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IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009)

作者： Rohian, Hashmat An, Aijun Zhao, Jiashu Huang, Xiangji York Univ Dept Comp Sci & Engn Toronto ON M3J 2R7 Canada

ISBN: (纸本)9780769538853

One of the most demanding problems in mining temporal data is to identify how multivariate change associations might be discovered and used to better understand data interactions and dependencies. This paper introduces a framework to mine associations among significant changes in multivariate time-series data. Building on statistical methods, we detect significant changes in time-series data and use marginal change rates to qualify the direction of change at significant change points. Furthermore, a propositional confirmation-guided rule discovery method is used to discover associations among these significant changes. We apply our approach to gene expression data measured in yeast cell cycles and demonstrate that our method can learn novel and high-quality significant change associations among different genes. Such associations can be used to cluster genes and build gene interaction networks.

关键词： Biological data Mining and Visualization microarray data analysis Change Association Mining

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Measuring the Inter-sample Heterogeneity by Dynamic PCA Biplot

Measuring the Inter-sample Heterogeneity by Dynamic PCA Bipl...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Zhao, Bi Xue, Bin Univ S Florida Coll Arts & Sci Dept Cell Biol Microbiol & Mol Biol Tampa FL 33620 USA

ISBN: (纸本)9781509030507

In microarray/RNAseq experiments, different samples used in the same experiment may have significant levels of heterogeneity. Here, heterogeneity refers to the unique temporospatial composition of various cells in different samples. Consequently, the experimental results and subsequent data analysis may be seriously biased. To minimize the influence of heterogeneous samples in data analysis, the first step is to identify heterogeneous samples by measuring the inter- sample heterogeneity. Principal component analysis (PCA) biplot is often used for this purpose. However, the PCAbiplot is normally obtained from one set of differentially expressed genes, indicating the biplot is static and may not represent the heterogeneity dynamically. Therefore, a novel dynamic PCA biplot was developed in this project, and the advantage of this new method was exemplified in two GEO datasets.

关键词： microarray data analysis sample heterogeneity genotypic distance gpsDEG

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Investigating the minimum required number of genes for optimum classification of myopathy microarray data.

Investigating the minimum required number of genes for optim...

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9th International Conference on Information Technology and Applications in Biomedicine

作者： Sakellariou, Argiris Sanoudou, Despina Spyrou, George Acad Athens Biomed Res Fdn Athens Greece

ISBN: (纸本)9781424453788

The investigation of potential microarray markers, which in turn will speed up the molecular analysis and provide reliable results on the benefit of patient care is of significant importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most significant genes according to their expression values is a necessary component towards this goal. In the current article, we present an investigation regarding the minimum required subsets of genes, which best classify myopathy data. For this purpose, we developed a tool that facilitates the users to easily access/use multiple feature selection methods and subsequently perform classification of data. For the current study, five feature selection methods on datasets from two different myopathies have been utilized. Our findings reveal subsets of very small number of genes, which can successfully classify gene expression datasets from different patients with skeletal myopathies. In addition, we observe that similar classification results may be obtained from completely different subsets of genes. The developed tool can expedite the identification of small gene subsets with high classification accuracy that could ultimately be used in the genetics clinics for diagnostic, prognostic and pharmacogenomic purposes.

关键词： molecular diagnosis myopathy microarray data analysis feature selection

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Statistical discrimination of breast cancer microarray data

Statistical discrimination of breast cancer microarray data

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International Conference on Bioinformatics and Systems Biology (BSB)

作者： Kumar, Gautam Lahiri, Tapobarata Kumar, Rajnish Indian Inst Informat Technol Dept Appl Sci Allahabad 211012 Uttar Pradesh India

ISBN: (纸本)9781509022618

Recently published literatures indicate that, a consistent and precise classification system of microarray data is very essential for the successful identification of genes responsible for a cancer subtype and early treatment of cancer. However, in common clinical practice, diagnostic assertion of malignancies mostly relies on the morphological examination of tissues and clinical tests. In spite of the recent progress of treatment that uses semiempirical approaches involving computation methods, there are still uncertainties in cancer identification and diagnosis. High density oligonucleotide chips and genomic microarray data are being used progressively more in cancer research by generating huge expression data for thousands of genes simultaneously that poses problem of mining specific genes responsible for characterization of a specific cancer subtype. In this backdrop, we briefly address the impact of various statistical methods and their relative performances for the identification of potential probes and Discriminant genes for the breast cancer. We used Fisher's Discriminant Ratio (FDR), two tailed T-Test and vector norm on raw expression data and for each of the probe generated from the difference data accounting for up and down regulation of expression for each probes for various samples. The result indicates the potential of these methods to identify genes responsible for manifestation of breast cancer which is also well supported by the published result of experiments. The success of this approach not only gives the benefit of identification of cancer specific genes but also may help building of efficient classifier on the basis of these genes for automatic diagnostics of cancer.

