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Gene selection for microarray data analysis using principal component analysis.

Statistics in medicine

Wang A, Gehan EA.
PMID: 15806617
Stat Med. 2005 Jul 15;24(13):2069-87. doi: 10.1002/sim.2082.

Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become...

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Wang A, Gehan EA. Gene selection for microarray data analysis using principal component analysis. Stat Med. 2005;24(13):2069-87doi: 10.1002/sim.2082.
Wang, A., & Gehan, E. A. (2005). Gene selection for microarray data analysis using principal component analysis. Statistics in medicine, 24(13), 2069-87. https://doi.org/10.1002/sim.2082
Wang, Antai, and Gehan, Edmund A. "Gene selection for microarray data analysis using principal component analysis." Statistics in medicine vol. 24,13 (2005): 2069-87. doi: https://doi.org/10.1002/sim.2082
Wang A, Gehan EA. Gene selection for microarray data analysis using principal component analysis. Stat Med. 2005 Jul 15;24(13):2069-87. doi: 10.1002/sim.2082. PMID: 15806617.
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Gene-expression analysis and network discovery using Genevestigator.

Trends in plant science

Zimmermann P, Hennig L, Gruissem W.
PMID: 16081312
Trends Plant Sci. 2005 Sep;10(9):407-9. doi: 10.1016/j.tplants.2005.07.003.

The Genevestigator software suite belongs to a new generation of web-based tools that provide categorized quantitative information about elements (genes or annotations) contained in large microarray databases. The first version of Genevestigator, in use since 2004, has now been...

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Zimmermann P, Hennig L, Gruissem W. Gene-expression analysis and network discovery using Genevestigator. Trends Plant Sci. 2005;10(9):407-9doi: 10.1016/j.tplants.2005.07.003.
Zimmermann, P., Hennig, L., & Gruissem, W. (2005). Gene-expression analysis and network discovery using Genevestigator. Trends in plant science, 10(9), 407-9. https://doi.org/10.1016/j.tplants.2005.07.003
Zimmermann, Philip, et al. "Gene-expression analysis and network discovery using Genevestigator." Trends in plant science vol. 10,9 (2005): 407-9. doi: https://doi.org/10.1016/j.tplants.2005.07.003
Zimmermann P, Hennig L, Gruissem W. Gene-expression analysis and network discovery using Genevestigator. Trends Plant Sci. 2005 Sep;10(9):407-9. doi: 10.1016/j.tplants.2005.07.003. PMID: 16081312.
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From molecular to modular cardiology. How to interpret the millions of data that came out from large scale analysis of gene expression?.

Archives des maladies du coeur et des vaisseaux

Léger J, Swynghedauw B.
PMID: 16618026
Arch Mal Coeur Vaiss. 2006 Mar;99(3):231-6.

Cell biology is in transition from reductionism, to a more integrated science which is now preoccupied by molecular interactions acting in modules. Large-scale quantitative analysis of gene expression, including cDNA microarrays and proteomic analysis, is now applied to heart...

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Léger J, Swynghedauw B. From molecular to modular cardiology. How to interpret the millions of data that came out from large scale analysis of gene expression?. Arch Mal Coeur Vaiss. 2006;99(3):231-6
Léger, J., & Swynghedauw, B. (2006). From molecular to modular cardiology. How to interpret the millions of data that came out from large scale analysis of gene expression?. Archives des maladies du coeur et des vaisseaux, 99(3), 231-6.
Léger, J, and Swynghedauw, B. "From molecular to modular cardiology. How to interpret the millions of data that came out from large scale analysis of gene expression?." Archives des maladies du coeur et des vaisseaux vol. 99,3 (2006): 231-6.
Léger J, Swynghedauw B. From molecular to modular cardiology. How to interpret the millions of data that came out from large scale analysis of gene expression?. Arch Mal Coeur Vaiss. 2006 Mar;99(3):231-6. PMID: 16618026.
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[Parental environment alters gene expression of the child during fetal and infantile stages: life score approach and epigenetics].

