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6th IEEE Syposium on BioInformatics and BioEngineering (BIBE 2006)

作者： Yoon, Youngmi Lee, Jongchan Park, Sanghyun Yonsei Univ Dept Comp Sci Seoul 120749 South Korea

ISBN: (纸本)0769527272

Since microarray data acquire tens of thousands of gene expression values simultaneously, they could be very useful in identifying the phenotypes of diseases. However, the results of analyzing several microarray datasets which were independently carried out with the same biological objectives, could turn out to be different. One of the main reasons is attributable to the limited number of samples involved in one microarray experiment. In order to increase the classification accuracy, it is desirable to augment the sample size by integrating and maximizing the use of independently-conducted microarray datasets. In this paper, we propose a two-stage approach which firstly integrates individual microarray datasets to overcome the problem caused by limited number of samples, and identifies informative genes, secondly builds a classifier using only the informative genes. The classifier from large samples by integrating independent microarray datasets achieves high accuracy, sensitivity, and specificity on independent test sample dataset.

关键词： bioinformatics microarray data analysis microarray data integration microarray classification informative gene selection

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BI-COPAM ENSEMBLE CLUSTERING APPLICATION TO FIVE ESCHERICHIA COLI BACTERIAL dataSETS 22

BI-COPAM ENSEMBLE CLUSTERING APPLICATION TO FIVE ESCHERICHIA...

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22nd European Signal Processing Conference (EUSIPCO)

作者： Abu-Jamous, Basel Fa, Rui Roberts, David J. Nandi, Asoke K. Brunel Univ Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England Univ Oxford John Radcliffe Hosp Natl Hlth Serv Blood & Transplant Oxford OX3 9UB England Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla Finland

ISBN: (纸本)9780992862619

Bi-CoPaM ensemble clustering has the ability to mine a set of microarray datasets collectively to identify the subsets of genes consistently co-expressed in all of them. It also has the capability of considering the entire gene set without pre-filtering as it implicitly filters out less interesting genes. While it showed success in revealing new insights into the biology of yeast, it has never been applied to bacteria. In this study, we apply Bi-CoPaM to five bacterial datasets, identifying two clusters of genes as the most consistently co-expressed. Strikingly, their average profiles are consistently negatively correlated in most of the datasets. Thus, we hypothesise that they are regulated by a common biological machinery, and that their genes with unknown biological processes may be participating in the same processes in which most of their genes known to participate. Additionally, our results demonstrate the applicability of Bi-CoPaM to a wide range of species.

关键词： Bi-CoPaM microarray data analysis gene clustering Escherichia coli bacteria

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Integrative network component analysis for regulatory network reconstruction

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4th International Symposium on Bioinformatics Research and Applications

作者： Wang, Chen Xuan, Jianhua Chen, Li Zhao, Po Wang, Yue Clarke, Robert Hoffman, Eric P. Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA 22203 USA Childrens Natl Med Ctr Res Ctr Genet Washington DC 20010 USA Georgetown Univ Dept Oncol & Physiol & Biophys Washington DC 20057 USA

ISBN: (纸本)9783540794493

Network Component analysis (NCA) has shown its effectiveness in regulator identification by inferring the transcription factor activity (TFA) when both microarray data and ChIP-on-chip data are available. However, the NCA scheme is not applicable to many biological studies due to the lack of complete ChIP-on-chip data. In this paper, we propose an integrative NCA (iNCA) approach to combine motif information, limited ChIP-on-chip data, and gene expression data for regulatory network inference. Specifically, a Bayesian framework is adopted to develop a novel strategy, namely stability analysis with topological sampling, to infer key TFAs and their downstream gene targets. The iNCA approach with stability analysis reduces the computational cost by avoiding a direct estimation of the high-dimensional distribution in a traditional Bayesian approach. Stability indices are designed to measure the goodness of the estimated TFAs and their connectivity strengths. The approach can also be used to evaluate the confidence level of different data sources, considering the inevitable inconsistency among the data sources. The iNCA approach has been applied to a time course microarray data set of muscle regeneration. The experimental results show that iNCA can effectively integrate motif information, ChIP-on-chip data and microarray data to identify key regulators and their gene targets in muscle regeneration. In particular, several identified TFAs like those of MyoD, myogenin and YY1 are well supported by biological experiments.

关键词： network component analysis gene regulatory networks microarray data analysis ChIP-on-chip muscle regeneration

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EPBC: Enhanced Possibilistic Biclustering With Application to Gene Expression analysis

EPBC: Enhanced Possibilistic Biclustering With Application t...