关键词： Breast Cancer microarray data analysis Statistical classification Gene identification

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Generalized discriminant analysis for tumor classification with gene expression data

Generalized discriminant analysis for tumor classification w...

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5th International Conference on Machine Learning and Cybernetics

作者： Yang, Wen-Hui Dai, Dao-Qing Yan, Hong Sun Yat Sen Univ Ctr Comp Vis Guangzhou 510275 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)1424400600

DNA microarray technology is the latest and the most advanced tool for parallel measuring of the activity and interactions of thousands of genes. The challenge is that the data dimension is large compared to the number of data points, which leads to small sample size (SSS) problem. Principal Component analysis plus Linear Discriminant analysis (PCA+LDA) is a well-known technique to cope with this problem, however, it cannot completely solve the SSS problem. In this paper we propose two novel discriminant techniques. Experimental results on gene expression data sets demonstrate that our methods have good discriminating power and outperform the direct linear discriminant analysis, moreover they are more stable than the PCA+LDA approach.

关键词： microarray data analysis small sample size problem linear discriminant analysis RBF kernel classification

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SugarArray: A User-centred-designed Platform for the analysis of Lectin and Glycan microarrays 13

SugarArray: A User-centred-designed Platform for the Analysi...

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13th International Joint Conference on Biomedical Engineering Systems and Technologies

作者： Sucre, Aurora Pazos, Raquel Reichardt, Niels-Christian Garin-Muga, Alba Biodonostia eHlth Grp Donostia San Sebastian 20014 Spain Vicomtech eHlth & Biomed Applicat Donostia San Sebastian 20014 Spain Glycotechnol Lab CIC BiomaGUNE Paseo Miramon 182 San Sebastian 20014 Spain CIBER BBN Paseo Miramon 182 San Sebastian 20014 Spain

ISBN: (纸本)9789897583988

Glycan and lectin microarrays are two arising technologies, very important to the glycomics field. Glycomics is the science that focuses on defining the structures and functions of carbohydrates in nature. These microarrays provide information regarding the interactions between specific carbohydrates and proteins, and it has many applications in clinical and research settings. Nevertheless, the availability of analytical software for these types of arrays is very limited, so researchers usually perform data processing and analytical pipelines manually, which is very time consuming and prone to error. SugarArray was born as a user-friendly and intuitive stand-alone solution that process the intensity data generated from glycan or lectin array studies, and displays the results to the user in an understandable manner The solution also allows the users to manage the data as needed, create data plots and automatically generate reports. This tool was intended to simplify the processing steps of the analytical pipeline, so the users can focus on what really matters: understanding the results.

关键词： Glycomics Lectin microarray Glycan microarray microarray data analysis data Visualization

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Identifying Cancer Subnetwork Markers Using Game Theory Method

Identifying Cancer Subnetwork Markers Using Game Theory Meth...

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International Conference on Biomedical and Health Informatics (ICBHI)

作者： Farahmand, Saman Goliaei, Sama Kashani, Zahra Razaghi Moghadam Farahmand, Sina Univ Tehran Network Sci & Technol Dept Res Lab Computat Biol Tehran Iran Univ Tehran Life Sci Engn Dept Tehran Iran IIT Lab Neural Engn Res Biomed Engn Dept Chicago IL 60616 USA

ISBN: (纸本)9789811045059;9789811045042

In this paper, a novel game theory method is proposed to identify subnetwork markers by integrating gene expression profile and protein-protein interaction network. The proposed method has been evaluated on different cancer datasets in order to classify cancer phenotypes. To verify the performance of our approach, the identified subnetwork markers are compared with a greedy search method. The proposed method is capable of identifying robust subnetwork markers and presents higher classification performance.

