International Journal of Data Mining and Bioinformatics最新文献_第5页

A novel random forests-based feature selection method for microarray expression data analysis 基于随机森林特征选择的微阵列表达数据分析方法

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI: 10.1504/IJDMB.2015.070852

Dengju Yao, Jing Yang, Xiaojuan Zhan, Xiaorong Zhan, Zhiqiang Xie

引用次数: 21

Assessing protein-protein interactions based on the semantic similarity of interacting proteins 基于相互作用蛋白的语义相似性评估蛋白-蛋白相互作用

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI: 10.1504/IJDMB.2015.070842

Guangyu Cui, Byungmin Kim, Saud Alguwaizani, Kyungsook Han

引用次数: 6

TrieAMD: a scalable and efficient apriori motif discovery approach TrieAMD:一种可扩展的、高效的先验基序发现方法

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI: 10.1504/IJDMB.2015.070833

Isra M. Al-Turaiki, G. Badr, H. Mathkour

引用次数: 4

Mitigating bias in planning two-colour microarray experiments 双色微阵列实验计划中的减少偏差

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI: 10.1504/IJDMB.2015.070838

Nilgun Ferhatosmanoglu, T. Allen, Ümit V. Çatalyürek

引用次数: 2

An integrated strategy for functional analysis of microbial communities based on gene ontology and 16S rRNA gene 基于基因本体和16S rRNA基因的微生物群落功能分析集成策略

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI: 10.1504/IJDMB.2015.070841

Suping Deng, De-shuang Huang

引用次数: 4

Gene function prediction with knowledge from gene ontology 利用基因本体知识预测基因功能

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI: 10.1504/IJDMB.2015.070840

Ying Shen, Lin Zhang

引用次数: 1

DNA sequence and structure properties analysis reveals similarities and differences to promoters of stress responsive genes in Arabidopsis thaliana DNA序列和结构特性分析揭示了拟南芥逆境响应基因启动子的异同

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-07-01 DOI: 10.1504/IJDMB.2015.070832

P. Zhu, Yanhong Zhou, Libin Zhang, Chuang Ma

{"title":"DNA sequence and structure properties analysis reveals similarities and differences to promoters of stress responsive genes in Arabidopsis thaliana","authors":"P. Zhu, Yanhong Zhou, Libin Zhang, Chuang Ma","doi":"10.1504/IJDMB.2015.070832","DOIUrl":"https://doi.org/10.1504/IJDMB.2015.070832","url":null,"abstract":"Understanding regulatory mechanisms of stress response in plants has important biological and agricultural significances. In this study, we firstly compiled a set of genes responsive to different stresses in Arabidopsis thaliana and then comparatively analysed their promoters at both the DNA sequence and three-dimensional structure levels. Amazingly, the comparison revealed that the profiles of several sequence and structure properties vary distinctly in different regions of promoters. Moreover, the content of nucleotide T and the profile of B-DNA twist are distinct in promoters from different stress groups, suggesting Arabidopsis genes might exploit different regulatory mechanisms in response to various stresses. Finally, we evaluated the performance of two representative promoter predictors including EP3 and PromPred. The evaluation results revealed their strengths and weakness for identifying stress-related promoters, providing valuable guidelines to accelerate the discovery of novel stress-related promoters and genes in plants.","PeriodicalId":54964,"journal":{"name":"International Journal of Data Mining and Bioinformatics","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2015-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/IJDMB.2015.070832","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66730639","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

A mixture of physicochemical and evolutionary-based feature extraction approaches for protein fold recognition. 混合物理化学和进化为基础的特征提取方法的蛋白质折叠识别。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.066359

Abdollah Dehzangi, Alok Sharma, James Lyons, Kuldip K Paliwal, Abdul Sattar

{"title":"A mixture of physicochemical and evolutionary-based feature extraction approaches for protein fold recognition.","authors":"Abdollah Dehzangi, Alok Sharma, James Lyons, Kuldip K Paliwal, Abdul Sattar","doi":"10.1504/ijdmb.2015.066359","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.066359","url":null,"abstract":"Recent advancement in the pattern recognition field stimulates enormous interest in Protein Fold Recognition (PFR). PFR is considered as a crucial step towards protein structure prediction and drug design. Despite all the recent achievements, the PFR still remains as an unsolved issue in biological science and its prediction accuracy still remains unsatisfactory. Furthermore, the impact of using a wide range of physicochemical-based attributes on the PFR has not been adequately explored. In this study, we propose a novel mixture of physicochemical and evolutionary-based feature extraction methods based on the concepts of segmented distribution and density. We also explore the impact of 55 different physicochemical-based attributes on the PFR. Our results show that by providing more local discriminatory information as well as obtaining benefit from both physicochemical and evolutionary-based features simultaneously, we can enhance the protein fold prediction accuracy up to 5% better than previously reported results found 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.2015.066359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33973465","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: 19

Concepts of relative sample outlier (RSO) and weighted sample similarity (WSS) for improving performance of clustering genes: co-function and co-regulation. 提高聚类基因性能的相对样本离群值(RSO)和加权样本相似性(WSS)概念:协同功能和协同调控。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.067322

Anindya Bhattacharya, Nirmalya Chowdhury, Rajat K De

{"title":"Concepts of relative sample outlier (RSO) and weighted sample similarity (WSS) for improving performance of clustering genes: co-function and co-regulation.","authors":"Anindya Bhattacharya, Nirmalya Chowdhury, Rajat K De","doi":"10.1504/ijdmb.2015.067322","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.067322","url":null,"abstract":"Performance of clustering algorithms is largely dependent on selected similarity measure. Efficiency in handling outliers is a major contributor to the success of a similarity measure. Better the ability of similarity measure in measuring similarity between genes in the presence of outliers, better will be the performance of the clustering algorithm in forming biologically relevant groups of genes. In the present article, we discuss the problem of handling outliers with different existing similarity measures and introduce the concepts of Relative Sample Outlier (RSO). We formulate new similarity, called Weighted Sample Similarity (WSS), incorporated in Euclidean distance and Pearson correlation coefficient and then use them in various clustering and biclustering algorithms to group different gene expression profiles. Our results suggest that WSS improves performance, in terms of finding biologically relevant groups of genes, of all the considered clustering algorithms.","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.2015.067322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34039166","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

Ensemble of sparse classifiers for high-dimensional biological data. 高维生物数据的稀疏分类器集成。

IF 0.3 4区生物学

International Journal of Data Mining and Bioinformatics Pub Date : 2015-01-01 DOI: 10.1504/ijdmb.2015.069416

Sunghan Kim, Fabien Scalzo, Donatello Telesca, Xiao Hu

{"title":"Ensemble of sparse classifiers for high-dimensional biological data.","authors":"Sunghan Kim, Fabien Scalzo, Donatello Telesca, Xiao Hu","doi":"10.1504/ijdmb.2015.069416","DOIUrl":"https://doi.org/10.1504/ijdmb.2015.069416","url":null,"abstract":"Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques.","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.2015.069416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34123510","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