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3rd International Conference on Bioinformatics and Biomedical Engineering

作者： Mahfouz, Mohamed A. Ismail, Mohamed A. Univ Alexandria Fac Engn Dept Comp & Syst Engn Alexandria 21544 Egypt

ISBN: (纸本)9781424429011

Biclustering is an important data mining technique that allows identifying groups of genes which behave similarly under a subset of conditions for analyzing gene expression data from microarray technology. As a gene may play more than one biological role in conjunction with distinct groups of genes, possibilistic biclustering algorithms can give much insight towards different biological processes that each gene might participate into, along with providing a degree of participation as well, and the conditions under which its participation is most effective. This paper proposes modifications to the possibilistic biclustering algorithm introduced by Maurizio Filippone, et. al, in 2004 termed as PBC in which the mean square residue is minimized and at the same time the size of a bicluster is maximized by computing the zeros of the derivative of their objective function with respect to rows and columns memberships. Their algorithm suffers from some serious drawback First in computing the derivative of their objective function they consider the residue as a constant even though changing a membership of a row or a column affects the residue of each entry in the bicluster since it changes the average of the whole bicluster and the average of each column or row respectively. Furthermore, their algorithm is strongly sensitive to its two input parameters. In this paper the derivatives are accurately computed also their objective function is modified such that only single parameter is needed which allow us to develop a procedure for approximating a range for suitable values for this parameter. Whereas the accurate computation of the derivatives slightly increases the runtime of the proposed algorithm, experimental study on Yeast and several artificial datasets with embedded constant and additive modules having different noise levels shows that our algorithm can offer substantial improvements in terms of the quality of the output biclusters over several previously proposed biclu

关键词： Possibilistic biclustering fuzzy clustering biclustering bidimensional clustering microarray data analysis gene expression data

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KRLMM: an adaptive genotype calling method for common and low frequency variants

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BMC BIOINFORMATICS 2014年第1期15卷 1-11页

作者： Liu, Ruijie Dai, Zhiyin Yeager, Meredith Irizarry, Rafael A. Ritchie, Matthew E. Walter & Eliza Hall Inst Med Res Mol Med Div Parkville Vic 3052 Australia NCI Frederick Canc Genom Res Lab SAIC Frederick Inc Frederick MD 20877 USA Dana Farber Canc Inst Dept Biostat & Computat Biol Boston MA 02215 USA Univ Melbourne Dept Math & Stat Parkville Vic 3010 Australia Univ Melbourne Dept Med Biol Parkville Vic 3010 Australia

Background: SNP genotyping microarrays have revolutionized the study of complex disease. The current range of commercially available genotyping products contain extensive catalogues of low frequency and rare variants. Existing SNP calling algorithms have difficulty dealing with these low frequency variants, as the underlying models rely on each genotype having a reasonable number of observations to ensure accurate clustering. Results: Here we develop KRLMM, a new method for converting raw intensities into genotype calls that aims to overcome this issue. Our method is unique in that it applies careful between sample normalization and allows a variable number of clusters k (1, 2 or 3) for each SNP, where k is predicted using the available data. We compare our method to four genotyping algorithms (GenCall, GenoSNP, Illuminus and OptiCall) on several Illumina data sets that include samples from the HapMap project where the true genotypes are known in advance. All methods were found to have high overall accuracy (> 98%), with KRLMM consistently amongst the best. At low minor allele frequency, the KRLMM, OptiCall and GenoSNP algorithms were observed to be consistently more accurate than GenCall and Illuminus on our test data. Conclusions: Methods that tailor their approach to calling low frequency variants by either varying the number of clusters (KRLMM) or using information from other SNPs (OptiCall and GenoSNP) offer improved accuracy over methods that do not (GenCall and Illuminus). The KRLMM algorithm is implemented in the open-source crlmm package distributed via the Bioconductor project (http://***).

关键词： Genotyping Clustering microarray data analysis

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pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS)

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BMC BIOINFORMATICS 2019年第1期20卷 218-218页

作者： Graw, Stefan Henn, Rosalyn Thompson, Jeffrey A. Koestler, Devin C. Univ Kansas Med Ctr Dept Biostat & Data Sci Kansas City KS 66103 USA Univ Kansas Med Ctr Dept Canc Biol Kansas City KS 66103 USA

BackgroundWhen designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed *** current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user *** under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in t

关键词： DNA methylation microarray data analysis Statistical power Sample size calculation Bioconductor package Illumina human methylation BeadChip

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Investigating the Minimum Required Number of Genes for the Classification of Neuromuscular Disease microarray data

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2011年第3期15卷 349-355页

作者： Sakellariou, Argiris Sanoudou, Despina Spyrou, George Acad Athens Biomed Res Fdn Athens 11527 Greece Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 11527 Greece Natl & Kapodistrian Univ Athens Sch Med Dept Pharmacol Athens 11527 Greece

