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Array data extractor (ADE): A LabVIEW program to extract and merge gene array data

BMC Research Notes 2013年第1期6卷 1-7页

作者： Kurtenbach, Stefan Kurtenbach, Sarah Zoidl, Georg Faculty of Health Department of Psychology Molecular and Cellular Neuroscience York University Toronto ON M3J 1P3 4700 Keele Street Canada

Background: Large data sets from gene expression array studies are publicly available offering information highly valuable for research across many disciplines ranging from fundamental to clinical research. Highly advanced bioinformatics tools have been made available to researchers, but a demand for user-friendly software allowing researchers to quickly extract expression information for multiple genes from multiple studies persists. Findings. Here, we present a user-friendly LabVIEW program to automatically extract gene expression data for a list of genes from multiple normalized microarray datasets. Functionality was tested for 288 class A G protein-coupled receptors (GPCRs) and expression data from 12 studies comparing normal and diseased human hearts. Results confirmed known regulation of a beta 1 adrenergic receptor and further indicate novel research targets. Conclusions: Although existing software allows for complex data analyses, the LabVIEW based program presented here, "Array data Extractor (ADE)", provides users with a tool to retrieve meaningful information from multiple normalized gene expression datasets in a fast and easy way. Further, the graphical programming language used in LabVIEW allows applying changes to the program without the need of advanced programming knowledge. © 2013 Kurtenbach et al.;licensee BioMed Central Ltd.

关键词： ADE Array data Extractor Combining of multiple datasets Gene expression array Merge p-values Meta-analysis microarray data analysis Statistics

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Framework for identifying common aberrations in DNA copy number data

Framework for identifying common aberrations in DNA copy num...

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11th Annual International Conference on Research in Computational Molecular Biology

作者： Ben-Dor, Amir Lipson, Doron Tsalenko, Anya Reimers, Mark Baumbusch, Lars O. Barrett, Michael T. Weinstein, John N. Borresen-Dale, Anne-Lise Yakhini, Zohar Agilent Labs Santa Clara CA 95051 USA Technion Dept Comp Sci Haifa Israel NCI Bethesda MD USA Inst Canc Res Dept Genet Rikshosp Radiumhosp Med Ctr Bethesda MD USA Translat Genom Res Inst Phoenix AZ USA

ISBN: (纸本)9783540716808

High-resolution array comparative genomic hybridization (aCGH) provides exon-level mapping of DNA aberrations in cells or tissues. Such aberrations are central to carcinogenesis and, in many cases, central to targeted therapy of the cancers. Some of the aberrations are sporadic, one-of-a-kind changes in particular tumor samples;others occur frequently and reflect common themes in cancer biology that have interpretable, causal ramifications. Hence, the difficult task of identifying and mapping common, overlapping genomic aberrations (including amplifications and deletions) across a sample set is an important one-, it can provide insight for the discovery of oncogenes, tumor suppressors, and the mechanisms by which they drive cancer development. In this paper we present an efficient computational framework for identification and statistical characterization of genomic aberrations that are common to multiple cancer samples in a CGH data set. We present and compare three different algorithmic approaches within the context of that framework. Finally, we apply our methods to two datasets - a collection of 20 breast cancer samples and a panel of 60 diverse human tumor cell lines (the NCI-60). Those analyses identified both known and novel common aberrations containing cancer-related genes. The potential impact of the analytical methods is well demonstrated by new insights into the patterns of deletion of CDKN2A (p16), a tumor suppressor gene crucial for the genesis of many types of cancer.

关键词： CGH cancer microarray data analysis common aberrations breast cancer NCI-60

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18th Australian Joint Conference on Artificial Intelligence

作者： Chen, XS Li, JY Daggard, G Huang, XD Univ So Queensland Dept Math & Comp Dept Biol & Phys Sci Toowoomba Qld 4350 Australia Univ New England Dept Math Stat & Comp Sci Armidale NSW 2350 Australia

ISBN: (纸本)3540304622

In this paper we propose a clustering algorithm called s-Cluster for analysis of gene expression data based on pattern-similarity. The algorithm captures the tight clusters exhibiting strong similar expression patterns in microarray data,and allows a high level of overlap among discovered clusters without completely grouping all genes like other algorithms. This reflects the biological fact that not all functions are turned on in an experiment, and that many genes are co-expressed in multiple groups in response to different stimuli. The experiments have demonstrated that the proposed algorithm successfully groups the genes with strong similar expression patterns and that the found clusters are interpretable.

