Displaying publications 1 - 20 of 28 in total

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  1. Fulltext An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters
    Abdullah A, Deris S, Anwar S, Arjunan SN
    PLoS One, 2013;8(3):e56310.
    PMID: 23469172 DOI: 10.1371/journal.pone.0056310
    The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.
  2. Fulltext An improved swarm optimization for parameter estimation and biological model selection
    Abdullah A, Deris S, Mohamad MS, Anwar S
    PLoS One, 2013;8(4):e61258.
    PMID: 23593445 DOI: 10.1371/journal.pone.0061258
    One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.
  3. Fulltext The inference of breast cancer metastasis through gene regulatory networks
    Ahmad FK, Deris S, Othman NH
    J Biomed Inform, 2012 Apr;45(2):350-62.
    PMID: 22179053 DOI: 10.1016/j.jbi.2011.11.015
    Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from microarray data. Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate. Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size. This method has been evaluated and compared with full-order conditional independence and different prognostic indices on a publicly available breast cancer data set. Our results suggest that the low-order conditional independence method will be able to handle a large number of genes in a small sample size with the least mean square error. In addition, this proposed method performs significantly better than other methods, including the full-order conditional independence and the St. Gallen consensus criteria. The proposed method achieved an area under the ROC curve of 0.79203, whereas the full-order conditional independence and the St. Gallen consensus criteria obtained 0.76438 and 0.73810, respectively. Furthermore, our empirical evaluation using the low-order conditional independence method has demonstrated a promising relationship between six gene regulators and two regulated genes and will be further investigated as potential breast cancer metastasis prognostic markers.
  4. A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models
    Arif MA, Mohamad MS, Abd Latif MS, Deris S, Remli MA, Mohd Daud K, et al.
    Comput Biol Med, 2018 11 01;102:112-119.
    PMID: 30267898 DOI: 10.1016/j.compbiomed.2018.09.015
    Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substance. The modification can be made using in silico gene knockout simulation that is able to predict and analyse the disrupted genes which may enhance the metabolites production. Global optimization algorithms have been widely used for identifying gene knockout strategies. However, their productions were less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models which are high dimensional and contain thousands of reactions. In this paper, a hybrid algorithm of Cuckoo Search and Minimization of Metabolic Adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, Biomass Product Couple Yield, and computation time.
  5. A review on the computational approaches for gene regulatory network construction
    Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z
    Comput Biol Med, 2014 May;48:55-65.
    PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011
    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
  6. Investigating the effects of imputation methods for modelling gene networks using a dynamic bayesian network from gene expression data
    Chai LE, Law CK, Mohamad MS, Chong CK, Choon YW, Deris S, et al.
    Malays J Med Sci, 2014 Mar;21(2):20-7.
    PMID: 24876803 MyJurnal
    BACKGROUND: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN).

    METHODS: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error.

    RESULTS: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset).

    CONCLUSION: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes.