The discovery of potential microarray markers, which will expedite molecular diagnosis/prognosis and provide reliable results to clinical decision-making and treatment selection for patients, is of paramount importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most statistically significant genes, are a powerful approach toward this goal. In this paper, we investigate the minimum required subsets of genes, which best classify neuromuscular disease data. For this purpose, we implemented a methodology pipeline that facilitated the use of multiple feature selection methods and subsequent performance of data classification. Five feature selection methods on datasets from ten different neuromuscular diseases were utilized. Our findings reveal subsets of very small number of genes, which can successfully classify normal/disease samples. Interestingly, we observe that similar classification results may be obtained from different subsets of genes. The proposed methodology 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： Feature selection microarray data analysis molecular diagnosis neuromuscular diseases

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Evaluation of the Complex Transcriptional Topography of Mesenchymal Stem Cell Chondrogenesis for Cartilage Tissue Engineering

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TISSUE ENGINEERING PART A 2010年第9期16卷 2699-2708页

作者： Huang, Alice H. Stein, Ashley Mauck, Robert L. Univ Penn Dept Orthopaed Surg McKay Orthopaed Res Lab Philadelphia PA 19104 USA Univ Penn Dept Bioengn Philadelphia PA 19104 USA

Mesenchymal stem cells (MSCs) are a promising cell source for cartilage tissue engineering given their chondrogenic potential. This potential has yet to be fully realized, as the mechanical properties of MSC-based constructs are lower than those of chondrocyte-based constructs cultured identically. The aim of this study was to better understand the transcriptional underpinnings of this functional limitation. Matched chondrocytes and MSCs from three donors were cultured in agarose in a defined medium containing transforming growth factor beta 3 (TGF-beta 3). We evaluated the compressive mechanical properties and matrix deposition of maturing constructs over 56 days. Transcriptional differences between the two cell types were assessed on day 0 and 28 via microarray analysis and real-time polymerase chain reaction;differential deposition of matrix molecules was assessed by immunohistochemistry. Although the mechanical and biochemical properties of cell-seeded constructs improved with culture duration, MSC values plateaued at day 28, and remained lower than chondrocyte values. Using microarray analysis, 324 genes were identified as mis-expressed during chondrogenesis. Differential expression of 18 genes was validated, and differential deposition of proteoglycan 4 and TGF-beta-induced 68 kDa protein (TGFBI) was confirmed. Temporal expression profiles of these 18 genes showed that some genes were never expressed (chondromodulin), some were expressed at lower levels (proteoglycan 4), and some were expressed only at later time points (TGFBI) in MSCs compared to chondrocytes. These findings further define the complex transcriptional topography of MSC chondrogenesis, and provide new benchmarks for optimizing the growth of MSC-based engineered cartilage.

关键词： Immunohistochemistry Mesenchymal stem cell microarray data analysis Transcription Transforming growth factor-beta

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Inverse Langmuir method for oligonucleotide microarray analysis

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BMC BIOINFORMATICS 2009年第1期10卷 64-64页

作者： Mulders, Geert C. W. M. Barkema, Gerard T. Carlon, Enrico Katholieke Univ Leuven Inst Theoret Phys B-3000 Louvain Belgium Univ Utrecht Inst Theoret Phys NL-3584 CE Utrecht Netherlands

Background: An algorithm for the analysis of Affymetrix Genechips is presented. This algorithm, referred to as the Inverse Langmuir Method (ILM), estimates the binding of transcripts to complementary probes using DNA/RNA hybridization free energies, and the hybridization between partially complementary transcripts in solution using RNA/RNA free energies. The balance between these two competing reactions allows for the translation of background-subtracted intensities into transcript concentrations. Results: To validate the ILM, it is applied to publicly available microarray data from a multi-lab comparison study. Here, microarray experiments are performed on samples which deviate only in few genes. The log(2) fold change between these two samples, as obtained from RT-PCR experiments, agrees well with the log(2) fold change as obtained with the ILM, indicating that the ILM determines changes in the expression level accurately. We also show that the ILM allows for the identification of outlying probes, as it yields independent concentration estimates per probe. Conclusion: The ILM is robust and offers an interesting alternative to purely statistical algorithms for microarray data analysis.

关键词： Outlier Probe microarray data analysis Log2 Fold Change Median Absolute Deviation Complementary Probe

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Identification of key candidate genes and biological pathways in neuropathic pain

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COMPUTERS IN BIOLOGY AND MEDICINE 2022年 150卷 106135-106135页

作者： Cui, Chun-Yan Liu, Xiao Peng, Ming-Hui Liu, Qing Zhang, Ying Southwest Med Univ Dept Pain Hosp TCM Luzhou 646000 Sichuan Peoples R China Southwest Med Univ Dept Anesthesiol Hosp TCM Luzhou 646000 Sichuan Peoples R China Hejiang Tradit Chinese Med Hosp Luzhou 646000 Sichuan Peoples R China

Background: Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. Methods: The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus(GEO)database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation ***: Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up -regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was E

关键词： Neuropathic pain Hub genes Robust rank aggregation microarray data analysis Protein-protein interaction (PPI) network analysis

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