Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions

Tung, Chun-Wei

doi:10.1007/978-3-319-09192-1_1

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…The DeepCarc model was designed to predict the general carcinogens, which are non-organ specific. We investigated other reported machine learning-based prediction models with the NCTRlcdb data set ( Liu et al, 2011 ; Tung, 2013 ; Tung, 2014 ; Beger et al, 2004 ). However, all the other reported prediction models aim to discriminate liver-specific carcinogens from others.…”

Section: Discussionmentioning

confidence: 99%

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation

Tong

Roberts

et al. 2021

Front. Artif. Intell.

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Carcinogenicity testing plays an essential role in identifying carcinogens in environmental chemistry and drug development. However, it is a time-consuming and label-intensive process to evaluate the carcinogenic potency with conventional 2-years rodent animal studies. Thus, there is an urgent need for alternative approaches to providing reliable and robust assessments on carcinogenicity. In this study, we proposed a DeepCarc model to predict carcinogenicity for small molecules using deep learning-based model-level representations. The DeepCarc Model was developed using a data set of 692 compounds and evaluated on a test set containing 171 compounds in the National Center for Toxicological Research liver cancer database (NCTRlcdb). As a result, the proposed DeepCarc model yielded a Matthews correlation coefficient (MCC) of 0.432 for the test set, outperforming four advanced deep learning (DL) powered quantitative structure-activity relationship (QSAR) models with an average improvement rate of 37%. Furthermore, the DeepCarc model was also employed to screen the carcinogenicity potential of the compounds from both DrugBank and Tox21. Altogether, the proposed DeepCarc model could serve as an early detection tool (https://github.com/TingLi2016/DeepCarc) for carcinogenicity assessment.

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Section: Discussionmentioning

confidence: 99%

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation

Tong

Roberts

et al. 2021

Front. Artif. Intell.

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“…The decision tree algorithm J48, also known as C4.5 algorithm22, is a tree-based classifier that has been extensively applied in related bioinformatics problems such the identification of biomarkers23 and the prediction of NGHC242526. In this study, the default confidence parameters of the confidence threshold and minimal number of samples in the left node were utilized for tree pruning to avoid overfitting.…”

Section: Methodsmentioning

confidence: 99%

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

Huang

Tung

2017

Sci Rep

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The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation.

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“…The prediction performance for each feature subset is evaluated by the PCR model using LOOCV. The sequential feature selection algorithms are simple yet powerful methods that have been successfully applied in several biological problems including pupylation sites [ 27 ], esophageal squamous cell carcinoma [ 28 ] and Ames-negative hepatocarcinogens [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

Identification of informative features for predicting proinflammatory potentials of engine exhausts

Lin

Jhang

et al. 2017

BioMed Eng OnLine

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BackgroundThe immunotoxicity of engine exhausts is of high concern to human health due to the increasing prevalence of immune-related diseases. However, the evaluation of immunotoxicity of engine exhausts is currently based on expensive and time-consuming experiments. It is desirable to develop efficient methods for immunotoxicity assessment.MethodsTo accelerate the development of safe alternative fuels, this study proposed a computational method for identifying informative features for predicting proinflammatory potentials of engine exhausts. A principal component regression (PCR) algorithm was applied to develop prediction models. The informative features were identified by a sequential backward feature elimination (SBFE) algorithm.ResultsA total of 19 informative chemical and biological features were successfully identified by SBFE algorithm. The informative features were utilized to develop a computational method named FS-CBM for predicting proinflammatory potentials of engine exhausts. FS-CBM model achieved a high performance with correlation coefficient values of 0.997 and 0.943 obtained from training and independent test sets, respectively.ConclusionsThe FS-CBM model was developed for predicting proinflammatory potentials of engine exhausts with a large improvement on prediction performance compared with our previous CBM model. The proposed method could be further applied to construct models for bioactivities of mixtures.

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Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions

Cited by 10 publications

References 20 publications

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation

DeepCarc: Deep Learning-Powered Carcinogenicity Prediction Using Model-Level Representation

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

Identification of informative features for predicting proinflammatory potentials of engine exhausts

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