International Journal of Data Mining and Bioinformatics最新文献_第8页

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":null,"pages":null},"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

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":null,"pages":null},"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

Managing changes in distributed biomedical ontologies using hierarchical distributed graph transformation. 使用分层分布式图转换管理分布式生物医学本体中的变更。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.066334

Arash Shaban-Nejad, Volker Haarslev

引用次数: 10

Translation of disease associated gene signatures across tissues. 组织间疾病相关基因特征的翻译。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.067321

Adetayo Kasim, Ziv Shkedy, Dan Lin, Suzy Van Sanden, Josè Cortiñas Abrahantes, Hinrich W H Göhlmann, Luc Bijnens, Dani Yekutieli, Michael Camilleri, Jeroen Aerssens, Willem Talloen

{"title":"Translation of disease associated gene signatures across tissues.","authors":"Adetayo Kasim, Ziv Shkedy, Dan Lin, Suzy Van Sanden, Josè Cortiñas Abrahantes, Hinrich W H Göhlmann, Luc Bijnens, Dani Yekutieli, Michael Camilleri, Jeroen Aerssens, Willem Talloen","doi":"10.1504/ijdmb.2015.067321","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.067321","url":null,"abstract":"It has recently been shown that disease associated gene signatures can be identified by profiling tissue other than the disease related tissue. In this paper, we investigate gene signatures for Irritable Bowel Syndrome (IBS) using gene expression profiling of both disease related tissue (colon) and surrogate tissue (rectum). Gene specific joint ANOVA models were used to investigate differentially expressed genes between the IBS patients and the healthy controls taken into account both intra and inter tissue dependencies among expression levels of the same gene. Classification algorithms in combination with feature selection methods were used to investigate the predictive power of gene expression levels from the surrogate and the target tissues. We conclude based on the analyses that expression profiles of the colon and the rectum tissue could result in better predictive accuracy if the disease associated genes are known.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.067321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34039165","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: 2

Signal transduction in the activation of spermatozoa compared to other signalling pathways: a biological networks study. 精子激活中的信号转导与其他信号通路的比较:一项生物网络研究。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.068953

Nicola Bernabò, Mauro Mattioli, Barbara Barboni

引用次数: 13

Predicting gene functions from multiple biological sources using novel ensemble methods. 利用新颖的集成方法预测多种生物来源的基因功能。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.069418

Chandan K Reddy, Mohammad S Aziz

引用次数: 2

An ensemble method for reconstructing gene regulatory network with jackknife resampling and arithmetic mean fusion. 基于折刀重采样和算术均值融合的基因调控网络集成重构方法。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.069658

Chen Zhou, Shao-Wu Zhang, Fei Liu

引用次数: 7

Co-decision matrix framework for name entity recognition in biomedical text. 生物医学文本名称实体识别的协同决策矩阵框架。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.067956

Haochang Wang, Yu Li

引用次数: 1

An integrated approach to identify protein complex based on best neighbour and modularity increment. 一种基于最近邻和模块化增量的蛋白质复合体识别方法。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.067973

Xianjun Shen, Yanli Zhao, Yanan Li, Yang Yi, Tingting He, Jincai Yang

{"title":"An integrated approach to identify protein complex based on best neighbour and modularity increment.","authors":"Xianjun Shen, Yanli Zhao, Yanan Li, Yang Yi, Tingting He, Jincai Yang","doi":"10.1504/ijdmb.2015.067973","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.067973","url":null,"abstract":"In order to overcome the limitations of global modularity and the deficiency of local modularity, we propose a hybrid modularity measure Local-Global Quantification (LGQ) which considers global modularity and local modularity together. LGQ adopts a suitable module feature adjustable parameter to control the balance of global detecting capability and local search capability in Protein-Protein Interactions (PPI) Network. Furthermore, we develop a new protein complex mining algorithm called Best Neighbour and Local-Global Quantification (BN-LGQ) which integrates the best neighbour node and modularity increment. BN-LGQ expands the protein complex by fast searching the best neighbour node of the current cluster and by calculating the modularity increment as a metric to determine whether the best neighbour node can join the current cluster. The experimental results show BN-LGQ performs a better accuracy on predicting protein complexes and has a higher match with the reference protein complexes than MCL and MCODE algorithms. Moreover, BN-LGQ can effectively discover protein complexes with better biological significance in the PPI network.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.067973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34145689","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

Fuzzy-rough-neural-based f-information for gene selection and sample classification. 基于模糊粗糙神经的基因选择和样本分类f信息。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.066333

P Ganesh Kumar, C Rani, D Mahibha, T Aruldoss Albert Victoire

{"title":"Fuzzy-rough-neural-based f-information for gene selection and sample classification.","authors":"P Ganesh Kumar, C Rani, D Mahibha, T Aruldoss Albert Victoire","doi":"10.1504/ijdmb.2015.066333","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.066333","url":null,"abstract":"The greatest restriction in estimating the information measure for microarray data is the continuous nature of gene expression values. The traditional criterion function of f-information discretises the continuous gene expression value for calculating the probability function during gene selection. This leads to loss of biological meaning of microarray data and results in poor classification accuracy. To overcome this difficulty, the concepts of fuzzy and rough set are combined to redefine the criterion functions of f-information and are used to form candidate genes from which informative genes are selected using neural network. The performance of the proposed Fuzzy-Rough-Neural-based f-Information (FRNf-I) is evaluated using ten gene expression datasets. Simulation results show that the proposed approach compute f-information measure easily without discretisation. Statistical analysis of the test result shows that the proposed FRNf-I selects comparatively less number of genes and more classification accuracy than the other approaches reported in the literature.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/ijdmb.2015.066333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33973461","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: 10