[Nihon koshu eisei zasshi] Japanese journal of public health

Fujiwara T.
PMID: 18634180
Nihon Koshu Eisei Zasshi. 2008 May;55(5):344-9.

No abstract available.

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Fujiwara T. [Parental environment alters gene expression of the child during fetal and infantile stages: life score approach and epigenetics]. Nihon Koshu Eisei Zasshi. 2008;55(5):344-9
Fujiwara, T. (2008). [Parental environment alters gene expression of the child during fetal and infantile stages: life score approach and epigenetics]. [Nihon koshu eisei zasshi] Japanese journal of public health, 55(5), 344-9.
Fujiwara, Takeo. "[Parental environment alters gene expression of the child during fetal and infantile stages: life score approach and epigenetics]." [Nihon koshu eisei zasshi] Japanese journal of public health vol. 55,5 (2008): 344-9.
Fujiwara T. [Parental environment alters gene expression of the child during fetal and infantile stages: life score approach and epigenetics]. Nihon Koshu Eisei Zasshi. 2008 May;55(5):344-9. Japanese. PMID: 18634180.
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Exploration and statistical analysis of human gene expression annotations.

AMIA ... Annual Symposium proceedings. AMIA Symposium

Ceresa M, Masseroli M.
PMID: 18693993
AMIA Annu Symp Proc. 2007 Oct 11;892.

Gene expression information is a relevant resource useful to better understand gene functions. To evaluate genomic annotations sparsely available in numerous databanks accessible via Internet, we previously developed GFINDer, a Web server that performs statistical analysis of functional and...

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Ceresa M, Masseroli M. Exploration and statistical analysis of human gene expression annotations. AMIA Annu Symp Proc. 2007;892
Ceresa, M., & Masseroli, M. (2007). Exploration and statistical analysis of human gene expression annotations. AMIA ... Annual Symposium proceedings. AMIA Symposium, 892.
Ceresa, Mario, and Masseroli, Marco. "Exploration and statistical analysis of human gene expression annotations." AMIA ... Annual Symposium proceedings. AMIA Symposium vol. (2007): 892.
Ceresa M, Masseroli M. Exploration and statistical analysis of human gene expression annotations. AMIA Annu Symp Proc. 2007 Oct 11;892. PMID: 18693993.
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Correction of gene expression data: Performance-dependency on inter-replicate and inter-treatment biases.

Journal of biotechnology

Darbani B, Stewart CN, Noeparvar S, Borg S.
PMID: 25150216
J Biotechnol. 2014 Oct 20;188:100-9. doi: 10.1016/j.jbiotec.2014.08.012. Epub 2014 Aug 20.

This report investigates for the first time the potential inter-treatment bias source of cell number for gene expression studies. Cell-number bias can affect gene expression analysis when comparing samples with unequal total cellular RNA content or with different RNA...

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Darbani B, Stewart CN, Noeparvar S, et al. Correction of gene expression data: Performance-dependency on inter-replicate and inter-treatment biases. J Biotechnol. 2014;188:100-9doi: 10.1016/j.jbiotec.2014.08.012.
Darbani, B., Stewart, C. N., Noeparvar, S., & Borg, S. (2014). Correction of gene expression data: Performance-dependency on inter-replicate and inter-treatment biases. Journal of biotechnology, 188100-9. https://doi.org/10.1016/j.jbiotec.2014.08.012
Darbani, Behrooz, et al. "Correction of gene expression data: Performance-dependency on inter-replicate and inter-treatment biases." Journal of biotechnology vol. 188 (2014): 100-9. doi: https://doi.org/10.1016/j.jbiotec.2014.08.012
Darbani B, Stewart CN, Noeparvar S, Borg S. Correction of gene expression data: Performance-dependency on inter-replicate and inter-treatment biases. J Biotechnol. 2014 Oct 20;188:100-9. doi: 10.1016/j.jbiotec.2014.08.012. Epub 2014 Aug 20. PMID: 25150216.
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Effect of data combination on predictive modeling: a study using gene expression data.

