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Mining nutrigenetics patterns related to obesity: use of parallel multifactor dimensionality reduction 挖掘与肥胖相关的营养遗传学模式:使用平行多因素降维
International Journal of Bioinformatics Research and Applications Pub Date : 2015-05-01 DOI: 10.1504/IJBRA.2015.069194
K. Karayianni, K. Grimaldi, K. Nikita, I. Valavanis
{"title":"Mining nutrigenetics patterns related to obesity: use of parallel multifactor dimensionality reduction","authors":"K. Karayianni, K. Grimaldi, K. Nikita, I. Valavanis","doi":"10.1504/IJBRA.2015.069194","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.069194","url":null,"abstract":"This paper aims to enlighten the complex etiology beneath obesity by analysing data from a large nutrigenetics study, in which nutritional and genetic factors associated with obesity were recorded for around two thousand individuals. In our previous work, these data have been analysed using artificial neural network methods, which identified optimised subsets of factors to predict one's obesity status. These methods did not reveal though how the selected factors interact with each other in the obtained predictive models. For that reason, parallel Multifactor Dimensionality Reduction (pMDR) was used here to further analyse the pre-selected subsets of nutrigenetic factors. Within pMDR, predictive models using up to eight factors were constructed, further reducing the input dimensionality, while rules describing the interactive effects of the selected factors were derived. In this way, it was possible to identify specific genetic variations and their interactive effects with particular nutritional factors, which are now under further study.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.069194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702249","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}
引用次数: 0
Analysing extremely small sized ratio datasets 分析极小的比率数据集
International Journal of Bioinformatics Research and Applications Pub Date : 2015-05-01 DOI: 10.1504/IJBRA.2015.069225
Piero Ricchiuto, J. Sng, W. Goh
{"title":"Analysing extremely small sized ratio datasets","authors":"Piero Ricchiuto, J. Sng, W. Goh","doi":"10.1504/IJBRA.2015.069225","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.069225","url":null,"abstract":"The naïve use of expression ratios in high-throughput biological studies can greatly limit analytical outcome especially when sample size is small. In the worst-case scenario, with only one reference and one test state each (often due to the severe lack of study material); such limitations make it difficult to perform statistically meaningful analysis. Workarounds include the single sample Z-test or through network inference. Here, we describe a complementary plot-based approach for analysing such extremely small sized ratio (ESSR) data - a generalisation of the Bland-Altman plot, which we shall refer to as the Dodeca-Panels. Included in this paper is an R implementation of the Dodeca-Panels method.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.069225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702316","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}
引用次数: 1
BioInt: an integrative biological object-oriented application framework and interpreter BioInt:一个集成的生物面向对象的应用程序框架和解释器
International Journal of Bioinformatics Research and Applications Pub Date : 2015-05-01 DOI: 10.1504/IJBRA.2015.069195
Sanket Desai, P. Burra
{"title":"BioInt: an integrative biological object-oriented application framework and interpreter","authors":"Sanket Desai, P. Burra","doi":"10.1504/IJBRA.2015.069195","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.069195","url":null,"abstract":"BioInt, a biological programming application framework and interpreter, is an attempt to equip the researchers with seamless integration, efficient extraction and effortless analysis of the data from various biological databases and algorithms. Based on the type of biological data, algorithms and related functionalities, a biology-specific framework was developed which has nine modules. The modules are a compilation of numerous reusable BioADTs. This software ecosystem containing more than 450 biological objects underneath the interpreter makes it flexible, integrative and comprehensive. Similar to Python, BioInt eliminates the compilation and linking steps cutting the time significantly. The researcher can write the scripts using available BioADTs (following C++ syntax) and execute them interactively or use as a command line application. It has features that enable automation, extension of the framework with new/external BioADTs/libraries and deployment of complex work flows.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.069195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702260","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}
引用次数: 4
Predicting DNA mutations during cancer evolution 预测癌症进化过程中的DNA突变
International Journal of Bioinformatics Research and Applications Pub Date : 2015-05-01 DOI: 10.1504/IJBRA.2015.069186
J. Martínez, Nelson Lopez-Jimenez, Tao Meng, S. S. Iyengar
