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You searched for subject:(microarray data). Showing records 1 – 30 of 102 total matches.

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University of Georgia

1. Yang, Yiwen. Phylogenetic analysis of cancer microarray data.

Degree: MS, Statistics, 2014, University of Georgia

 Recent advances in biotechnology and the availability of genetic data have greatly facilitated the molecular exploration of cancer. Cancer microarray data analysis provides new insights… (more)

Subjects/Keywords: Microarray data

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APA (6th Edition):

Yang, Y. (2014). Phylogenetic analysis of cancer microarray data. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/yang_yiwen_201412_ms

Chicago Manual of Style (16th Edition):

Yang, Yiwen. “Phylogenetic analysis of cancer microarray data.” 2014. Masters Thesis, University of Georgia. Accessed March 29, 2020. http://purl.galileo.usg.edu/uga_etd/yang_yiwen_201412_ms.

MLA Handbook (7th Edition):

Yang, Yiwen. “Phylogenetic analysis of cancer microarray data.” 2014. Web. 29 Mar 2020.

Vancouver:

Yang Y. Phylogenetic analysis of cancer microarray data. [Internet] [Masters thesis]. University of Georgia; 2014. [cited 2020 Mar 29]. Available from: http://purl.galileo.usg.edu/uga_etd/yang_yiwen_201412_ms.

Council of Science Editors:

Yang Y. Phylogenetic analysis of cancer microarray data. [Masters Thesis]. University of Georgia; 2014. Available from: http://purl.galileo.usg.edu/uga_etd/yang_yiwen_201412_ms


Laurentian University

2. Almoeirfi, Makkeyah. Classification approaches for microarray gene expression data analysis .

Degree: 2015, Laurentian University

 The technology of Microarray is among the vital technological advancements in bioinformatics. Usually, microarray data is characterized by noisiness as well as increased dimensionality. Therefore,… (more)

Subjects/Keywords: Microarray; data; gene

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APA (6th Edition):

Almoeirfi, M. (2015). Classification approaches for microarray gene expression data analysis . (Thesis). Laurentian University. Retrieved from https://zone.biblio.laurentian.ca/dspace/handle/10219/2535

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Almoeirfi, Makkeyah. “Classification approaches for microarray gene expression data analysis .” 2015. Thesis, Laurentian University. Accessed March 29, 2020. https://zone.biblio.laurentian.ca/dspace/handle/10219/2535.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Almoeirfi, Makkeyah. “Classification approaches for microarray gene expression data analysis .” 2015. Web. 29 Mar 2020.

Vancouver:

Almoeirfi M. Classification approaches for microarray gene expression data analysis . [Internet] [Thesis]. Laurentian University; 2015. [cited 2020 Mar 29]. Available from: https://zone.biblio.laurentian.ca/dspace/handle/10219/2535.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Almoeirfi M. Classification approaches for microarray gene expression data analysis . [Thesis]. Laurentian University; 2015. Available from: https://zone.biblio.laurentian.ca/dspace/handle/10219/2535

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Wright State University

3. Mao, Shihong. Comparative Microarray Data Mining.

Degree: PhD, Computer Science and Engineering PhD, 2007, Wright State University

 As a revolutionary technology, microarrays have great potential to provide genome-wide patterns of gene expression, to make accurate medical diagnosis, and to explore genetic causes… (more)

Subjects/Keywords: Computer Science; data mining; microarray data; comparative

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APA (6th Edition):

Mao, S. (2007). Comparative Microarray Data Mining. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415

Chicago Manual of Style (16th Edition):

Mao, Shihong. “Comparative Microarray Data Mining.” 2007. Doctoral Dissertation, Wright State University. Accessed March 29, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.

MLA Handbook (7th Edition):

Mao, Shihong. “Comparative Microarray Data Mining.” 2007. Web. 29 Mar 2020.

Vancouver:

Mao S. Comparative Microarray Data Mining. [Internet] [Doctoral dissertation]. Wright State University; 2007. [cited 2020 Mar 29]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415.

