapur, Hyderabad - 500 081, India
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Zhang N, Duan G, Gao S, Ruan J, Zhang T. Prediction of the parallel/antiparallel orientation of beta-strands using amino acid pairing preferences and support vector machines. J Theor Biol 2010; 263:360-8. [PMID: 20035768 DOI: 10.1016/j.jtbi.2009.12.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2009] [Revised: 11/05/2009] [Accepted: 12/17/2009] [Indexed: 10/20/2022]
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Fatemi MH, Ghorbanzad'e M, Baher E. Quantitative Structure Retention Relationship Modeling of Retention Time for Some Organic Pollutants. ANAL LETT 2010. [DOI: 10.1080/00032710903486294] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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22
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Asadabadi EB, Abdolmaleki P, Barkooie SMH, Jahandideh S, Rezaei MA. A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase. Comput Biol Med 2009; 39:1089-95. [DOI: 10.1016/j.compbiomed.2009.09.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2009] [Accepted: 09/12/2009] [Indexed: 10/20/2022]
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Prediction of skin sensitization with a particle swarm optimized support vector machine. Int J Mol Sci 2009; 10:3237-3254. [PMID: 19742136 PMCID: PMC2738923 DOI: 10.3390/ijms10073237] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2009] [Accepted: 06/24/2009] [Indexed: 11/16/2022] Open
Abstract
Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers.
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Gamma-turn types prediction in proteins using the two-stage hybrid neural discriminant model. J Theor Biol 2009; 259:517-22. [PMID: 19409396 DOI: 10.1016/j.jtbi.2009.04.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2008] [Revised: 02/19/2009] [Accepted: 04/21/2009] [Indexed: 11/20/2022]
Abstract
Due to the slightly success of protein secondary structure prediction using the various algorithmic and non-algorithmic techniques, similar techniques have been developed for predicting gamma-turns in proteins by Kaur and Raghava [2003. A neural-network based method for prediction of gamma-turns in proteins from multiple sequence alignment. Protein Sci. 12, 923-929]. However, the major limitation of previous methods was inability in predicting gamma-turn types. In a recent investigation we introduced a sequence based predictor model for predicting gamma-turn types in proteins [Jahandideh, S., Sabet Sarvestani, A., Abdolmaleki, P., Jahandideh, M., Barfeie, M, 2007a. gamma-turn types prediction in proteins using the support vector machines. J. Theor. Biol. 249, 785-790]. In the present work, in order to analyze the effect of sequence and structure in the formation of gamma-turn types and predicting gamma-turn types in proteins, we applied novel hybrid neural discriminant modeling procedure. As the result, this study clarified the efficiency of using the statistical model preprocessors in determining the effective parameters. Moreover, the optimal structure of neural network can be simplified by a preprocessor in the first stage of hybrid approach, thereby reducing the needed time for neural network training procedure in the second stage and the probability of overfitting occurrence decreased and a high precision and reliability obtained in this way.
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Classification structure–activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives. Toxicol Lett 2008; 177:10-9. [DOI: 10.1016/j.toxlet.2007.12.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2007] [Revised: 12/12/2007] [Accepted: 12/12/2007] [Indexed: 11/20/2022]
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Patlewicz G, Aptula A, Roberts D, Uriarte E. A Minireview of Available Skin Sensitization (Q)SARs/Expert Systems. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200710067] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Wang J, Du H, Yao X, Hu Z. Using classification structure pharmacokinetic relationship (SCPR) method to predict drug bioavailability based on grid-search support vector machine. Anal Chim Acta 2007; 601:156-63. [DOI: 10.1016/j.aca.2007.08.040] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2007] [Revised: 07/20/2007] [Accepted: 08/20/2007] [Indexed: 11/25/2022]
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