The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) sub-types classification, we propose a novel integrative scheme utilizing PPI network, functional gene sets information, and mRNA profiling. The workflow of the proposed scheme includes three major steps: First, by combining protein-protein interaction network structure and gene co-expression relationship into...
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...
One of the most challenging points in studying human common complex diseases is to search for both strong and weak susceptibility single-nucleotide polymorphisms (SNPs) and identify forms of genetic disease models. Currently, a number of methods have been proposed for this purpose. Many of them have not been validated through applications into various genome datasets, so their abilities are not clear...
cDNA microarray expression data is widely used to help biomedical research. The data must be normalized because of various error functioned interferences existed. This paper has discussed the normalization for supervised multi-class (phenotype) data. All the classes are the type of multi-sample. Also, a reasonable hybrid cross-phenotype normalization (CPN) algorithm based on iterative nonlinear regression...
This paper proposed a graph-based clustering approach for gene expression data. The new method is based on regulatory network graph obtained from gene expression data. Clustering is performed based on the topological features of the graph which characterizes the regulatory relationships between genes, which is different from the conventional methods that simply group genes with similar gene expression...
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,...
A linear mixture model of non-negative sources is used to dissect the gene expression data into components that are putative underlying active biological processes. Each biological process/component is characterized by its specific genes that are exclusively highly expressed in it and expected to be functional enriched; while a majority of all the genes maintain basic cellular structure and functions...
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...
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...
Gene module discovery can provide comprehensive molecular portrait of biological regulation and functional genomics. We present a new analytic strategy - nonnegative independent component analysis to reveal some gene module composite. The results show that by grouping genes in the latent space, we can find statistically more significant enrichment of gene annotations within clusters. Further, this...
For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic...
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...
For the critical task of gene module discovery in genomic research, we present a model-based hierarchical data clustering and visualization algorithm, visual statistical data analyzer (VISDA), which effectively exploits human-data interaction to improve the clustering outcome. Guided by a diagnostic tree, we apply VISDA to a muscular dystrophy dataset that contains a number of different phenotypic...
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...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.