In a simplified secured database access model, privileged group and public group can access data with any distribution. In order to secure the database, the confidentiality policy must be applied. Often, the management of the database privacy is neglected. This paper looks into the data confidentiality management and suggests to use semi-Markov chains to model the policy and the simulation results are discussed.
{"title":"Modeling Confidentiality in a Simplified Database Access","authors":"M. Shing, Chen-chi Shing, Kuo Lane Chen, Huei Lee","doi":"10.1109/IJCBS.2009.43","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.43","url":null,"abstract":"In a simplified secured database access model, privileged group and public group can access data with any distribution. In order to secure the database, the confidentiality policy must be applied. Often, the management of the database privacy is neglected. This paper looks into the data confidentiality management and suggests to use semi-Markov chains to model the policy and the simulation results are discussed.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115491205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microarray expression data, which contain expression levels of a large number of simultaneously observed genes, have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for tasks such as building prediction models for molecular classification of cancers. Traditional gene selection methods, however, fail to take the sample distributions into consideration for gene selection. Due to the scarcity of the samples, in Biomedical research it is very common to have severely biased data distributions with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). Sample sets with biased distributions require special attention for identifying genes responsible for particular disease. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify genes relevant to fatal diseases for biased microarray expression data. Experimental comparisons with the traditional ReliefF method on five microarray datasets demonstrate the effectiveness of the proposed methods in selecting informative genes from microarray expression data with biased sample distributions.
{"title":"Gene Selection for Microarray Expression Data with Imbalanced Sample Distributions","authors":"Abu H. M. Kamal, Xingquan Zhu, R. Narayanan","doi":"10.1109/IJCBS.2009.117","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.117","url":null,"abstract":"Microarray expression data, which contain expression levels of a large number of simultaneously observed genes, have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for tasks such as building prediction models for molecular classification of cancers. Traditional gene selection methods, however, fail to take the sample distributions into consideration for gene selection. Due to the scarcity of the samples, in Biomedical research it is very common to have severely biased data distributions with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). Sample sets with biased distributions require special attention for identifying genes responsible for particular disease. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify genes relevant to fatal diseases for biased microarray expression data. Experimental comparisons with the traditional ReliefF method on five microarray datasets demonstrate the effectiveness of the proposed methods in selecting informative genes from microarray expression data with biased sample distributions.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123420615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Structural annotation of genomes is one of major goals of genomics research. Most popular tools for structural annotation of genomes are determined by computational pipelines. It is well-known that these computational methods have a number of shortcomings including false identifications and incorrect identification of gene boundaries. Proteomic data can used to confirm the identification of genes identified by computational methods and correct mistakes. A Proteogenomic mapping method has been developed, which uses peptides identified from mass spectrometry for structural annotation of genomes. Spectra are matched against both a protein database and the genome database translated in all six reading frames. Those peptides that match the genome but not the protein database potentially represent novel protein coding genes, annotation errors. These short experimentally derived peptides are used to discover potential novel protein coding genes called expressed Protein Sequence Tags (ePSTs) by aligning the peptides to the genomic DNA and extending the translation in the 3' and 5' direction. In the paper, an enhanced pipeline, has been designed and developed for discovering and evaluating of potential novel protein coding genes: 1) a distance-based outlier detection method for validating peptides identified from MS/MS, 2) a proteogenomic mapping for discovery of potential novel protein coding genes, 3) collection of evidence from a number of sources and automatically evaluate potential novel protein coding genes by using machine learning techniques, such as Neural Network, Support Vector Machine, Naïve Bayes etc.