  7. Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme
    Chan WH, Mohamad MS, Deris S, Zaki N, Kasim S, Omatu S, et al.
    Comput Biol Med, 2016 10 01;77:102-15.
    PMID: 27522238 DOI: 10.1016/j.compbiomed.2016.08.004
    Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be included when the pathway data is used for analysis of context specific data like cancer microarray data. Therefore, efficient identification of informative genes is inevitable. Embedded methods like penalized classifiers have been used for microarray analysis due to their embedded gene selection. This paper proposes an improved penalized support vector machine with absolute t-test weighting scheme to identify informative genes and pathways. Experiments are done on four microarray data sets. The results are compared with previous methods using 10-fold cross validation in terms of accuracy, sensitivity, specificity and F-score. Our method shows consistent improvement over the previous methods and biological validation has been done to elucidate the relation of the selected genes and pathway with the phenotype under study.
  8. A hybrid of ant colony optimization and minimization of metabolic adjustment to improve the production of succinic acid in Escherichia coli
    Chong SK, Mohamad MS, Mohamed Salleh AH, Choon YW, Chong CK, Deris S
    Comput Biol Med, 2014 Jun;49:74-82.
    PMID: 24763079 DOI: 10.1016/j.compbiomed.2014.03.011
    This paper presents a study on gene knockout strategies to identify candidate genes to be knocked out for improving the production of succinic acid in Escherichia coli. Succinic acid is widely used as a precursor for many chemicals, for example production of antibiotics, therapeutic proteins and food. However, the chemical syntheses of succinic acid using the traditional methods usually result in the production that is far below their theoretical maximums. In silico gene knockout strategies are commonly implemented to delete the gene in E. coli to overcome this problem. In this paper, a hybrid of Ant Colony Optimization (ACO) and Minimization of Metabolic Adjustment (MoMA) is proposed to identify gene knockout strategies to improve the production of succinic acid in E. coli. As a result, the hybrid algorithm generated a list of knockout genes, succinic acid production rate and growth rate for E. coli after gene knockout. The results of the hybrid algorithm were compared with the previous methods, OptKnock and MOMAKnock. It was found that the hybrid algorithm performed better than OptKnock and MOMAKnock in terms of the production rate. The information from the results produced from the hybrid algorithm can be used in wet laboratory experiments to increase the production of succinic acid in E. coli.
  9. Fulltext Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization
    Choon YW, Mohamad MS, Deris S, Illias RM, Chong CK, Chai LE, et al.
    PLoS One, 2014;9(7):e102744.
    PMID: 25047076 DOI: 10.1371/journal.pone.0102744
    Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.
  10. A hybrid of bees algorithm and flux balance analysis with OptKnock as a platform for in silico optimization of microbial strains
    Choon YW, Mohamad MS, Deris S, Illias RM, Chong CK, Chai LE
    Bioprocess Biosyst Eng, 2014 Mar;37(3):521-32.
    PMID: 23892659 DOI: 10.1007/s00449-013-1019-y
    Microbial strain optimization focuses on improving technological properties of the strain of microorganisms. However, the complexities of the metabolic networks, which lead to data ambiguity, often cause genetic modification on the desirable phenotypes difficult to predict. Furthermore, vast number of reactions in cellular metabolism lead to the combinatorial problem in obtaining optimal gene deletion strategy. Consequently, the computation time increases exponentially with the increase in the size of the problem. Hence, we propose an extension of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by integrating OptKnock into BAFBA to validate the result. This paper presents a number of computational experiments to test on the performance and capability of BAFBA. Escherichia coli, Bacillus subtilis and Clostridium thermocellum are the model organisms in this paper. Also included is the identification of potential reactions to improve the production of succinic acid, lactic acid and ethanol, plus the discussion on the changes in the flux distribution of the predicted mutants. BAFBA shows potential in suggesting the non-intuitive gene knockout strategies and a low variability among the several runs. The results show that BAFBA is suitable, reliable and applicable in predicting optimal gene knockout strategy.
  11. Fulltext Gene knockout identification using an extension of Bees Hill Flux Balance Analysis
    Choon YW, Mohamad MS, Deris S, Chong CK, Omatu S, Corchado JM
    Biomed Res Int, 2015;2015:124537.
    PMID: 25874200 DOI: 10.1155/2015/124537
    Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.
  12. A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout
    Daud KM, Mohamad MS, Zakaria Z, Hassan R, Shah ZA, Deris S, et al.
    Comput Biol Med, 2019 10;113:103390.
    PMID: 31450056 DOI: 10.1016/j.compbiomed.2019.103390
    Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.
  13. Samira-VP: A simple protein alignment method with rechecking the alphabet vector positions
    Fotoohifiroozabadi S, Mohamad MS, Deris S
    J Bioinform Comput Biol, 2017 Apr;15(2):1750004.
    PMID: 28274174 DOI: 10.1142/S0219720017500044
    Protein structure alignment and comparisons that are based on an alphabetical demonstration of protein structure are more simple to run with faster evaluation processes; thus, their accuracy is not as reliable as three-dimension (3D)-based tools. As a 1D method candidate, TS-AMIR used the alphabetic demonstration of secondary-structure elements (SSE) of proteins and compared the assigned letters to each SSE using the [Formula: see text]-gram method. Although the results were comparable to those obtained via geometrical methods, the SSE length and accuracy of adjacency between SSEs were not considered in the comparison process. Therefore, to obtain further information on accuracy of adjacency between SSE vectors, the new approach of assigning text to vectors was adopted according to the spherical coordinate system in the present study. Moreover, dynamic programming was applied in order to account for the length of SSE vectors. Five common datasets were selected for method evaluation. The first three datasets were small, but difficult to align, and the remaining two datasets were used to compare the capability of the proposed method with that of other methods on a large protein dataset. The results showed that the proposed method, as a text-based alignment approach, obtained results comparable to both 1D and 3D methods. It outperformed 1D methods in terms of accuracy and 3D methods in terms of runtime.