{"title":"Predicting DNA mutations during cancer evolution","authors":"J. Martínez, Nelson Lopez-Jimenez, Tao Meng, S. S. Iyengar","doi":"10.1504/IJBRA.2015.069186","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.069186","url":null,"abstract":"Bio-systems are inherently complex information processing systems. Their physiological complexities limit the formulation and testing of a hypothesis for their behaviour. Our goal here was to test a computational framework utilising published data from a longitudinal study of patients with acute myeloid leukaemia (AML), whose DNA from both normal and malignant tissues were subjected to NGS analysis at various points in time. By processing the sequencing data before relapse time, we tested our framework by predicting the regions of the genome to be mutated at relapse time and, later, by comparing our results with the actual regions that showed mutations (discovered by genome sequencing at relapse time). After a detailed statistical analysis, the resulting correlation coefficient (degree of matching of proposed framework with real data) is 0.9816 ± 0.009 at 95% confidence interval. This high performance from our proposed framework opens new research opportunities for bioinformatics researchers and clinical doctors.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.069186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702184","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}
引用次数: 1
Understanding the virulence of the entero-aggregative E. coli O104: H4 了解肠道聚集性大肠杆菌O104: H4的毒力
International Journal of Bioinformatics Research and Applications Pub Date : 2015-05-01 DOI: 10.1504/IJBRA.2015.069185
B. Kesavan, K. Srividhya, S. Krishnaswamy, M. Raja, N. Vidya, A. Mohan
{"title":"Understanding the virulence of the entero-aggregative E. coli O104: H4","authors":"B. Kesavan, K. Srividhya, S. Krishnaswamy, M. Raja, N. Vidya, A. Mohan","doi":"10.1504/IJBRA.2015.069185","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.069185","url":null,"abstract":"O104:H4 is a new strain of E. coli that has caused an outbreak in Germany. It was isolated from patients with bloody diarrhoea and Haemolytic Uremic Syndrome (HUS). BGI (www.genomics.cn) sequenced and assembled this new strain. It was reported to show resistance to a number of drugs that are toxic to other E. coli and causes serious complications during infections, which ultimately lead to death. Multi-drug resistance and high virulence of this strain is thought to be acquired from different sources, by horizontal gene transfer. A total of 38 prophage elements were detected from the new strain by using three computational tools viz., DRAD, Prophage Finder and PHAST. Analysis on these prophage elements shows a number of virulence proteins like Shiga toxin and multi-drug resistance protein encoding genes. The high virulence of the strain could be attributed by the prophage elements acquired from its micro environment.","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.069185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66702131","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}
引用次数: 2
TDAC: Co-Expressed Gene Pattern Finding Using Attribute Clustering TDAC:利用属性聚类发现共表达基因模式
International Journal of Bioinformatics Research and Applications Pub Date : 2015-02-01 DOI: 10.1007/978-81-322-1602-5_64
Tahleen A. Rahman, D. Bhattacharyya
{"title":"TDAC: Co-Expressed Gene Pattern Finding Using Attribute Clustering","authors":"Tahleen A. Rahman, D. Bhattacharyya","doi":"10.1007/978-81-322-1602-5_64","DOIUrl":"https://doi.org/10.1007/978-81-322-1602-5_64","url":null,"abstract":"","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75595974","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}
引用次数: 0
HDVDB: a data warehouse for hepatitis delta virus. 丁型肝炎病毒数据仓库。
International Journal of Bioinformatics Research and Applications Pub Date : 2015-01-01 DOI: 10.1504/IJBRA.2015.068091
Sarita Singh, Sunil Kumar Gupta, Anuradha Nischal, Kamlesh Kumar Pant, Prahlad Kishore Seth
{"title":"HDVDB: a data warehouse for hepatitis delta virus.","authors":"Sarita Singh,&nbsp;Sunil Kumar Gupta,&nbsp;Anuradha Nischal,&nbsp;Kamlesh Kumar Pant,&nbsp;Prahlad Kishore Seth","doi":"10.1504/IJBRA.2015.068091","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.068091","url":null,"abstract":"<p><p>Hepatitis Delta Virus (HDV) is an RNA virus and causes delta hepatitis in humans. Although a lot of data is available for HDV, but retrieval of information is a complicated task. Current web database 'HDVDB' provides a comprehensive web-resource for HDV. The database is basically concerned with basic information about HDV and disease caused by this virus, genome structure, pathogenesis, epidemiology, symptoms and prevention, etc. Database also supplies sequence data and bibliographic information about HDV. A tool 'siHDV Predict' to design the effective siRNA molecule to control the activity of HDV, is also integrated in database. It is a user friendly information system available at public domain and provides annotated information about HDV for research scholars, scientists, pharma industry people for further study. </p>","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.068091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33018043","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}