{"title":"Proteogenomic Mapping for Structural Annotation of Prokaryote Genomes","authors":"Nan Wang, S. Burgess, M. Lawrence, S. Bridges","doi":"10.1109/IJCBS.2009.126","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.126","url":null,"abstract":"Structural annotation of genomes is one of major goals of genomics research. Most popular tools for structural annotation of genomes are determined by computational pipelines. It is well-known that these computational methods have a number of shortcomings including false identifications and incorrect identification of gene boundaries. Proteomic data can used to confirm the identification of genes identified by computational methods and correct mistakes. A Proteogenomic mapping method has been developed, which uses peptides identified from mass spectrometry for structural annotation of genomes. Spectra are matched against both a protein database and the genome database translated in all six reading frames. Those peptides that match the genome but not the protein database potentially represent novel protein coding genes, annotation errors. These short experimentally derived peptides are used to discover potential novel protein coding genes called expressed Protein Sequence Tags (ePSTs) by aligning the peptides to the genomic DNA and extending the translation in the 3' and 5' direction. In the paper, an enhanced pipeline, has been designed and developed for discovering and evaluating of potential novel protein coding genes: 1) a distance-based outlier detection method for validating peptides identified from MS/MS, 2) a proteogenomic mapping for discovery of potential novel protein coding genes, 3) collection of evidence from a number of sources and automatically evaluate potential novel protein coding genes by using machine learning techniques, such as Neural Network, Support Vector Machine, Naïve Bayes etc.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An important application of graph partitioning is data clustering using a graph model--- the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. An effective multi-level algorithm based on AIS (artificial immune systems) for graph bipartitioning is proposed. During its coarsening phase, we adopt an improved matching approach based on the global information of the graph core to develop its guidance function. During its refinement phase, we exploit the hybrid immune refinement algorithm inspired in the CSA (clonal selection algorithm) and affinity maturation of the AIS. The algorithm is verified to be capable of finding the global approximate bipartitioning which incorporate early-exit FM (FM-EE) local improvement heuristic into CSA. The success of our algorithm relies on exploiting both the CSA and the concept of the graph core. It is implemented with American National Standards Institute (ANSI) C and compared to MeTiS that is a state-of-the-art partitioner in the literature. Our experimental evaluations show that it performs well and produces encouraging solutions on 18 graphs benchmarks.
{"title":"An Effective Multi-level Immune Algorithm for Graph Bipartitionin","authors":"Ming Leng, Lingyu Sun, Songnian Yu","doi":"10.1109/IJCBS.2009.12","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.12","url":null,"abstract":"An important application of graph partitioning is data clustering using a graph model--- the pairwise similarities between all data objects form a weighted graph adjacency matrix that contains all necessary information for clustering. An effective multi-level algorithm based on AIS (artificial immune systems) for graph bipartitioning is proposed. During its coarsening phase, we adopt an improved matching approach based on the global information of the graph core to develop its guidance function. During its refinement phase, we exploit the hybrid immune refinement algorithm inspired in the CSA (clonal selection algorithm) and affinity maturation of the AIS. The algorithm is verified to be capable of finding the global approximate bipartitioning which incorporate early-exit FM (FM-EE) local improvement heuristic into CSA. The success of our algorithm relies on exploiting both the CSA and the concept of the graph core. It is implemented with American National Standards Institute (ANSI) C and compared to MeTiS that is a state-of-the-art partitioner in the literature. Our experimental evaluations show that it performs well and produces encouraging solutions on 18 graphs benchmarks.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128441186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a approach for automatically generating fuzzy rules from sample patterns is presented. Then a self-adaptive fuzzy neural network is built based on the fuzzy partition which divides the input space with input and output information. The salient characteristics of the self-adaptive fuzzy neural networks are: 1) structure identification and parameters estimation are performed automatically and simultaneously; 2) fuzzy rules can be recruited or deleted dynamically; 3) parameters of rules can be obtained by evolutionary computation. Simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed. Comprehensive com-parisons with other approach show that the proposed approach is superior over other in terms of learning efficiency and performance.
