Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy
نویسندگان
چکیده
منابع مشابه
Acute Toxicity-Supported Chronic Toxicity Prediction: A k-Nearest Neighbor Coupled Read-Across Strategy
A k-nearest neighbor (k-NN) classification model was constructed for 118 RDT NEDO (Repeated Dose Toxicity New Energy and industrial technology Development Organization; currently known as the Hazard Evaluation Support System (HESS)) database chemicals, employing two acute toxicity (LD50)-based classes as a response and using a series of eight PaDEL software-derived fingerprints as predictor var...
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1 A Strategy for Structuring and Reporting 2 a Read-Across Prediction of Toxicity 3 4 T.W. Schultz, P. Amcoff, E. Berggren, F. Gautier, M. Klaric, D. J. Knight, C. Mahony, M. 5 Schwarz, A. White and M.T.D. Cronin 6 The University of Tennessee, College of Veterinary Medicine, Knoxville, TN, 37996-4500, 7 USA. e-mail: tschultz @utk.edu; 8 Cosmetics Europe, Avenue Herrmann Debroux 40, 1160 Brussel...
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Category formation, grouping and read across methods are broadly applicable in toxicological assessments and may be used to fill data gaps for chemical safety assessment and regulatory decisions. In order to facilitate a transparent and systematic approach to aid regulatory acceptance, a strategy to evaluate chemical category membership, to support the use of read-across predictions that may be...
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ML-kNN is a well-known algorithm for multi-label classification. Although effective in some cases, ML-kNN has some defect due to the fact that it is a binary relevance classifier which only considers one label every time. In this paper, we present a new method for multi-label classification, which is based on lazy learning approaches to classify an unseen instance on the basis of its k nearest ...
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ژورنال
عنوان ژورنال: International Journal of Molecular Sciences
سال: 2015
ISSN: 1422-0067
DOI: 10.3390/ijms160511659