关键词： Cancer subnetwork markers microarray data analysis Game theory Cancer classification

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Theoretically modeling microarray with the chemical equilibrium and thermodynamics

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Journal of Bioinformatics and Computational Biology 2005年第2期3卷 477-490页

作者： Xu, Mei Toronto Ont. 19 Belcourt Road Canada

To date, the idea that microarray may shed the light on cellular processes by identifying groups of genes that appear to be co-expressed seems to remain a dream. This is partly because that there are some blank (meaning the knowledge is unavailable) or even erroneous areas in the fundamental theory in this field. This paper attempts to present the digest of microarray hybridization system with chemical thermodynamics, theoretically clarifying some misunderstandings and looking for answers to some critical questions around this technology, such as the mechanisms and conditions of quantitative measuring by hybridization reaction, the reasons of inconsistency of the data and the analysis results and the solutions, how to analyze the data, etc. A theoretical model for the next generation of microarray is proposed. We believe that this model is universal, laying the foundation for microarray technology from array design through the data analysis. © Imperial College Press.

关键词： Affymetrix Chemical equilibrium Chemical thermodynamics Information microarray microarray data analysis

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INTEGRATING PATHWAY ENRICHMENT AND GENE NETWORK analysis PROVIDES ACCURATE DISEASE CLASSIFICATION

INTEGRATING PATHWAY ENRICHMENT AND GENE NETWORK ANALYSIS PRO...

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International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS)

作者： Ibrahim, Maysson Al-Haj Jassim, Sabah Cawthorne, Michael A. Langlands, Kenneth Buckingham Univ Dept Appl Comp Buckingham England

ISBN: (纸本)9789898425904

At present, a range of clinical indicators are used to gain insight into the course a newly-presented individual's disease may take, and so inform treatment regimes. However, such indicators are not absolutely predictive and patients with apparently low-risk disease may follow a more aggressive course. Advances in molecular medicine offer the hope of improved disease stratification and personalised treatment. For example, the identification of "genetic sigantures" characteristics of disease subtypes is facilitated by high-throughput transcriptional profiling techniques (microarray) in which gene expression levels for thousands of gene are measured across a range of biopsy samples. However, the selection of a compact gene set conferring the most clinically-relevant information from complex and high-dimensional microarray datasets is a challenging task. We reduced this complexity using a Pathway Enrichment and Gene Network analysis (PEGNA) method, which integrated gene expression data with prior biological knowledge to select a group of strongly-correlated genes providing accurate discrimination of complex disease subtypes. In our method, pathway enrichment analysis was applied to a microarray dataset in order to identify the most impacted biological processes. Secondly, we used gene network analysis to find a group of strongly-correlated genes from which subsets of genes were selected to use for disease classification with a support vector machine classifier. In this way, we were able to more accurately classify disease states, using smaller numbers of genes, compared to other methods across a range of biological datasets.

关键词： Disease classification Biomarker discovery Pathway enrichment Gene network analysis microarray data analysis

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A Gene Ranking Method Using Text-Mining for the Identification of Disease Related Genes

A Gene Ranking Method Using Text-Mining for the Identificati...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lee, Hyungmin Shin, Miyoung Hong, Munpyo Kyungpook Natl Univ Grad Sch EECS Daegu South Korea Sungkyunkwan Univ Coll Liberal Arts Seoul South Korea

ISBN: (纸本)9781424483075

For the identification of significant genes involved in specific diseases, microarray gene expression profiles have been widely used to prioritize candidate genes. In this paper, we propose a new gene ranking method that employs gene-gene relations extracted from literature along with gene expression scores obtained from microarrays. Here the gene-gene relations are extracted by taking a hybrid approach which is a combination of syntactic analysis and co-occurrence based approaches. Specifically, we perform the syntactic parsing on the text and then, within each clause of the parsed sentence, the co-occurred gene names are considered to be mutually related. Both the gene network derived from the gene-gene relations obtained in the above way and the gene expression scores are given as the inputs to the GeneRank algorithm. For the evaluation of our approach, we conducted experiments with the publicly available prostate cancer data. The results show that our method is superior in the precision and the recall to the original GeneRank which employs the gene-gene relations built from gene ontology annotations. Furthermore, our hybrid approach to the gene-gene relation extraction produces better prioritization of truly disease-related genes in top ranks than the existing popular co-occurrence approach.

关键词： gene ranking text-mining relation extraction disease related genes microarray data analysis

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