Council of Science Editors:

Mao S. Comparative Microarray Data Mining. [Doctoral Dissertation]. Wright State University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1198695415


North Carolina State University

4. Chopra, Pankaj. Data Mining Techniques to Enable Large-scale Exploratory Analysis of Heterogeneous Scientific Data.

Degree: PhD, Computer Science, 2009, North Carolina State University

 Recent advances in microarray technology have enabled scientists to simultaneously gather data on thousands of genes. However, due to the complexity of genetic interactions, the… (more)

Subjects/Keywords: pathway analysis; data mining; gene expression; data mining genetic pathways; microarray data mining; microarray clustering

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APA (6th Edition):

Chopra, P. (2009). Data Mining Techniques to Enable Large-scale Exploratory Analysis of Heterogeneous Scientific Data. (Doctoral Dissertation). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/4168

Chicago Manual of Style (16th Edition):

Chopra, Pankaj. “Data Mining Techniques to Enable Large-scale Exploratory Analysis of Heterogeneous Scientific Data.” 2009. Doctoral Dissertation, North Carolina State University. Accessed March 29, 2020. http://www.lib.ncsu.edu/resolver/1840.16/4168.

MLA Handbook (7th Edition):

Chopra, Pankaj. “Data Mining Techniques to Enable Large-scale Exploratory Analysis of Heterogeneous Scientific Data.” 2009. Web. 29 Mar 2020.

Vancouver:

Chopra P. Data Mining Techniques to Enable Large-scale Exploratory Analysis of Heterogeneous Scientific Data. [Internet] [Doctoral dissertation]. North Carolina State University; 2009. [cited 2020 Mar 29]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4168.

Council of Science Editors:

Chopra P. Data Mining Techniques to Enable Large-scale Exploratory Analysis of Heterogeneous Scientific Data. [Doctoral Dissertation]. North Carolina State University; 2009. Available from: http://www.lib.ncsu.edu/resolver/1840.16/4168


NSYSU

5. Chen, Ming-cheng. A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification.

Degree: Master, Computer Science and Engineering, 2008, NSYSU

 The microarray technology plays an important role of clinical oncology field. The patient can be diagnosed a symptom about cancer through microarray data. Currently, to… (more)

Subjects/Keywords: Microarray Data Classification; Genetic Algorithm; Fuzzy Theory

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APA (6th Edition):

Chen, M. (2008). A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chen, Ming-cheng. “A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification.” 2008. Thesis, NSYSU. Accessed March 29, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chen, Ming-cheng. “A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification.” 2008. Web. 29 Mar 2020.

Vancouver:

Chen M. A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification. [Internet] [Thesis]. NSYSU; 2008. [cited 2020 Mar 29]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen M. A GA-Fuzzy-Based Voting Mechanism for Microarray Data Classification. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0930108-105834

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Indian Institute of Science

6. Mukhopadhyay, Sayan. Variance of Difference as Distance Like Measure in Time Series Microarray Data Clustering.

Degree: 2014, Indian Institute of Science

 Our intention is to find similarity among the time series expressions of the genes in microarray experiments. It is hypothesized that at a given time… (more)

Subjects/Keywords: Microarray Data Clustering; Time Series Microarray Data Clustering; Time Series Microarrays; Microarray Data Analysis; Microarray Gene Expression Data; Gene Expression Data Clustering; Time Series Gene Expression Data; Distance-based Data Clustering; Cancer related Gene Expression Data; Gene Ontology; Microarray Time Series; Autoregressive Model; Computer Science

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APA (6th Edition):

Mukhopadhyay, S. (2014). Variance of Difference as Distance Like Measure in Time Series Microarray Data Clustering. (Thesis). Indian Institute of Science. Retrieved from http://etd.iisc.ernet.in/handle/2005/2986 ; http://etd.ncsi.iisc.ernet.in/abstracts/3848/G26721-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mukhopadhyay, Sayan. “Variance of Difference as Distance Like Measure in Time Series Microarray Data Clustering.” 2014. Thesis, Indian Institute of Science. Accessed March 29, 2020. http://etd.iisc.ernet.in/handle/2005/2986 ; http://etd.ncsi.iisc.ernet.in/abstracts/3848/G26721-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mukhopadhyay, Sayan. “Variance of Difference as Distance Like Measure in Time Series Microarray Data Clustering.” 2014. Web. 29 Mar 2020.