  14. An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways
    Ismail AM, Mohamad MS, Abdul Majid H, Abas KH, Deris S, Zaki N, et al.
    Biosystems, 2017 Dec;162:81-89.
    PMID: 28951204 DOI: 10.1016/j.biosystems.2017.09.013
    Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.
  15. Fulltext A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production
    Ismail MA, Deris S, Mohamad MS, Abdullah A
    PLoS One, 2015;10(5):e0126199.
    PMID: 25961295 DOI: 10.1371/journal.pone.0126199
    This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.
  16. Multi-stage filtering for improving confidence level and determining dominant clusters in clustering algorithms of gene expression data
    Kasim S, Deris S, Othman RM
    Comput Biol Med, 2013 Sep;43(9):1120-33.
    PMID: 23930805 DOI: 10.1016/j.compbiomed.2013.05.011
    A drastic improvement in the analysis of gene expression has lead to new discoveries in bioinformatics research. In order to analyse the gene expression data, fuzzy clustering algorithms are widely used. However, the resulting analyses from these specific types of algorithms may lead to confusion in hypotheses with regard to the suggestion of dominant function for genes of interest. Besides that, the current fuzzy clustering algorithms do not conduct a thorough analysis of genes with low membership values. Therefore, we present a novel computational framework called the "multi-stage filtering-Clustering Functional Annotation" (msf-CluFA) for clustering gene expression data. The framework consists of four components: fuzzy c-means clustering (msf-CluFA-0), achieving dominant cluster (msf-CluFA-1), improving confidence level (msf-CluFA-2) and combination of msf-CluFA-0, msf-CluFA-1 and msf-CluFA-2 (msf-CluFA-3). By employing double filtering in msf-CluFA-1 and apriori algorithms in msf-CluFA-2, our new framework is capable of determining the dominant clusters and improving the confidence level of genes with lower membership values by means of which the unknown genes can be predicted.
  17. Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains
    Lu SJ, Salleh AH, Mohamad MS, Deris S, Omatu S, Yoshioka M
    Comput Biol Chem, 2014 12;53PB:175-183.
    PMID: 25462325 DOI: 10.1016/j.compbiolchem.2014.09.008
    Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms.
  18. Fulltext A Review on the Bioinformatics Tools for Neuroimaging
    Man MY, Ong MS, Mohamad MS, Deris S, Sulong G, Yunus J, et al.
    Malays J Med Sci, 2015 Dec;22(Spec Issue):9-19.
    PMID: 27006633 MyJurnal
    Neuroimaging is a new technique used to create images of the structure and function of the nervous system in the human brain. Currently, it is crucial in scientific fields. Neuroimaging data are becoming of more interest among the circle of neuroimaging experts. Therefore, it is necessary to develop a large amount of neuroimaging tools. This paper gives an overview of the tools that have been used to image the structure and function of the nervous system. This information can help developers, experts, and users gain insight and a better understanding of the neuroimaging tools available, enabling better decision making in choosing tools of particular research interest. Sources, links, and descriptions of the application of each tool are provided in this paper as well. Lastly, this paper presents the language implemented, system requirements, strengths, and weaknesses of the tools that have been widely used to image the structure and function of the nervous system.
  19. An improved hybrid of SVM and SCAD for pathway analysis
    Misman MF, Mohamad MS, Deris S, Abdullah A, Hashim SZ
    Bioinformation, 2011;7(4):169-75.
    PMID: 22102773
    Pathway analysis has lead to a new era in genomic research by providing further biological process information compared to traditional single gene analysis. Beside the advantage, pathway analysis provides some challenges to the researchers, one of which is the quality of pathway data itself. The pathway data usually defined from biological context free, when it comes to a specific biological context (e.g. lung cancer disease), typically only several genes within pathways are responsible for the corresponding cellular process. It also can be that some pathways may be included with uninformative genes or perhaps informative genes were excluded. Moreover, many algorithms in pathway analysis neglect these limitations by treating all the genes within pathways as significant. In previous study, a hybrid of support vector machines and smoothly clipped absolute deviation with groups-specific tuning parameters (gSVM-SCAD) was proposed in order to identify and select the informative genes before the pathway evaluation process. However, gSVM-SCAD had showed a limitation in terms of the performance of classification accuracy. In order to deal with this limitation, we made an enhancement to the tuning parameter method for gSVM-SCAD by applying the B-Type generalized approximate cross validation (BGACV). Experimental analyses using one simulated data and two gene expression data have shown that the proposed method obtains significant results in identifying biologically significant genes and pathways, and in classification accuracy.
  20. Fulltext An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes
    Mohamad MS, Omatu S, Deris S, Yoshioka M, Abdullah A, Ibrahim Z
    Algorithms Mol Biol, 2013;8(1):15.
    PMID: 23617960 DOI: 10.1186/1748-7188-8-15
    Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.
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