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Tri Basuki Kurniawan,Noor Khafifah Khalid,Zuwairie Ibrahim,Mohamad Shukri Zainal Abidin,Marzuki Khalid 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
Generally, in DNA computing, the DNA sequences used for the computation should be critically designed in order to reduce error that could occur during computation. In order to design a set DNA sequences for Direct-Proportional Length-Based DNA Computing (DPLB-DNAC), a Population-based Ant Colony Optimization (P-ACO) method is proposed. Previously, the DNA sequences for DPLB-DNAC are designed using graph method and Generate-and-Test approach, respectively. The both of methods are without the optimized objective functions process. The proposed method used four object functions in their process to obtain the best solutions. The results obtained from the proposed method are compared with the sequences generated by graph and Generate-and-Test methods. The results show that P-ACO approach can generate relatively better DNA sequences in some objectives than others. It can be concluded that proposed algorithm can obtain relatively a better set of DNA sequences for DPLB-DNAC.
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.
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.