AMIA ... Annual Symposium proceedings. AMIA Symposium

Osl M, Dreiseitl S, Kim J, Patel K, Baumgartner C, Ohno-Machado L.
PMID: 21347042
AMIA Annu Symp Proc. 2010 Nov 13;2010:567-71.

BACKGROUND: The quality of predictive modeling in biomedicine depends on the amount of data available for model building.OBJECTIVE: To study the effect of combining microarray data sets on feature selection and predictive modeling performance.METHODS: Empirical evaluation of stability of...

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Osl M, Dreiseitl S, Kim J, et al. Effect of data combination on predictive modeling: a study using gene expression data. AMIA Annu Symp Proc. 2010;2010:567-71
Osl, M., Dreiseitl, S., Kim, J., Patel, K., Baumgartner, C., & Ohno-Machado, L. (2010). Effect of data combination on predictive modeling: a study using gene expression data. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010567-71.
Osl, Melanie, et al. "Effect of data combination on predictive modeling: a study using gene expression data." AMIA ... Annual Symposium proceedings. AMIA Symposium vol. 2010 (2010): 567-71.
Osl M, Dreiseitl S, Kim J, Patel K, Baumgartner C, Ohno-Machado L. Effect of data combination on predictive modeling: a study using gene expression data. AMIA Annu Symp Proc. 2010 Nov 13;2010:567-71. PMID: 21347042; PMCID: PMC3041313.
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Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems.

Biotechnology journal

Cardinale S, Arkin AP.
PMID: 22649052
Biotechnol J. 2012 Jul;7(7):856-66. doi: 10.1002/biot.201200085. Epub 2012 May 31.

Despite the efforts that bioengineers have exerted in designing and constructing biological processes that function according to a predetermined set of rules, their operation remains fundamentally circumstantial. The contextual situation in which molecules and single-celled or multi-cellular organisms find...

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Cardinale S, Arkin AP. Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems. Biotechnol J. 2012;7(7):856-66doi: 10.1002/biot.201200085.
Cardinale, S., & Arkin, A. P. (2012). Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems. Biotechnology journal, 7(7), 856-66. https://doi.org/10.1002/biot.201200085
Cardinale, Stefano, and Arkin, Adam Paul. "Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems." Biotechnology journal vol. 7,7 (2012): 856-66. doi: https://doi.org/10.1002/biot.201200085
Cardinale S, Arkin AP. Contextualizing context for synthetic biology--identifying causes of failure of synthetic biological systems. Biotechnol J. 2012 Jul;7(7):856-66. doi: 10.1002/biot.201200085. Epub 2012 May 31. PMID: 22649052; PMCID: PMC3440575.
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NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression.

BJU international

[No authors listed]
PMID: 30134062
BJU Int. 2018 Aug;122(2):173-180. doi: 10.1111/bju.14452.

No abstract available.

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NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression. BJU Int. 2018;122(2):173-180doi: 10.1111/bju.14452.
(2018). NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression. BJU international, 122(2), 173-180. https://doi.org/10.1111/bju.14452
"NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression." BJU international vol. 122,2 (2018): 173-180. doi: https://doi.org/10.1111/bju.14452
NICE Advice - Prolaris gene expression assay for assessing long-term risk of prostate cancer progression: © NICE (2016) Prolaris gene expression assay for assessing long-term risk of prostate cancer progression. BJU Int. 2018 Aug;122(2):173-180. doi: 10.1111/bju.14452. PMID: 30134062.
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Computational functional genomics-based reduction of disease-related gene sets to their key components.

Bioinformatics (Oxford, England)

Lippmann C, Ultsch A, Lötsch J.
PMID: 30500872
Bioinformatics. 2019 Jul 15;35(14):2362-2370. doi: 10.1093/bioinformatics/bty986.

