International Journal of Data Mining and Bioinformatics最新文献_第7页

A method for extracting task-oriented information from biological text sources. 一种从生物文本源中提取面向任务信息的方法。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.070072

Dhanasekaran Kuttiyapillai, R Rajeswari

{"title":"A method for extracting task-oriented information from biological text sources.","authors":"Dhanasekaran Kuttiyapillai, R Rajeswari","doi":"10.1504/ijdmb.2015.070072","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.070072","url":null,"abstract":"A method for information extraction which processes the unstructured data from document collection has been introduced. A dynamic programming technique adopted to find relevant genes from sequences which are longest and accurate is used for finding matching sequences and identifying effects of various factors. The proposed method could handle complex information sequences which give different meanings in different situations, eliminating irrelevant information. The text contents were pre-processed using a general-purpose method and were applied with entity tagging component. The bottom-up scanning of key-value pairs improves content finding to generate relevant sequences to the testing task. This paper highlights context-based extraction method for extracting food safety information, which is identified from articles, guideline documents and laboratory results. The graphical disease model verifies weak component through utilisation of development data set. This improves the accuracy of information retrieval in biological text analysis and reporting applications.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"12 4","pages":"387-99"},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.070072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34192164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Granular support vector machine to identify unknown structural classes of protein. 颗粒支持向量机识别未知结构类型的蛋白质。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.070065

Rohayanti Hassan, Razib M Othman, Zuraini A Shah

引用次数: 0

A system biology approach for understanding the miRNA regulatory network in colon rectal cancer. 用系统生物学方法了解结肠癌miRNA调控网络。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.066332

Meeta Pradhan, Kshithija Nagulapalli, Lakenvia Ledford, Yogesh Pandit, Mathew Palakal

引用次数: 4

Discovery of phenotypic networks from genotypic association studies with application to obesity. 从基因型关联研究中发现表型网络与肥胖症的应用。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.069414

Christine W Duarte, Yann C Klimentidis, Jacqueline J Harris, Michelle Cardel, José R Fernández

{"title":"Discovery of phenotypic networks from genotypic association studies with application to obesity.","authors":"Christine W Duarte, Yann C Klimentidis, Jacqueline J Harris, Michelle Cardel, José R Fernández","doi":"10.1504/ijdmb.2015.069414","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.069414","url":null,"abstract":"Genome-wide Association Studies (GWAS) have resulted in many discovered risk variants for several obesity-related traits. However, before clinical relevance of these discoveries can be achieved, molecular or physiological mechanisms of these risk variants needs to be discovered. One strategy is to perform data mining of phenotypically-rich data sources such as those present in dbGAP (database of Genotypes and Phenotypes) for hypothesis generation. Here we propose a technique that combines the power of existing Bayesian Network (BN) learning algorithms with the statistical rigour of Structural Equation Modelling (SEM) to produce an overall phenotypic network discovery system with optimal properties. We illustrate our method using the analysis of a candidate SNP data set from the AMERICO sample, a multi-ethnic cross-sectional cohort of roughly 300 children with detailed obesity-related phenotypes. We demonstrate our approach by showing genetic mechanisms for three obesity-related SNPs.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"12 2","pages":"129-43"},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.069414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34123508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Sequence-based protein superfamily classification using computational intelligence techniques: a review. 基于序列的蛋白质超家族分类使用计算智能技术:综述。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.067957

Swati Vipsita, Santanu Kumar Rath

{"title":"Sequence-based protein superfamily classification using computational intelligence techniques: a review.","authors":"Swati Vipsita, Santanu Kumar Rath","doi":"10.1504/ijdmb.2015.067957","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.067957","url":null,"abstract":"Protein superfamily classification deals with the problem of predicting the family membership of newly discovered amino acid sequence. Although many trivial alignment methods are already developed by previous researchers, but the present trend demands the application of computational intelligent techniques. As there is an exponential growth in size of biological database, retrieval and inference of essential knowledge in the biological domain become a very cumbersome task. This problem can be easily handled using intelligent techniques due to their ability of tolerance for imprecision, uncertainty, approximate reasoning, and partial truth. This paper discusses the various global and local features extracted from full length protein sequence which are used for the approximation and generalisation of the classifier. The various parameters used for evaluating the performance of the classifiers are also discussed. Therefore, this review article can show right directions to the present researchers to make an improvement over the existing methods.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"11 4","pages":"424-57"},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.067957","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34145688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Discovering essential proteins based on PPI network and protein complex. 基于PPI网络和蛋白质复合物发现必需蛋白质。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.068951

