Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Multi-criterion optimization for genetic network modeling
Signal Processing - Special issue: Genomic signal processing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Analyzing time series gene expression data
Bioinformatics
Fusing microarray experiments with multivariate regression
Bioinformatics
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
The Journal of Machine Learning Research
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constructing gene regulatory networks from microarray data using GA/PSO with DTW
Applied Soft Computing
eXploratory K-Means: A new simple and efficient algorithm for gene clustering
Applied Soft Computing
| Hi-index | 0.00 |
To unravel the controlling mechanisms of gene regulation, in this paper we present the application of sophisticated soft computing methods applied on an important problem from Bioinformatics-inferring gene regulatory networks (GRN) from time series gene expression microarray data. The main questions addressed in this paper are: (a) what knowledge can be derived from different models? (b) Would an integrated approach be more suitable to reveal about the controls of gene regulation? To reduce the number of genes in addition to apply the appropriate clustering methods, here we have also considered the valuable inputs from the biological experiments. To infer the GRN we have applied: three computational intelligence methods-Least Angle Regression (LARS), Expectation Maximization (EM) with Kalman Filter (KF), and an Evolving Fuzzy Neural Network (EFuNN). The methods are applied on time series microarray data of Schizosaccharomyces pombe yeast cell-cycle genes. Each method reveals some new aspects of the problem and it is agreed that to infer the GRN and to understand the processes behind gene regulation it is more suitable to adopt such integrative approach as ours through which some new knowledge is discovered, such as: using LARS we hypothesize-first, an exoglucanase gene exg1 is now implicated to be tied with MCB cluster regulation and second, a mannosidase with histone linked mannoses. A new quantitative prediction is that the time delay of the interaction between two genes seems to be approximately 30min, or 0.17 cell cycles. Using the method of EM with KF, 25 cell cycle-regulated key genes were successfully clustered into three functionally co-regulated groups. We have also identified two genes namely Cdc22 and Suc22 that indeed interact with each other and are the potential candidates as a control in Ribonucleotide reductase (RNR) activity. Based on the EFuNN results and integrating knowledge from EM-KF method, we hypothesize that interaction between Suc22, Cdc22 and Mrc1 may be mediated by two other genes namely Cds1 and Spd1. The methods discussed and applied here can be used to analyze any kind of short time series of many interacting variables for inferring the regulatory network. Researchers should take such integrative computational intelligence approach seriously to understand the complex phenomenon of gene regulation and thus to simulate the development of the cell.