Machine learning algorithms for mode-of-action classification in toxicity assessment. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Zhang, Yile; Wong, Yau; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy; Jin, Can
• Is Part Of: Biodata mining. Volume 9:Issue 1(2016)
• Page Start: 1
• Page End: 21
• Publication Date: 2016-12
• Subjects: Time-concentrations response curve -- Mode of action -- Wavelet transform -- Dose response curve -- Machine learning -- Support vector machine -- Artificial neural network; Bioinformatics -- Periodicals; Computational biology -- Periodicals; Data mining -- Periodicals; 570.285
• DOI: 10.1186/s13040-016-0098-0
• Languages: English
• ISSNs: 1756-0381

Abstract

Abstract Background Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. Results In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Conclusions Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.