Artificial Intelligence and Bioinformatics Research Group

Johor Bahru, Malaysia

Artificial Intelligence and Bioinformatics Research Group

Johor Bahru, Malaysia
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Yi L.S.,Artificial Intelligence and Bioinformatics Research Group | Chin T.L.,Artificial Intelligence and Bioinformatics Research Group | Mohamad M.S.,Artificial Intelligence and Bioinformatics Research Group | Deris S.,University of Malaysia, Kelantan | And 2 more authors.
Mini-Reviews in Organic Chemistry | Year: 2015

Metabolic pathway analysis has become significant for evaluating intrinsic network characteristics in biochemical reaction network reconstruction. Current applications of metabolic pathway analysis involve identifying the enzyme for the desired production, identifying pathways of optimal production, determining non-redundant pathways for drug design, and genome comparisons by alignment of pathways for missing genes identification. With the expanded application of bioinformatics, more organized methods have been introduced to examine the overall metabolic networks and network reconstruction based on genomic data. There are several in silico approaches for analysing metabolic pathways, including elementary mode analysis and extreme pathway under pathway topology analysis, flux balance analysis and metabolic flux analysis under analysis of metabolic fluxes, and metabolic control analysis. In this paper, elementary mode analysis, flux balance analysis, metabolic flux analysis and metabolic control analysis are reviewed, together with their application in metabolic network reconstruction and biological production enhancement in biological organisms. Next, a comparison of strengths and weaknesses between each of the metabolic pathway analysis methods is presented in this paper. © 2015 Bentham Science Publishers.


Moorthy K.,Artificial Intelligence and Bioinformatics Research Group | Mohamad M.S.,Artificial Intelligence and Bioinformatics Research Group | Deris S.,Artificial Intelligence and Bioinformatics Research Group | Ibrahim Z.,University of Technology Malaysia
ICIC Express Letters | Year: 2012

Microarray ade4 or known as MADE4 is a multivariate software analysis package for microarray gene expression data. This software package is capable of accepting wide variety of gene expression data formats such as Bioconductor Affy Batch and exprSet. This MADE4 R package extends the advantages of ade4 package in multivariate statistical and graphical functions for the use in the microarray data application. Moreover, MADE4 provides new graphical and visualization tools that assist in the interpretation of multivariate analysis of microarray data. Besides that, LLSimpute algorithm has been incorporated to assist in handling of datasets with missing values and this has eased the application for the users to analysis on gene expression data that contain missing values.


Misman M.F.,Artificial Intelligence and Bioinformatics Research Group | Mohamad M.S.,Artificial Intelligence and Bioinformatics Research Group | Deris S.,Artificial Intelligence and Bioinformatics Research Group | Hashim S.Z.M.,Artificial Intelligence and Bioinformatics Research Group | And 2 more authors.
ICIC Express Letters | Year: 2012

A hybrid of support vector machines and a smoothly clipped absolute deviation with group-specific penalty terms (gSVM-SCAD) is a penalized classifier that has been used to identify and select significant pathways in pathway-based microarray analysis. Despite its advantages in identifying significant pathways, the gSVM-SCAD has some limitations, as it depends on the proper choice of tuning parameter. If the tuning parameter is too small, it can bring little sparsity and overfit to the classifier model, while if it is too large, it can make much sparsity to the classifier model and produce poor discriminating power. Therefore, it is important to choose an appropriate tuning parameter selector method for the gSVM-SCAD. The generalized cross validation (GACV) has been widely used as a tuning parameter selector method. Unfortunately, GACV has some limitations where it poorly performs when dealing with the low number of variables (in this paper referred as genes) and large sample sizes. This is because some pathways contain not more than 100 genes and even some pathways contain less than 10 genes. This scenario can lead to the poor performance of SCAD in selecting the informative genes and simultaneously identifying significant ones. In order to surmount the limitations of the gSVM-SCAD, we proposed to use the B-type generalized approximate cross validation (BGACV) as a tuning parameter selector method for gSVM-SCAD. ICIC International © 2012.

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