Prediction of Non-genotoxic Hepatocarcinogenicity Using Chemical-Protein Interactions

Tung, Chun-Wei

doi:10.1007/978-3-642-39159-0_21

Cited by 12 publications

(5 citation statements)

References 35 publications

(43 reference statements)

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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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“…Decision tree algorithms are useful methods to generate interpretable rules based on gene expressions for ESCC classification that are widely used in various classification and regression problems such as immunogenic peptides [ 17 ], promoters [ 18 ], and nongenotoxic hepatocarcinogenicity [ 19 ]. In this study, a decision tree method J48 implemented in WEKA [ 20 ], also known as C4.5 [ 21 ], is applied to construct decision tree classifiers and derive interpretable rules.…”

Section: Methodsmentioning

confidence: 99%

Identification of Biomarkers for Esophageal Squamous Cell Carcinoma Using Feature Selection and Decision Tree Methods

Tung

Chen

et al. 2013

The Scientific World Journal

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Esophageal squamous cell cancer (ESCC) is one of the most common fatal human cancers. The identification of biomarkers for early detection could be a promising strategy to decrease mortality. Previous studies utilized microarray techniques to identify more than one hundred genes; however, it is desirable to identify a small set of biomarkers for clinical use. This study proposes a sequential forward feature selection algorithm to design decision tree models for discriminating ESCC from normal tissues. Two potential biomarkers of RUVBL1 and CNIH were identified and validated based on two public available microarray datasets. To test the discrimination ability of the two biomarkers, 17 pairs of expression profiles of ESCC and normal tissues from Taiwanese male patients were measured by using microarray techniques. The classification accuracies of the two biomarkers in all three datasets were higher than 90%. Interpretable decision tree models were constructed to analyze expression patterns of the two biomarkers. RUVBL1 was consistently overexpressed in all three datasets, although we found inconsistent CNIH expression possibly affected by the diverse major risk factors for ESCC across different areas.

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Prediction of Non-genotoxic Hepatocarcinogenicity Using Chemical-Protein Interactions

Cited by 12 publications

References 35 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 Biomarkers for Esophageal Squamous Cell Carcinoma Using Feature Selection and Decision Tree Methods

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