关键词： data mining bioinformatics microarray data analysis clustering

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LiCl Containing Thermosensitive Formulation Improves Hemostasis, Wound Healing, and Hair Regrowth

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REGENERATIVE ENGINEERING AND TRANSLATIONAL MEDICINE 2021年第3期7卷 362-378页

作者： Verma, Yogesh Kumar Verma, Ranjan Sing, Ajay Kumar Gangenahalli, Gurudutta Inst Nucl Med & Allied Sci Div Stem Cell & Gene Therapy Res Delhi 110054 India

Lithium is clinically used for the treatment of the bipolar manic disorder. In this study, we have evaluated its role in the wound healing process on the basis of our previous microarray studies where many genes related to wound healing were upregulated. Formulations containing LiCl, poloxamer 407 (P407), alginate, and serum were prepared so as to make them "active" thermosensitive preparation, which may participate in different phases of wound healing. The formulations were analyzed for their physical properties, hemostasis, healing, hair regrowth, and expression of genes related to wound healing. The formulations showed a thermoreversible property, improved hemostasis in a liver laceration, and improved healing of full-thickness skin wound and hair regrowth in BALB/c mice and Sprague Dawley rat model. Further, we found genes which facilitate repair of the wound, e.g., TGF beta, COX2, and Col1a1, were also upregulated in RT-PCR assay. Particularly, we have detected the potential role of KRTAP10-2 gene in LiCl-induced hair regrowth. These formulations are likely to have application in the treatment of various injuries including accidents, trauma, diabetic wound, radiotherapy, war injuries, etc., where facilitated wound healing is essential. Lay Summary Our studies on microarray data analysis of LiCl-treated cells showed upregulation of wound healing genes. A formulation containing LiCl was evaluated in vitro and in vivo, which facilitated various stages of wound healing. The temperature-dependent phase changing property of this formulation makes it effective at places where the temperature remains subzero. The formulation may be prepared with other ingredients such as analgesics and antibiotics and could be packed as per the requirement such as spray, cream, gel, etc. Future Studies The formulation may further be evaluated in the GLP (Good Laboratory Practice) setting;this may help in the repurposing of LiCl and other constituents for wound healing purpose.

关键词： microarray data analysis Poloxamer LiCl Hemostasis Healing Hair regrowth

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PreCLAS: An Evolutionary Tool for Unsupervised Feature Selection 15th

PreCLAS: An Evolutionary Tool for Unsupervised Feature Selec...

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15th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Carballido, Jessica A. Ponzoni, Ignacio Cecchini, Rocio L. Univ Nacl Sur Dept Comp Sci & Engn Inst Comp Sci & Engn UNS CONICET Bahia Blanca Buenos Aires Argentina

ISBN: (纸本)9783030617059;9783030617042

Several research areas are being faced with data matrices that are not suitable to be managed with traditional clustering, regression, or classification strategies. For example, biological so-called omic problems present models with thousands or millions of rows and less than a hundred columns. This matrix structure hinders the successful progress of traditional data analysis methods and thus needs some means for reducing the number of rows. This article presents an unsupervised approach called PreCLAS for preprocessing matrices with dimension problems to obtain data that are apt for clustering and classification strategies. The PreCLAS was implemented as an unsupervised strategy that aims at finding a submatrix with a drastically reduced number of rows, preferring those rows that together present some group structure. Experimentation was carried out in two stages. First, to assess its functionality, a benchmark dataset was studied in a clustering context. Then, a microarray dataset with genomic information was analyzed, and the PreCLAS was used to select informative genes in the context of classification strategies. Experimentation showed that the new method performs successfully at drastically reducing the number of rows of a matrix, smartly performing unsupervised feature selection for both classification and clustering problems.

关键词： Clustering tendency Classification strategies Evolutionary algorithm Unsupervised feature selection microarray data analysis

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A Novel SPDF Ensemble Classifier for Cancer Classification

A Novel SPDF Ensemble Classifier for Cancer Classification

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9th International Conference on Natural Computation (ICNC)

作者： Zhang, Chunying Wu, Fang Tong, Tuopeng Chen, Sun Song, Kai Ma, Min Zheng, Guangqiang Tianjin Univ Sch Chem Engn & Technol Tianjin 300072 Peoples R China

ISBN: (纸本)9781467347143

To gain more accurate and reliable cancer classification results through DNA microarray analysis, a novel ensemble classifier SPDF (Subspace Partial least square based Decision Forest) is developed. The original data are split into subspaces by column. For each subspace, partial least square (PLS) is applied to extract orthogonal latent variables (LVs). In conjunction with the Minimal Redundancy and Maximal Relevance (MRMR) as the gene-selection preprocessing method, the adverse effect of the too high dimensional variables with too small samples could be overcome successfully. Then, all available LVs are aggregated as the new training data where the Decision Forest is trained for classification. Therefore relying on the feature extraction power of PLS and the orthogonality of LVs, the multi-colinearity and high noise inherent in microarray data could be eliminated effectively. Moreover, the Decision Forest could enhance the data variety and further lighten the dependence of the classification results to the given data. The applications to two microarray datasets show that compared with Rotation Forest, Bagging and Boosting, the new SPDF method yields consistently accurate and robust predictive performance, with the maximal improvement reaching 7.26% in terms of classification accuracy on the Colon cancer classification.

