title: Machine learning algorithms for mode-of-action classification in toxicity assessment.
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.
authors.affiliation: Department of Mathematical and Statistical Science, University of Alberta, T6G 2G1, Edmonton, Canada.
authors.forename: Yile
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Department of Mathematical and Statistical Science, University of Alberta, T6G 2G1, Edmonton, Canada.
authors.forename: Yau Shu
authors.initials: YS
authors.lastname: Wong
authors.affiliation: Department of Mathematical and Statistical Science, University of Alberta, T6G 2G1, Edmonton, Canada.
authors.forename: Jian
authors.initials: J
authors.lastname: Deng
authors.affiliation: Department of Mathematics and Statistics, Grant MacEwan University, T5P 2P7, Edmonton, Canada.
authors.forename: Cristina
authors.initials: C
authors.lastname: Anton
authors.affiliation: Department of Laboratory Medicine and Pathology, University of Alberta, T6G 2B7, Edmonton, Canada.
authors.forename: Stephan
authors.initials: S
authors.lastname: Gabos
authors.affiliation: Alberta Health, T5J 1S6, Edmonton, Canada.
authors.forename: Weiping
authors.initials: W
authors.lastname: Zhang
authors.affiliation: Alberta Centre for Toxicology, University of Calgary, T2N 4N1, Calgary, Canada.
authors.forename: Dorothy Yu
authors.initials: DY
authors.lastname: Huang
authors.affiliation: AACEA Biosciences Inc, San Diego, 92121 USA.
authors.forename: Can
authors.initials: C
authors.lastname: Jin
keywords: Artificial neural network; Dose response curve; Machine learning; Mode of action; Support vector machine; Time-concentrations response curve; Wavelet transform