An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

Abstract

BackgroundOne of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.ResultsHere we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences.ConclusionThe high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well.

Transcriptional signatures of regulatory and toxic responses to benzo-[a]-pyrene exposure

“…We demonstrate that machine learning can be used for identifying these characteristic signatures and for subsequently classifying genes as to whether they are primary Ahr -dependent targets or indirectly affected (BPDE-dependent) genes. This general strategy of using time course gene expression data to predict transcriptional regulatory roles has been previously explored [ 11 - 14 ], although primarily in lower organisms such as bacteria and yeast.…”

High-throughput next-generation sequencing technologies foster new cutting-edge computing techniques in bioinformatics

“…Corresponding author Uversky presented this important research and discoveries in the keynote lecture. Jianhua Ruan, Youping Deng, Weixiong Zhang [ 8 ] presented an ensemble learning approach to the problem of reverse-engineering transcriptional regulatory networks using time-series gene expression data. Hong Zhou et al [ 9 ] studied energy profile and secondary structure that impact shRNA Efficacy.…”

A Top-Performing Algorithm for the DREAM3 Gene Expression Prediction Challenge

“…Answers to these two questions are fundamentally important in many gene expression-based studies. Furthermore, many methods have been proposed for constructing gene regulatory networks [10] – [17] , which are bases for modeling gene expression. Results of this challenge problem may tell us whether the current gene regulatory network models are sufficiently accurate to make quantitative predictions.…”

“…Popular gene regulatory network models include Bayesian networks [10] – [12] , Boolean networks [13] , and regression/classification-based models [14] – [17] . These methods can model the expression level of a gene by the expression levels of other genes [10] , [13] , [15] , [16] , by the presence or absence of TF binding sites on its promoter sequences [12] , [14] , [17] , or a combination of the two types of information [18] – [20] . For this particular challenge problem, these methods can all potentially be applied, as most of them have been developed based on yeast data, and participants are allowed to use additional data beyond what was provided by the DREAM organizers.…”