Vancouver:

Mukhopadhyay S. Variance of Difference as Distance Like Measure in Time Series Microarray Data Clustering. [Internet] [Thesis]. Indian Institute of Science; 2014. [cited 2020 Mar 29]. Available from: http://etd.iisc.ernet.in/handle/2005/2986 ; http://etd.ncsi.iisc.ernet.in/abstracts/3848/G26721-Abs.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mukhopadhyay S. Variance of Difference as Distance Like Measure in Time Series Microarray Data Clustering. [Thesis]. Indian Institute of Science; 2014. Available from: http://etd.iisc.ernet.in/handle/2005/2986 ; http://etd.ncsi.iisc.ernet.in/abstracts/3848/G26721-Abs.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Anna University

7. Valarmathie, P. Analysis of imputed microarray data using data mining techniques; -.

Degree: Information and Communication Engineering, 2014, Anna University

Rapid development of huge amount of data like microarray gene expression data that is stored in databases need powerful tools so as to analyze and… (more)

Subjects/Keywords: Data mining; Data mining techniques; Hybrid clustering technique; Imputed microarray data; Information and communication engineering; Microarray

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APA (6th Edition):

Valarmathie, P. (2014). Analysis of imputed microarray data using data mining techniques; -. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/24984

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Valarmathie, P. “Analysis of imputed microarray data using data mining techniques; -.” 2014. Thesis, Anna University. Accessed March 29, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/24984.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Valarmathie, P. “Analysis of imputed microarray data using data mining techniques; -.” 2014. Web. 29 Mar 2020.

Vancouver:

Valarmathie P. Analysis of imputed microarray data using data mining techniques; -. [Internet] [Thesis]. Anna University; 2014. [cited 2020 Mar 29]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/24984.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Valarmathie P. Analysis of imputed microarray data using data mining techniques; -. [Thesis]. Anna University; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/24984

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Newcastle

8. Arefin, Ahmed Shamsul. An integrated, fast and scalable approach for large-scale biological network analysis.

Degree: κNN) graph-based approach. Moreover, instead of constructing the graph/network on a highly expensive system, we show its construction on graphics processing units (GPUs), which are now widely available as inexpensive, highly parallel devices. The outcome of our κNN graph construction method (termed as GPU-FS-κNN) can be used to carry out many other important computational tasks. In particular, we demonstrate its applications in two popular data analysis methods: clustering and centrality analysis. To do this, we first propose a GPU-based fast method for constructing minimum spanning trees (MST) from the κNN graphs (termed as κNN-Borůvka) and a method for partitioning the trees in an agglomerative fashion (termed as κNN-Borůvka-Agglomerative). Then, we demonstrate the use of κNN graphs in accelerating and scaling the computations of two degree-based (e.g., degree and eigenvectors) and three shortest path based (closeness, eccentricity and betweenness) centrality metrics. At the end, we integrate the developed methods and combinedly apply them on two publicly available gene-expression data sets (Alzheimer’s disease and breast cancer, two popular data analysis methods: clustering and centrality analysis. To do this, we first propose a GPU-based fast method for constructing minimum spanning trees (MST) from the κNN graphs (termed as κNN-Borůvka) and a method for partitioning the trees in an agglomerative fashion (termed as κNN-Borůvka-Agglomerative). Then, we demonstrate the use of κNN graphs in accelerating and scaling the computations of two degree-based (e.g., degree and eigenvectors) and three shortest path based (closeness, eccentricity and betweenness) centrality metrics. At the end, we integrate the developed methods and combinedly apply them on two publicly available gene-expression data sets (Alzheimer’s disease and breast cancer, 2013, University of Newcastle