{"title":"Designing the Self-Adaptive Fuzzy Neural Networks","authors":"Liu Fang","doi":"10.1109/IJCBS.2009.40","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.40","url":null,"abstract":"In this paper, a approach for automatically generating fuzzy rules from sample patterns is presented. Then a self-adaptive fuzzy neural network is built based on the fuzzy partition which divides the input space with input and output information. The salient characteristics of the self-adaptive fuzzy neural networks are: 1) structure identification and parameters estimation are performed automatically and simultaneously; 2) fuzzy rules can be recruited or deleted dynamically; 3) parameters of rules can be obtained by evolutionary computation. Simulation results demonstrate that a compact and high performance fuzzy rule base can be constructed. Comprehensive com-parisons with other approach show that the proposed approach is superior over other in terms of learning efficiency and performance.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124760318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent advances in highly parallel, multithreaded, manycore Graphics Processing Units (GPUs) have been enabling massive parallel implementations of many applications in bioinformatics. In this paper, we describe a parallel implementation of genome-wide association studies (GWAS) using Compute Unified Device Architecture (CUDA). Using a single NVIDIA GTX 280 graphics card, we achieve speedups of about 15 times over Intel Xeon E5420. We also implement a highly scalable, massive parallel, GWAS system using the Message Passing Interface (MPI) and show that a single GTX 280 can have similar performance as a 16-node cluster. We further apply the GPU program to two real genome-wide case-control data sets. The results show that the GPU program is 17.7 times as fast as the CPU version for an Age-related Macular Degeneration (AMD) data set and 25.7 times as fast as the CPU version for a Parkinson’s disease data set.
{"title":"Accelerating Genome-Wide Association Studies Using CUDA Compatible Graphics Processing Units","authors":"Rui Jiang, Feng Zeng, Wangshu Zhang, Xuebing Wu, Zhihong Yu","doi":"10.1109/IJCBS.2009.32","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.32","url":null,"abstract":"Recent advances in highly parallel, multithreaded, manycore Graphics Processing Units (GPUs) have been enabling massive parallel implementations of many applications in bioinformatics. In this paper, we describe a parallel implementation of genome-wide association studies (GWAS) using Compute Unified Device Architecture (CUDA). Using a single NVIDIA GTX 280 graphics card, we achieve speedups of about 15 times over Intel Xeon E5420. We also implement a highly scalable, massive parallel, GWAS system using the Message Passing Interface (MPI) and show that a single GTX 280 can have similar performance as a 16-node cluster. We further apply the GPU program to two real genome-wide case-control data sets. The results show that the GPU program is 17.7 times as fast as the CPU version for an Age-related Macular Degeneration (AMD) data set and 25.7 times as fast as the CPU version for a Parkinson’s disease data set.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121179742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. A variety of toxicological effects have been associated with explosive compounds 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX). Here we developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset with 15,208 non-redundant earthworm (Eisenia fetida) gene probes on each array. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control (untreated), TNT-treated, and RDX-treated. First, the class comparison statistical algorithm implemented in BRB-ArrayTools was used to infer a total of 869 genes that significantly changed relative to controls as a result of exposure to TNT or RDX at various concentrations for 4 or 14 days. Then, nine tree-based supervised machine learning algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance in the nine classification methods, and were used to build support vector machines (SVM). A SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). Results of unsupervised clustering show that the use of the top 100 classifier genes can assign the largest number of the 248 worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes.
{"title":"Discovery of Biomarker Genes from Earthworm Microarray Data by Discriminant Analysis and Clustering","authors":"Ying Li, Nan Wang, E. Perkins, P. Gong","doi":"10.1109/IJCBS.2009.134","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.134","url":null,"abstract":"Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. A variety of toxicological effects have been associated with explosive compounds 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX). Here we developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset with 15,208 non-redundant earthworm (Eisenia fetida) gene probes on each array. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control (untreated), TNT-treated, and RDX-treated. First, the class comparison statistical algorithm implemented in BRB-ArrayTools was used to infer a total of 869 genes that significantly changed relative to controls as a result of exposure to TNT or RDX at various concentrations for 4 or 14 days. Then, nine tree-based supervised machine learning algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance in the nine classification methods, and were used to build support vector machines (SVM). A SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). Results of unsupervised clustering show that the use of the top 100 classifier genes can assign the largest number of the 248 worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128023141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tumor networks display percolation like scaling, representing the first evidence for a biological growth process whose key determinants are local substrate properties. In this paper we present a full characterization of a recently proposed model which reproduces the main features of the biological system, focusing on its dynamical properties, on the fractal properties of patterns, and on the percolative phase transition. We propose a simple model which reproduces many features of the biological system.