引用次数: 3
TDAC: co-expressed gene pattern finding using attribute clustering. TDAC:利用属性聚类发现共表达基因模式。
International Journal of Bioinformatics Research and Applications Pub Date : 2015-01-01 DOI: 10.1504/IJBRA.2015.067339
Tahleen A Rahman, Dhruba K Bhattacharyya
{"title":"TDAC: co-expressed gene pattern finding using attribute clustering.","authors":"Tahleen A Rahman,&nbsp;Dhruba K Bhattacharyya","doi":"10.1504/IJBRA.2015.067339","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.067339","url":null,"abstract":"<p><p>A number of clustering methods introduced for analysis of gene expression data for extracting potential relationships among the genes are studied and reported in this paper. An effective unsupervised method (TDAC) is proposed for simultaneous detection of outliers and biologically relevant co-expressed patterns. Effectiveness of TDAC is established in comparison to its other competing algorithms over six publicly available benchmark gene expression datasets in terms of both internal and external validity measures. Main attractions of TDAC are: (a) it does not require discretisation, (b) it is capable of identifying biologically relevant gene co-expressed patterns as well as outlier genes(s), (c) it is cost-effective in terms of time and space, (d) it does not require the number of clusters a priori, and (e) it is free from the restrictions of using any proximity measure. </p>","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.067339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33043360","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}
引用次数: 0
Identifying protein complexes based on the integration of PPI network and gene expression data. 基于PPI网络和基因表达数据的蛋白质复合物鉴定。
International Journal of Bioinformatics Research and Applications Pub Date : 2015-01-01 DOI: 10.1504/IJBRA.2015.067337
Weijie Chen, Min Li, Xuehong Wu, Jianxin Wang
{"title":"Identifying protein complexes based on the integration of PPI network and gene expression data.","authors":"Weijie Chen,&nbsp;Min Li,&nbsp;Xuehong Wu,&nbsp;Jianxin Wang","doi":"10.1504/IJBRA.2015.067337","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.067337","url":null,"abstract":"<p><p>Identification of protein complexes is crucial to understand principles of cellular organisation and predict protein functions. In this paper, a novel protein complex discovery algorithm IPCIPG is proposed based on the integration of Protein-Protein Interaction network (PPI network) and gene expression data. IPCIPG is a local search algorithm which has two versions: IPCIPG-n for identifying non-overlapping clusters and IPCIPG-o for detecting overlapping clusters. The experimental results on the yeast PPI network show that IPCIPG can identify protein complexes with specific biological meaning more effectively, precisely and comprehensively than six other algorithms: HUNTER, HC-PIN, CMC, SPICi, MOCDE and MCL. </p>","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.067337","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33043359","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}
引用次数: 2
Proteins involved in more domain types tend to be more essential. 参与更多结构域类型的蛋白质往往更重要。
International Journal of Bioinformatics Research and Applications Pub Date : 2015-01-01 DOI: 10.1504/IJBRA.2015.068086
Lu Chen, Yingjiao Cheng, Min Li, Jianxin Wang
{"title":"Proteins involved in more domain types tend to be more essential.","authors":"Lu Chen,&nbsp;Yingjiao Cheng,&nbsp;Min Li,&nbsp;Jianxin Wang","doi":"10.1504/IJBRA.2015.068086","DOIUrl":"https://doi.org/10.1504/IJBRA.2015.068086","url":null,"abstract":"<p><p>Investigation of essential proteins is significantly valuable for understanding of cellular life, drug design and other practical purposes. In most of current studies, essential proteins are generally mined in protein-protein interaction (PPI) networks with diverse topology features. In this study, we investigate what kind of proteins is inclined to be essential from a new perspective. The investigation implies that protein essentiality is correlated with protein domains, which are functional, structural and evolutionary units of proteins. Proteins with a larger Number of Domain Types (NDT) tend to be essential. The analyses on 22 species show that essential proteins identified by NDT are much more than those identified by ten random identifications. The consideration of the structural feature makes us less dependent on network data and thus enables us to investigate protein essentiality of more species with incomplete and/or inconsistent network data. </p>","PeriodicalId":35444,"journal":{"name":"International Journal of Bioinformatics Research and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJBRA.2015.068086","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33143603","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}
引用次数: 4
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