Research Doctorate - Computer Science

THE amount of data in our world has been exploding. Computer-based methods used to analyze data ten years ago are… (more)

Subjects/Keywords: data clustering; centrality analysis; GPU-based computation; microarray-based data analysis

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APA (6th Edition):

Arefin, A. S. (2013). An integrated, fast and scalable approach for large-scale biological network analysis. (Doctoral Dissertation). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/938499

Chicago Manual of Style (16th Edition):

Arefin, Ahmed Shamsul. “An integrated, fast and scalable approach for large-scale biological network analysis.” 2013. Doctoral Dissertation, University of Newcastle. Accessed March 29, 2020. http://hdl.handle.net/1959.13/938499.

MLA Handbook (7th Edition):

Arefin, Ahmed Shamsul. “An integrated, fast and scalable approach for large-scale biological network analysis.” 2013. Web. 29 Mar 2020.

Vancouver:

Arefin AS. An integrated, fast and scalable approach for large-scale biological network analysis. [Internet] [Doctoral dissertation]. University of Newcastle; 2013. [cited 2020 Mar 29]. Available from: http://hdl.handle.net/1959.13/938499.

Council of Science Editors:

Arefin AS. An integrated, fast and scalable approach for large-scale biological network analysis. [Doctoral Dissertation]. University of Newcastle; 2013. Available from: http://hdl.handle.net/1959.13/938499


University of Pretoria

9. Law, Philip John. Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data.

Degree: Biochemistry, 2009, University of Pretoria

Microarray technology makes it possible to identify changes in gene expression of an organism, under various conditions. The challenge to researchers that employ microarray expression… (more)

Subjects/Keywords: Technology; Microarray data; Genes; Modules in madiba; Web-based toolkit; UCTD

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APA (6th Edition):

Law, P. J. (2009). Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data. (Masters Thesis). University of Pretoria. Retrieved from http://hdl.handle.net/2263/27204

Chicago Manual of Style (16th Edition):

Law, Philip John. “Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data.” 2009. Masters Thesis, University of Pretoria. Accessed March 29, 2020. http://hdl.handle.net/2263/27204.

MLA Handbook (7th Edition):

Law, Philip John. “Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data.” 2009. Web. 29 Mar 2020.

Vancouver:

Law PJ. Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data. [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2020 Mar 29]. Available from: http://hdl.handle.net/2263/27204.

Council of Science Editors:

Law PJ. Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data. [Masters Thesis]. University of Pretoria; 2009. Available from: http://hdl.handle.net/2263/27204


Laurentian University

10. [No author]. Classification approaches for microarray gene expression data analysis .

Degree: 2015, Laurentian University

 The technology of Microarray is among the vital technological advancements in bioinformatics. Usually, microarray data is characterized by noisiness as well as increased dimensionality. Therefore,… (more)

Subjects/Keywords: microarray data; vector machine classifier; radial kernel; linear kernel

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APA (6th Edition):

author], [. (2015). Classification approaches for microarray gene expression data analysis . (Thesis). Laurentian University. Retrieved from https://zone.biblio.laurentian.ca/dspace/handle/10219/2531

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

author], [No. “Classification approaches for microarray gene expression data analysis .” 2015. Thesis, Laurentian University. Accessed March 29, 2020. https://zone.biblio.laurentian.ca/dspace/handle/10219/2531.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

author], [No. “Classification approaches for microarray gene expression data analysis .” 2015. Web. 29 Mar 2020.

