http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Particle Swarm Optimization for Gene Selection Using Microarray Data
Mohd Saberi Mohamad,Sigeru Omatu,Safaai Deris,Michifumi Yoshioka 한국멀티미디어학회 2009 한국멀티미디어학회 국제학술대회 Vol.2009 No.-
In order to find and select possible informative genes for cancer classification, recently, many researchers are analyzing micro array data using various computational intelligence methods. However, due to a small number of samples compared to the huge number of genes, irrelevant genes, and noisy genes, most of these methods face difficulties to select the informative genes. In this paper, we propose an improved binary particle swarm optimization to select a small subset of informative genes that is relevant for the cancer classification. Instead of the existing rule of position update in binary particle swarm optimization (BPSO), we modify the rule so that it selects efficiently the small subset from the microarray data. By performing experiments on two different public cancer data sets, we have found that the performance of the proposed method is superior to other related previous works, including BPSO in terms of classification accuracy and the number of selected genes.
Abdul Hakim Mohamed Salleh,Mohd Saberi Mohamad,Safaai Deris,Sigeru Omatu,Florentino Fdez-Riverola,Juan Manuel Corchado 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.4
The increasing demand of biochemical supply for various industries has spurred the development of metabolic engineering to find the optimal design of the microbial cell factories. Traditional method of chemical synthesis using the natural producer leads to the production far below their theoretical maximums. Gene knockout strategy is then introduced to improve the metabolite production. To aid the process, many computational algorithms have been developed to design the optimal microbial strain as cell factories to increase the production of the desired metabolite. However, due to the size of the genome scale model of the microbial strain, finding the optimal combination of genes to be knocked out is not an easy task. In this paper, we propose a hybrid of Genetic Ant Colony Optimization (GACO) and Flux Balance Analysis (FBA) namely GACOFBA to find the optimal gene knockout that increase the production of the target metabolite. Using E. coli and S. cerevisiae genome scale model, we test our proposed hybrid algorithm to increase the production of four different metabolites. By comparing with the results from existing method OptKnock as well as the conventional Ant Colony Optimization (ACO), the results show that our proposed hybrid algorithm able to identify the best set of genes and increase the production while maintaining the optimal growth rate.
Pooi San Chua,Abdul Hakim Mohamed Salleh,Mohd Saberi Mohamad,Safaai Deris,Sigeru Omatu,Michifumi Yoshioka 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.2
The current problem for metabolic engineering is how to identify a suitable set of genes for knockout that can improve the production of certain metabolites and sustain the growth rate from the thousands of metabolic networks which are complex and combinatorial. Some approaches, such as OptKnock and OptGene, are developed to enhance the production of desired metabolites. However, the performances of these approaches are suboptimal and the obtained results are unsatisfactory because of computational limitations such as local minima. In this paper, we propose a hybrid of Bat Algorithm and Flux Balance Analysis (BATFBA) to enhance succinate and lactate production by identifying a set of genes for knock out. The Bat Algorithm is an optimisation algorithm, whereas Flux Balance Analysis (FBA) is a mathematical approach to analyse the flow of metabolites through a metabolic network. The Escherichia coli iJR904 dataset was used to determine optimal knockout genes, production rate, and growth rate. By applying this hybrid method to the iJR904 dataset, we found that BATFBA yielded better results than existing methods, such as OptKnock and a hybrid of Artificial Bee Colony algorithms and Flux Balance Analysis (ABCFBA), at predicting succinate and lactate production.
A Review of Software for Predicting Gene Function
Swee Kuan Loha,Swee Thing Low,Mohd Saberi Mohamad,Safaai Deris,Shahreen Kasim,Choon Yee Wen,Zuwairie Ibrahim,Bambang Susilo,Yusuf Hendrawan,Agustin Krisna Wardani 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.2
A rich resource of information on functional genomics data can be applied to annotating the thousands of unknown gene functions that can be retrieved from most sequenced. High-throughput sequencing can lead to increased understanding of proteins and genes. We can infer networks of functional couplings from direct and indirect interactions. The development of gene function prediction is one of the major recent advances in the bioinformatics fields. These methods explore genomic context by major recent advances in the bioinformatics fields rather than by sequence alignment. This paper reviews software related to predicting gene function. Most of these programs are freely available online. The advantages and disadvantages of each program are stated clearly in order for the reader to understand them in a simple way. Web links to the software are provided as well.
