DTSP-V: A trend-based Top Scoring Pairs method for classification of time series gene expression data
Kaimin Wu, Xiaofei Nan, Yumei Chai, Liming Wang, Kun Li · 2016
Time series gene expression is a genetic data collected at different time points in the process of biological growth. As it contains a large amount of biological information related to a specific time period, the study of classification of time series gene data is a very significant task. With low sample size and high dimensionality, gene expression data classified by traditional machine learning methods suffers not only the curse of dimensionality but also lack of the interpretability of the complex model derived from the data. Inspired by the idea of top gene pair comparison, we propose the algorithm of Dynamic Top Scoring Pairs on Variance (DTSP-V), and introduce the concept of trend to regularize the variances between time stamps. In order to make full use of the advantages of the Top Scoring Pairs algorithm, we use the idea of trend to process time series gene. DTSP-V calculation can classify time series data and select one or more pairs of features with the most classification ability simultaneously. Experiments show that in comparison with traditional machine learning algorithms, DTSP-V algorithm, achieves high classification accuracy, and more importantly, provides explanatory computational model that valuable biological information could be extracted.