Vancouver:

author] [. Classification approaches for microarray gene expression data analysis . [Internet] [Thesis]. Laurentian University; 2015. [cited 2020 Mar 29]. Available from: https://zone.biblio.laurentian.ca/dspace/handle/10219/2531.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

author] [. Classification approaches for microarray gene expression data analysis . [Thesis]. Laurentian University; 2015. Available from: https://zone.biblio.laurentian.ca/dspace/handle/10219/2531

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

11. Dinakaran K. Data mining techniques to classify microarray gene expression data;.

Degree: Data mining techniques to classify microarray gene expression data, 2015, Anna University

Modern medicine and healthcare technologies have generated a newlinegreat deal of information that is stored in the databases Extracting useful newlineknowledge and providing scientific assessment… (more)

Subjects/Keywords: Data mining; information and communication engineering; microarray gene

Page 1

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APA (6th Edition):

K, D. (2015). Data mining techniques to classify microarray gene expression data;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/38566

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

K, Dinakaran. “Data mining techniques to classify microarray gene expression data;.” 2015. Thesis, Anna University. Accessed March 29, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/38566.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

K, Dinakaran. “Data mining techniques to classify microarray gene expression data;.” 2015. Web. 29 Mar 2020.

Vancouver:

K D. Data mining techniques to classify microarray gene expression data;. [Internet] [Thesis]. Anna University; 2015. [cited 2020 Mar 29]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38566.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

K D. Data mining techniques to classify microarray gene expression data;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/38566

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. Almoeirfi, Makkeyah. Classification approaches for microarray gene expression data analysis .

Degree: 2015, Laurentian University

 The technology of Microarray is among the vital technological advancements in bioinformatics. Usually, microarray data is characterized by noisiness as well as increased dimensionality. Therefore,… (more)

Subjects/Keywords: Microarray; data; gene

…confidence levels in making classifications on the unknown gene sample based on microarray data by… …Analysis of Microarray data Microarrays have made ready for analysts to assemble a great deal of… …on microarray data using SVM comparing with two other classifiers. • Analyze two… …and the parameters best suited for classification of a given microarray data. • Use… …on the classification of microarray data utilizing the support vector machine (SVM… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Almoeirfi, M. (2015). Classification approaches for microarray gene expression data analysis . (Thesis). Laurentian University. Retrieved from https://zone.biblio.laurentian.ca/dspace/handle/10219/2535

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Almoeirfi, Makkeyah. “Classification approaches for microarray gene expression data analysis .” 2015. Thesis, Laurentian University. Accessed March 29, 2020. https://zone.biblio.laurentian.ca/dspace/handle/10219/2535.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Almoeirfi, Makkeyah. “Classification approaches for microarray gene expression data analysis .” 2015. Web. 29 Mar 2020.

Vancouver:

Almoeirfi M. Classification approaches for microarray gene expression data analysis . [Internet] [Thesis]. Laurentian University; 2015. [cited 2020 Mar 29]. Available from: https://zone.biblio.laurentian.ca/dspace/handle/10219/2535.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Almoeirfi M. Classification approaches for microarray gene expression data analysis . [Thesis]. Laurentian University; 2015. Available from: https://zone.biblio.laurentian.ca/dspace/handle/10219/2535

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Wright State University

13. Jiang, Chunyu. DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA.

Degree: PhD, Computer Science and Engineering PhD, 2007, Wright State University

 Huge amount of data is available in our society and the need for turning such data into useful information and knowledge is urgent. Data mining… (more)

Subjects/Keywords: Computer Science; genes; MTSO; RANKING; microarray; classification; MTSO Data; TIME SERIES

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APA (6th Edition):

Jiang, C. (2007). DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264

Chicago Manual of Style (16th Edition):

Jiang, Chunyu. “DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA.” 2007. Doctoral Dissertation, Wright State University. Accessed March 29, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264.

MLA Handbook (7th Edition):

Jiang, Chunyu. “DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA.” 2007. Web. 29 Mar 2020.

Vancouver:

Jiang C. DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA. [Internet] [Doctoral dissertation]. Wright State University; 2007. [cited 2020 Mar 29]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264.