Jun Ren, Jianxin Wang, Min Li, Fangxiang Wu

引用次数: 25

Metabolites production improvement by identifying minimal genomes and essential genes using flux balance analysis. 通过通量平衡分析鉴定最小基因组和必需基因来改善代谢物的产生。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.068955

Abdul Hakim Mohamed Salleh, Mohd Saberi Mohamad, Safaai Deris, Rosli Md Illias

{"title":"Metabolites production improvement by identifying minimal genomes and essential genes using flux balance analysis.","authors":"Abdul Hakim Mohamed Salleh, Mohd Saberi Mohamad, Safaai Deris, Rosli Md Illias","doi":"10.1504/ijdmb.2015.068955","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.068955","url":null,"abstract":"With the advancement in metabolic engineering technologies, reconstruction of the genome of host organisms to achieve desired phenotypes can be made. However, due to the complexity and size of the genome scale metabolic network, significant components tend to be invisible. We proposed an approach to improve metabolite production that consists of two steps. First, we find the essential genes and identify the minimal genome by a single gene deletion process using Flux Balance Analysis (FBA) and second by identifying the significant pathway for the metabolite production using gene expression data. A genome scale model of Saccharomyces cerevisiae for production of vanillin and acetate is used to test this approach. The result has shown the reliability of this approach to find essential genes, reduce genome size and identify production pathway that can further optimise the production yield. The identified genes and pathways can be extendable to other applications especially in strain optimisation.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"12 1","pages":"85-99"},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.068955","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34276061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

To screen the effective software for analysing gene interactions from Kashin-Beck disease genome profiling pathway and network, according to the tool of GeneMANIA. 根据GeneMANIA工具，从大骨节病基因组图谱途径和网络中筛选有效的基因相互作用分析软件。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.068963

Sen Wang, Weizhuo Wang, Junjie Zhao, Feng Zhang, Shulan He, Xiong Guo

{"title":"To screen the effective software for analysing gene interactions from Kashin-Beck disease genome profiling pathway and network, according to the tool of GeneMANIA.","authors":"Sen Wang, Weizhuo Wang, Junjie Zhao, Feng Zhang, Shulan He, Xiong Guo","doi":"10.1504/ijdmb.2015.068963","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.068963","url":null,"abstract":"In order to screen the more effective software for the pathway and network analysis of Kashin-Beck disease, gene microarrays, TranscriptomeBrowser, MetaCore and GeneMANIA were used for analysis. Three significant chondrocytic pathways and one network were screened by TranscriptomeBrowser; one significant pathway and one network were identified by MetaCore. BAX, APAF1, CASP6, BCL2, VEGF, SOCS3, BAK, TGFBI, TNFAIP6, TNFRSF11B and THBS1 were significant genes associated with the biological function of chondrocyte or cartilage involved in the TranscriptomeBrowser or MetaCore results. The interactions between the significant genes and their adjacent genes were searched and classified in GeneMANIA. In pathway analysis results, TranscriptomeBrowser is superior to get the interaction of pathway and co-expression compared with MetaCore; MetaCore is superior to get the interaction of physical interaction compared with TranscriptomeBrowser. In network analysis results, TranscriptomeBrowser contains more interaction message of co-localisation, MetaCore contains, more interaction message of co-expression.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"12 1","pages":"100-14"},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.068963","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34276062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Modelling and structural characteristics analysis of gene networks for prostate cancer. 前列腺癌基因网络的建模和结构特征分析。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.068950

Yulin Zhang, Shudong Wang, Dazhi Meng

引用次数: 0

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification. 带有误分类代价和拒绝代价的正则化极端学习机用于基因表达数据分类。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.069657

Huijuan Lu, Shasha Wei, Zili Zhou, Yanzi Miao, Yi Lu

{"title":"Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.","authors":"Huijuan Lu, Shasha Wei, Zili Zhou, Yanzi Miao, Yi Lu","doi":"10.1504/ijdmb.2015.069657","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.069657","url":null,"abstract":"The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":"12 3","pages":"294-312"},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.069657","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34125295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6