关键词： microarray data analysis ensemble classifier partial least squares feature extraction cancer classification

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Noise-robust soft clustering of gene expression time-course data

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Journal of Bioinformatics and Computational Biology 2005年第4期3卷 965-988页

作者： Futschik, Matthias E. Carlisle, Bronwyn Institute of Theoretical Biology Humboldt-University 10115 Berlin Invalidenstr. 43 Germany Department of Information Science University of Otago Dunedin PO Box 56 New Zealand Department of Biochemistry University of Otago Dunedin PO Box 56 New Zealand

Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, this is generally not the case for microarray time-course data, where gene clusters frequently overlap. Additionally, hard clustering algorithms are often highly sensitive to noise. To overcome the limitations of hard clustering, we applied soft clustering which offers several advantages for researchers. First, it generates accessible internal cluster structures, i.e. it indicates how well corresponding clusters represent genes. This can be used for the more targeted search for regulatory elements. Second, the overall relation between clusters, and thus a global clustering structure, can be defined. Additionally, soft clustering is more noise robust and a priori pre-filtering of genes can be avoided. This prevents the exclusion of biologically relevant genes from the data analysis. Soft clustering was implemented here using the fuzzy c-means algorithm. Procedures to find optimal clustering parameters were developed. A software package for soft clustering has been developed based on the open-source statistical language R. The package called Mfuzz is freely available. © Imperial College Press.

关键词： Clustering microarray data analysis Noise-robustness

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Comparative analysis of spatial patterns of gene expression in Drosophila melanogaster imaginal discs

Comparative analysis of spatial patterns of gene expression ...

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11th Annual International Conference on Research in Computational Molecular Biology

作者： Harmon, Cyrus L. Ahammad, Parvez Hammonds, Ann Weiszmann, Richard Celniker, Susan E. Sastry, S. Shankar Rubin, Gerald M. Univ Calif Berkeley Dept Mol & Cell Biol Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Dept Genome Sci Berkeley CA 94720 USA

ISBN: (纸本)9783540716808

Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding the roles of various genes in the development of metazoan organisms. To address this problem, we have developed high-throughput methods for generating images of gene expression in Drosophila melanogaster imaginal discs and for the automated analysis of these images. Our method automatically learns tissue shapes from a small number of manually segmented training examples and automatically aligns, extracts and scores new images, which are analyzed to generate gene expression maps for each gene. We have developed a reverse lookup procedure that enables us to identify genes that have spatial expression patterns most similar to a given gene of interest. Our methods enable us to cluster both the genes and the pixels that of the maps, thereby identifying sets of genes that have similar patterns, and regions of the tissues of interest that have similar gene expression profiles across a large number of genes.

关键词： genomic imaging gene expression analysis clustering microarray data analysis imaginal discs

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Latent variable and nICA modeling of pathway gene module composite

Latent variable and nICA modeling of pathway gene module com...

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28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Gong, Ting Zhu, Yitan Xuan, Jianhua Li, Huai Clarke, Robert Hoffman, Eric P. Wang, Yue Virginia Polytech Inst & State Univ Dept ECE Arlington VA USA Catholic Univ Amer Dept EECS Washington DC 20064 USA Natl Inst Hlth RRB Bioinformat Unit Baltimore MD 21201 USA Georgetown Univ Lombardi Canc Ctr Washington DC 20057 USA Childrens Natl Med Ctr Res Ctr Genet Med Washington DC USA

ISBN: (纸本)9781424400324

In this paper, we report a new gene clustering approach - non-negative independent component analysis (nICA) - for microarray data analysis. Due to positive nature of molecular expressions, nICA fits better to the reality of corresponding putative biological processes. In conjunction with nICA model, VIsual Statistical data Analyzer (VISDA) is applied to group genes into modules in the latent variable space. The experimental results show that significant enrichment of gene annotations within clusters can be obtained.

关键词： non-negative ICA latent variable model gene clustering module discovery microarray data analysis

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Genomics in Drug Discovery and Development 1

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2008年

作者： Dimitri Semizarov Ph.D. Eric Blomme D.V.M., Ph.D.

ISBN: (数字)9780470409770

ISBN: (纸本)9780470096048

Early characterization of toxicity and efficacy would significantly impact the overall productivity of pharmaceutical R&D and reduce drug candidate attrition and failure. By describing the available platforms and weighing their relative advantages and disadvantages, including microarray data analysis, Genomics in Drug Discovery and Development introduces readers to the biomarker, pharmacogenomic, and toxicogenomics toolbox. The authors provide a valuable resource for pharmaceutical discovery scientists, preclinical drug safety department personnel, regulatory personnel, discovery toxicologists, and safety scientists, drug development professionals, and pharmaceutical scientists.

关键词： drug toxicity drug efficacy pharmaceutical R&D pharmaceuticals preclinical drug safety personal medicine genetic therapy genomics proteomics microarray data analysis toxicogenomics

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