Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

An Improved Parallelized mRMR for Gene Subset Selection in Cancer Classification

DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the cancer subtypes due to most cancers being highly related to the specific biological insight.  Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods

An improved parallelized mRMR for gene subset selection in cancer classification

DNA microarray technique has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on morphological appearance of the tumor. The limitations of this approach are bias in identify the tumors by expert and faced the difficulty in differentiate the cancer subtypes due to most cancers being highly related to the specific biological insight. Thus, this study propose an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods

Identifying metabolic pathway within microarray gene expression data using combination of probabilistic models

Extracting metabolic pathway that dictates a specific biological response is currently one of the important disciplines in metabolic system biology research. Previous methods have successfully identified those pathways but without concerning the genetic effect and relationship of the genes, the underlying structure is not precisely represented and cannot be justified to be significant biologically. In this article, probabilistic models capable of identifying the significant pathways through metabolic networks that are related to a specific biological response are implemented. This article utilized combination of two probabilistic models, using ranking, clustering and classification techniques to address limitations of previous methods with the annotation to Kyoto Encyclopedia of Genes and Genomes (KEGG) to ensure the pathways are biologically plausible

Metabolic pathway extraction using combined probabilistic models

Extracting metabolic pathway from microarray gene expression data that dictates a specific biological response is currently one of the important disciplines in system biology research. However due to the complexity of the global metabolic network and the importance to maintain the biological structure, this has become a greater challenge. Previous methods have successfully identified those pathways but without concerning the genetic effect and relationship of the genes, representation of the underlying structure is not precise and cannot be justified to be significant biologically. In this article, probabilistic models that are capable of identifying the significant pathways through metabolic networks related to a specific biological response are implemented. This article utilized combination of two probabilistic models to address the limitations of previous methods with the annotation to pathway database to ensure the pathway is biologically plausible

SYNTHESIS OF CARAN ALDEHYDE AND COMPOUNDS OF CARAN SPECIES FROM 3-CARENE OXIDATION PRODUCTS

The liquid-phase catalytic oxidation process of 3-carene by an atmosphere oxygen has been studied. The reactions on the ozonization of the caran species oxygen-containing derivatives have been investigated. The technological synthesis schema of the caron aldehyde - semiproduct in the synthesis of the pyretroids has been developed. The procedures for creation of the caran species chiral compounds on base of the 3-carene oxidation products have been proposedAvailable from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio

Parameter estimation for simulation of glycolysis pathway by using an improved differential evolution

An improved differential evolution (DE) algorithm is proposed in this paper to optimize its performance in estimating the germane parameters for metabolic pathway data to simulate glycolysis pathway for Saccharomyces cerevisiae. This study presents an improved algorithm of parameter sensitivity test into the process of DE algorithm. The result of the improved algorithm is testifying to be supreme to the others estimation algorithms. The outcomes from this study promote estimating optimal kinetic parameters, shorter computation time and ameliorating the precision of simulated kinetic model for the experimental data

Selecting a small subset of informative genes from gene expression data by using a modified binary particle swarm optimisation

Gene expression technology, especially micro arrays, can be used to measure the expression levels of thousands of genes simultaneously in biological organisms. Gene expression data produced by micro arrays are expected to be useful for cancer classification. To select a small subset of informative genes for cancer classification, many researchers have analysed the gene expression data using various computational intelligence methods. However, due to the small number of samples compared with the huge number of genes (high-dimensional data), irrelevant genes, and noisy genes, many of the computational methods face difficulties in selecting the small subset. Thus, we propose a modified binary particle swarm optimisation to select a small subset of informative genes that are relevant for the cancer classification. In the proposed method, we introduce the particle speed and a rule for increasing the probability of bits in a particle's position to be zero. The method was empirically applied to a suite of four well-known benchmark gene expression data sets. The experimental results demonstrate that the proposed method outperforms the conventional version of binary particle swarm optimisation (BPSO) and other related works in terms of classification accuracy and the number of selected genes. In addition, this method also produces lower running times compared to BPSO

Identifying gene knockout strategies using a hybrid of bees algorithm and flux balance analysis for in silico optimization of microbial strains

Genome-scale metabolic networks reconstructions from different organisms have become popular in recent years. Genetic engineering is proven to be able to obtain the desirable phenotypes. Optimization algorithms are implemented in previous works to identify the effects of gene knockout on the results. However, the previous works face the problem of falling into local minima. Thus, a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) is proposed in this paper to solve the local minima problem and to predict optimal sets of gene deletion for maximizing the growth rate of certain metabolite. This paper involves two case studies that consider the production of succinate and lactate as targets, by using E.coli as model organism. The results from this experiment are the list of knockout genes and the growth rate after the deletion. BAFBA shows better results compared to the other methods. The identified list suggests gene modifications over several pathways and may be useful in solving challenging genetic engineering problems