{"title":"Two Dimensional Modeling and Fractal Characterization of Tumor Vascular Network","authors":"R. Dobrescu, L. Ichim","doi":"10.1109/IJCBS.2009.71","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.71","url":null,"abstract":"Tumor networks display percolation like scaling, representing the first evidence for a biological growth process whose key determinants are local substrate properties. In this paper we present a full characterization of a recently proposed model which reproduces the main features of the biological system, focusing on its dynamical properties, on the fractal properties of patterns, and on the percolative phase transition. We propose a simple model which reproduces many features of the biological system.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131402338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In present study attempt has been taken to determine the degree of malignancy of brain tumors using artificial intelligence. The suspicious regions in brain as suggested by the radiologists have been segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for precise extraction of boundary features of the tumor region. As Fourier Descriptors introduce a large number of feature vectors that may invite the problem of over learning and chance of misclassifications, the proposed diagnosis system efficiently search the significant boundary features by genetic algorithm and feed them to the adaptive neuro-fuzzy based classifier. In addition to shape based features, textural compositions are also incorporated to achieve high level of accuracy in diagnosis of tumors. The study involves 100 brain images and has shown 86% correct classification rate.
{"title":"A Study on Prognosis of Brain Tumors Using Fuzzy Logic and Genetic Algorithm Based Techniques","authors":"Arpita Das, M. Bhattacharya","doi":"10.1109/IJCBS.2009.129","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.129","url":null,"abstract":"In present study attempt has been taken to determine the degree of malignancy of brain tumors using artificial intelligence. The suspicious regions in brain as suggested by the radiologists have been segmented using fuzzy c-means clustering technique. Fourier descriptors are utilized for precise extraction of boundary features of the tumor region. As Fourier Descriptors introduce a large number of feature vectors that may invite the problem of over learning and chance of misclassifications, the proposed diagnosis system efficiently search the significant boundary features by genetic algorithm and feed them to the adaptive neuro-fuzzy based classifier. In addition to shape based features, textural compositions are also incorporated to achieve high level of accuracy in diagnosis of tumors. The study involves 100 brain images and has shown 86% correct classification rate.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114435129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks. Finding the elementary modes with minimum number of reactions (shortest elementary modes) is an interesting problem and has potential uses in various applications. However, this problem is NP-hard. This work is an initial step to analyze this problem from a parameterized computation view. With the number of reactions in elementary modes as natural parameter, we prove that finding the shortest elementary modes in metabolic networks is W[1]-hard.
{"title":"Parameterized Complexity of Finding Elementary Modes in Metabolic Networks","authors":"Hong Liu, Haodi Feng, Daming Zhu","doi":"10.1109/IJCBS.2009.121","DOIUrl":"https://doi.org/10.1109/IJCBS.2009.121","url":null,"abstract":"The concept of elementary (flux) modes provides a rigorous description of pathways in metabolic networks. Finding the elementary modes with minimum number of reactions (shortest elementary modes) is an interesting problem and has potential uses in various applications. However, this problem is NP-hard. This work is an initial step to analyze this problem from a parameterized computation view. With the number of reactions in elementary modes as natural parameter, we prove that finding the shortest elementary modes in metabolic networks is W[1]-hard.","PeriodicalId":170985,"journal":{"name":"2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114632943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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