MOTIVATION: The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction are applied to laboratory-derived genetic data,...

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Lippmann C, Ultsch A, Lötsch J. Computational functional genomics-based reduction of disease-related gene sets to their key components. Bioinformatics. 2019;35(14):2362-2370doi: 10.1093/bioinformatics/bty986.
Lippmann, C., Ultsch, A., & Lötsch, J. (2019). Computational functional genomics-based reduction of disease-related gene sets to their key components. Bioinformatics (Oxford, England), 35(14), 2362-2370. https://doi.org/10.1093/bioinformatics/bty986
Lippmann, Catharina, et al. "Computational functional genomics-based reduction of disease-related gene sets to their key components." Bioinformatics (Oxford, England) vol. 35,14 (2019): 2362-2370. doi: https://doi.org/10.1093/bioinformatics/bty986
Lippmann C, Ultsch A, Lötsch J. Computational functional genomics-based reduction of disease-related gene sets to their key components. Bioinformatics. 2019 Jul 15;35(14):2362-2370. doi: 10.1093/bioinformatics/bty986. PMID: 30500872.
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A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository.

Database : the journal of biological databases and curation

Patra BG, Roberts K, Wu H.
PMID: 33002137
Database (Oxford). 2020 Jan 01;2020:1. doi: 10.1093/database/baaa064.

It is a growing trend among researchers to make their data publicly available for experimental reproducibility and data reusability. Sharing data with fellow researchers helps in increasing the visibility of the work. On the other hand, there are researchers...

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Patra BG, Roberts K, Wu H. A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository. Database (Oxford). 2020;2020:1doi: 10.1093/database/baaa064.
Patra, B. G., Roberts, K., & Wu, H. (2020). A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository. Database : the journal of biological databases and curation, 20201. https://doi.org/10.1093/database/baaa064
Patra, Braja Gopal, et al. "A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository." Database : the journal of biological databases and curation vol. 2020 (2020): 1. doi: https://doi.org/10.1093/database/baaa064
Patra BG, Roberts K, Wu H. A content-based dataset recommendation system for researchers-a case study on Gene Expression Omnibus (GEO) repository. Database (Oxford). 2020 Jan 01;2020:1. doi: 10.1093/database/baaa064. PMID: 33002137; PMCID: PMC7659921.
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An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries.

Database : the journal of biological databases and curation

Patra BG, Soltanalizadeh B, Deng N, Wu L, Maroufy V, Wu C, Zheng WJ, Roberts K, Wu H, Yaseen A.
PMID: 33247935
Database (Oxford). 2020 Nov 28;2020. doi: 10.1093/database/baaa074.

The exponential growth of genomic/genetic data in the era of Big Data demands new solutions for making these data findable, accessible, interoperable and reusable. In this article, we present a web-based platform named Gene Expression Time-Course Research (GETc) Platform...

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Patra BG, Soltanalizadeh B, Deng N, et al. An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries. Database (Oxford). 2020;2020doi: 10.1093/database/baaa074.
Patra, B. G., Soltanalizadeh, B., Deng, N., Wu, L., Maroufy, V., Wu, C., Zheng, W. J., Roberts, K., Wu, H., & Yaseen, A. (2020). An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries. Database : the journal of biological databases and curation, 2020. https://doi.org/10.1093/database/baaa074
Patra, Braja Gopal, et al. "An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries." Database : the journal of biological databases and curation vol. 2020 (2020). doi: https://doi.org/10.1093/database/baaa074
Patra BG, Soltanalizadeh B, Deng N, Wu L, Maroufy V, Wu C, Zheng WJ, Roberts K, Wu H, Yaseen A. An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries. Database (Oxford). 2020 Nov 28;2020. doi: 10.1093/database/baaa074. PMID: 33247935.
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Showing 1 to 12 of 52 entries