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The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data

Liver cancer is a serious threat to public health and has fairly complicated pathogenesis. Therefore, the identification of key genes and pathways is of much importance for clarifying molecular mechanism of hepatocellular carcinoma (HCC) initiation and progression. HCC-associated gene expression dataset was downloaded from Gene Expression Omnibus database. Statistical software R ...

A comparative study of k-spectrum-based error correction methods for next-generation sequencing data analysis

Background Innumerable opportunities for new genomic research have been stimulated by advancement in high-throughput next-generation sequencing (NGS). However, the pitfall of NGS data abundance is the complication of distinction between true biological variants and sequence error alterations during downstream analysis. Many error correction methods have been developed to correct ...

Gene regulatory network inference and validation using relative change ratio analysis and time-delayed dynamic Bayesian network

The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and ...

SeqAssist: a novel toolkit for preliminary analysis of next-generation sequencing data

Background While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software ...

Differential reconstructed gene interaction networks for deriving toxicity threshold in chemical risk assessment

Background Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and ...

Identification of biomarkers that distinguish chemical contaminants based on gene expression profiles

Background High throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. Here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. Results In this study, in vitro cultures of primary rat hepatocytes ...

Genome-Wide Gene Expression Profiles in Lung Tissues of Pig Breeds Differing in Resistance to Porcine Reproductive and Respiratory Syndrome Virus

Porcine reproductive and respiratory syndrome (PRRS) caused by PRRS virus (PRRSV) is an infectious disease characterized by severe reproductive deficiency in pregnant sows, typical respiratory symptoms in piglets, and high mortality rate of piglets. In this study, we employed an Affymetrix microarray chip to compare the gene expression profiles of lung tissue samples from Dapulian ...

Learning the structure of gene regulatory networks from time series gene expression data

Background Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering ALgorithm) is one of the algorithms implemented for learning DBN structure and used to reconstruct gene regulatory ...

State Space Model with hidden variables for reconstruction of gene regulatory networks

Background State Space Model (SSM) is a relatively new approach to inferring gene regulatory networks. It requires less computational time than Dynamic Bayesian Networks (DBN). There are two types of variables in the linear SSM, observed variables and hidden variables. SSM uses an iterative method, namely Expectation-Maximization, to infer regulatory relationships from microarray ...

RefNetBuilder: a platform for construction of integrated reference gene regulatory networks from expressed sequence tags

Background Gene Regulatory Networks (GRNs) provide integrated views of gene interactions that control biological processes. Many public databases contain biological interactions extracted from experimentally validated literature reports, but most furnish only information for a few genetic model organisms. In order to provide a bioinformatic tool for researchers who work with ...

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

Background The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational ...

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 ...

Time lagged information theoretic approaches to the reverse engineering of gene regulatory networks

Gong Chaoyang Zhang 0 0 School of Computing, The University of Southern Mississippi , MS 39402 , USA Background: A number of models and algorithms have been proposed in the past for gene regulatory

Development of computations in bioscience and bioinformatics and its application: review of the Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

The first symposium of computations in bioinformatics and bioscience (SCBB06) was held in Hangzhou, China on June 21–22, 2006. Twenty-six peer-reviewed papers were selected for publication in this special issue of BMC Bioinformatics. These papers cover a broad range of topics including bioinformatics theories, algorithms, applications and tool development. The main technical topics ...

A novel gene network inference algorithm using predictive minimum description length approach

Vijender Chaitankar 0 Preetam Ghosh 0 Edward J Perkins Ping Gong Youping Deng Chaoyang Zhang 0 0 School of Computing, University of Southern Mississippi , MS 39402 , USA Background: Reverse

Identification of new members of hydrophobin family using primary structure analysis

Background Hydrophobins are fungal proteins that can turn into amphipathic membranes at hydrophilic/hydrophobic interfaces by self-assembly. The assemblages by Class I hydrophobins are extremely stable and possess the remarkable ability to change the polarity of the surface. One of its most important industrial applications is its usage as paint. Without detailed knowledge of the ...

Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data

Background Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the process of generating models in traditional multicategory support vector machines for large datasets is very computationally intensive, there is a need to improve the performance using high performance ...

Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition

Background Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein's subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid ...

Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks

Background The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for ...