Database and Tools for Metabolic Network Analysis
Lu Shi Jing,Farah Fathiah Muzaffar Shah,Mohd Saberi Mohamad,Nur Laily Hamran,Abdul Hakim Mohamed Salleh,Safaai Deris,Hany Alashwal 한국생물공학회 2014 Biotechnology and Bioprocess Engineering Vol.19 No.4
Metabolic network analysis has attracted muchattention in the area of systems biology. It has a profoundrole in understanding the key features of organismmetabolic networks and has been successfully applied inseveral fields of systems biology, including in silico geneknockouts, production yield improvement using engineeredmicrobial strains, drug target identification, and phenotypeprediction. A variety of metabolic network databases andtools have been developed in order to assist research inthese fields. Databases that comprise biochemical data arenormally integrated with the use of metabolic networkanalysis tools in order to give a more comprehensive result. This paper reviews and compares eight databases as wellas twenty one recent tools. The aim of this review is tostudy the different types of tools in terms of the featuresand usability, as well as the databases in terms of the scopeand data provided. These tools can be categorised intothree main types: standalone tools; toolbox-based tools;and web-based tools. Furthermore, comparisons of thedatabases as well as the tools are also provided to helpsoftware developers and users gain a clearer insight and abetter understanding of metabolic network analysis. Additionally, this review also helps to provide usefulinformation that can be used as guidance in choosing toolsand databases for a particular research interest.
Software for Detecting Gene-Gene Interactions in Genome Wide Association Studies
Ching Lee Koo,Mei Jing Liew,Mohd Saberi Mohamad,Abdul Hakim Mohamed Salleh,Safaai Deris,Zuwairie Ibrahim,Bambang Susilo,Yusuf Hendrawan,Agustin Krisna Wardani 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.4
Nowadays, genome-wide association studies (GWAS) have offered hundreds of thousands of single nucleotide polymorphism (SNPs). The studies of epistatic interactions of SNPs (denoted as gene-gene interactions or epitasis) are particularly important to unravel the genetic basis to complex multifactorial diseases. However, the greatest challenging and unsolved issue in GWAS is to discover epistatic interactions among large amount of SNPs data. Besides, traditional statistical approaches cannot solve such epistasis phenomenon due to possessing high dimensional data and the occurring of multiple polymorphisms. Hence, various kinds of promising software have been extensively investigated in order to solve these problems. This paper gives an overview on the software that had been used to detect gene-gene interactions that bring the effect on common and multifactorial diseases. Furthermore, sources, link, and functions description to the software are provided in this paper as well. Lastly, this paper presents the language implemented, system requirements, strengths, and weaknesses of software that had been widely used in detecting epistatic interactions in complex human diseases.
A Review of Cancer Classification Software for Gene Expression Data
Tan Ching Siang,Ting Wai Soon,Shahreen Kasim,Mohd Saberi Mohamad,Chan Weng Howe,Safaai Deris,Zalmiyah Zakaria,Zuraini Ali Shah,Zuwairie Ibrahim 보안공학연구지원센터 2015 International Journal of Bio-Science and Bio-Techn Vol.7 No.4
Microarray technology provides a way for researchers to measure the expression level of thousands of genes simultaneously in a single experiment. Due to the increasing amount of microarray data, the field of microarray data analysis has become a major topic among researchers. One of the examples of microarray data analysis is classification. Classification is the process of determining the classes for samples. The goal of classification is to identify the differentially expressed genes so that these genes can be used to predict the classes for new samples. In order to perform the tasks of classification of microarray data, classification software is required for effective classification and analysis of large-scale data. This paper reviews numerous classification software applications for gene expression data. In this paper, the reviewed software can be categorized into six supervised classification methods: Support Vector Machine, K-Nearest Neighbour, Neural Network, Linear Discriminant Analysis, Bayesian Classifier, and Random Forest.