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Network component analysis (NCA) and other methods based on the NCA model have become powerful bioinformatics tools to reconstruct underlying regulatory networks and recover hidden biological processes. However, due to the existence of experimental noises in micro array data and false information in network connectivity data (e.g., ChIP-on-chip binding data, motif information, etc.), it still remains...
DNA copy number change is an important form of structural variations in human genomes. Detecting copy number changes using DNA array data is a challenging task due to high density genomic loci, low signal to noise ratios, and normal tissue contamination. We propose fused margin regression (FMR) method that combines a variable fusion rule and robust epsilon-insensitive loss criterion to approximate...
Genome-wide association studies (GWAS) have been widely applied to identify informative SNPs associated with common and complex diseases. Besides single-SNP analysis, the interaction between SNPs is believed to play an important role in disease risk due to the complex networking of genetic regulations. While many approaches have been proposed for detecting SNP interactions, the relative performance...
One of the major goals in microarray data analysis is to identify biomarkers and build a classification model for future prediction. Many traditional statistical models, based on microarray data alone, often fail in identifying biologically meaningful genes, which should have synergistic effect on determine the clinical outcomes through some interactions rather than work individually. In this paper,...
In studying the relationship between risk factors and breast cancer, the growth patterns of fat pads and glandular tissues are considered as important biomarkers. The aim of this study is to measure the growth pattern statistics of rat mammary pads and glandular tissues with magnetic resonance (MR) time sequence images. In this paper, we proposed methods containing sequential steps to extract and...
In this paper we investigate application of the recently developed margin-based feature elimination (MFE) method for feature selection in support vector machines to high-dimensional, small sample size data from the DNA microarray domain. We compared the performance of MFE to the well-known recursive feature elimination (RFE) method. Our results show that MFE outperforms RFE in terms of generalization...
Gene expression programming (GEP) is a new member of evolutionary computation family, and is successful in symbolic regression and function finding in the field of data mining. However, GEP is difficult to find power functions with high ranks. To tackle this problem, this study proposes a novel GEP algorithm named HDN-GEP. The main contributions include: (1) a new structure named HDN (high density...
Many statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study from expression data alone. In this paper, we develop a novel strategy, namely knowledge-driven multi-level independent component analysis (ICA), to infer regulatory signals and identify biologically relevant biomarkers from microarray data. Specifically, based on multi-level...
The design principles of gene transcriptional regulation networks in cells have been puzzles due to their unknown dynamic and nonlinear mechanisms. Although high-throughput biotechnologies have generated unprecedented amounts of data, the integration of multi-source data to better understand the process of gene regulation has been a challenge in post genomics era. Gene expression data are limited...
In this paper, we report a new gene clustering approach, non-negative independent component analysis (nICA), for microarray data analysis. Due to positive nature of molecular expressions, nICA fits better to the reality of corresponding putative biological processes. In conjunction with nICA model, visual statistical data analyzer (VISDA) is applied to group genes into modules in the latent variable...
We present a novel algorithm combining artificial neural networks and swarm intelligence (SI) methods to infer network interactions. The algorithm uses ant colony optimization (ACO) to identify the optimal architecture of a recurrent neural network (RNN), while the weights of the RNN are optimized using particle swarm optimization (PSO). Our goal is to construct an RNN that mimics the true structure...
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