ArticleOncotarget2017
Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.
Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.Plant methods · 2022Article
- Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.International journal of molecular sciences · 2022Article
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- Data Prediction of Mobile Network Traffic in Public Scenes by SOS-Sensors (Basel, Switzerland) · 2020Article
- Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean.Frontiers in plant science · 2020Article
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- Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers.Scientific reports · 2018Article
- Synthesis of novel (E)-2-(4-(1H-1,2,4-triazol-1-yl)styryl)-4- (alkyl/arylmethyleneoxy)quinazoline derivatives as antimicrobial agents.Molecular diversity · 2018Article
- Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate.International journal of molecular sciences · 2018Article
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- 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.Oncotarget · 2017Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) study was performed to develop a computational model for correlating the structures of 581 aromatic compounds with their aquatic toxicity to tetrahymena pyriformis. A set of 68 molecular descriptors derived solely from the structures of the aromatic compounds were calculated based on Gaussian 03, HyperChem 7.5, and TSAR V3.3. A comprehensive feature selection method, minimum Redundancy Maximum Relevance (mRMR)-genetic algorithm (GA)-support vector regression (SVR) method, was applied to select the best descriptor subset in QSAR analysis. The SVR method was employed to model the toxicity potency from a training set of 500 compounds. Five-fold cross-validation method was used to optimize the parameters of SVR model. The new SVR model was tested on an independent dataset of 81 compounds. Both high internal consistent and external predictive rates were obtained, indicating the SVR model is very promising to become an effective tool for fast detecting the toxicity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.