Council of Science Editors:

Jiang C. DATA MINING AND ANALYSIS ON MULTIPLE TIME SERIES OBJECT DATA. [Doctoral Dissertation]. Wright State University; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1177959264


University of Akron

14. Selvaraja, Sudarshan. Microarray Data Analysis Tool (MAT).

Degree: MS, Computer Science, 2008, University of Akron

Microarray is a technology that has been widely used by the biologists to probe the presence of genes in a sample of DNA or RNA.… (more)

Subjects/Keywords: Bioinformatics; Computer Science; MAT; Microarray; Data Mining; microRNA; kNN; Weighted kNN

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Selvaraja, S. (2008). Microarray Data Analysis Tool (MAT). (Masters Thesis). University of Akron. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806

Chicago Manual of Style (16th Edition):

Selvaraja, Sudarshan. “Microarray Data Analysis Tool (MAT).” 2008. Masters Thesis, University of Akron. Accessed March 29, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806.

MLA Handbook (7th Edition):

Selvaraja, Sudarshan. “Microarray Data Analysis Tool (MAT).” 2008. Web. 29 Mar 2020.

Vancouver:

Selvaraja S. Microarray Data Analysis Tool (MAT). [Internet] [Masters thesis]. University of Akron; 2008. [cited 2020 Mar 29]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806.

Council of Science Editors:

Selvaraja S. Microarray Data Analysis Tool (MAT). [Masters Thesis]. University of Akron; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1227467806


University of Akron

15. Jose, Adarsh. Gene Selection by 1-D Discrete Wavelet Transform for Classifying Cancer Samples Using DNA Microarray Date.

Degree: MSin Engineering, Biomedical Engineering, 2009, University of Akron

  Selecting a set of highly discriminant genes for biological samples is an important task for designing highly efficient classifiers using DNA microarray data. The… (more)

Subjects/Keywords: Biomedical Research; discrete wavelet transform; microarray data; cancer; gene selection; classification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jose, A. (2009). Gene Selection by 1-D Discrete Wavelet Transform for Classifying Cancer Samples Using DNA Microarray Date. (Masters Thesis). University of Akron. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=akron1240851642

Chicago Manual of Style (16th Edition):

Jose, Adarsh. “Gene Selection by 1-D Discrete Wavelet Transform for Classifying Cancer Samples Using DNA Microarray Date.” 2009. Masters Thesis, University of Akron. Accessed March 29, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1240851642.

MLA Handbook (7th Edition):

Jose, Adarsh. “Gene Selection by 1-D Discrete Wavelet Transform for Classifying Cancer Samples Using DNA Microarray Date.” 2009. Web. 29 Mar 2020.

Vancouver:

Jose A. Gene Selection by 1-D Discrete Wavelet Transform for Classifying Cancer Samples Using DNA Microarray Date. [Internet] [Masters thesis]. University of Akron; 2009. [cited 2020 Mar 29]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1240851642.

Council of Science Editors:

Jose A. Gene Selection by 1-D Discrete Wavelet Transform for Classifying Cancer Samples Using DNA Microarray Date. [Masters Thesis]. University of Akron; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1240851642


University of Pretoria

16. [No author]. Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data .

Degree: 2009, University of Pretoria

Microarray technology makes it possible to identify changes in gene expression of an organism, under various conditions. The challenge to researchers that employ microarray expression… (more)

Subjects/Keywords: Technology; Microarray data; Genes; Modules in madiba; Web-based toolkit; UCTD

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

author], [. (2009). Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data . (Masters Thesis). University of Pretoria. Retrieved from http://upetd.up.ac.za/thesis/available/etd-08122009-210201/

Chicago Manual of Style (16th Edition):

author], [No. “Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data .” 2009. Masters Thesis, University of Pretoria. Accessed March 29, 2020. http://upetd.up.ac.za/thesis/available/etd-08122009-210201/.

MLA Handbook (7th Edition):

author], [No. “Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data .” 2009. Web. 29 Mar 2020.

Vancouver:

author] [. Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data . [Internet] [Masters thesis]. University of Pretoria; 2009. [cited 2020 Mar 29]. Available from: http://upetd.up.ac.za/thesis/available/etd-08122009-210201/.

Council of Science Editors:

author] [. Development and application of analysis modules in MADIBA, a Web-based toolkit for the interpretation of microarray data . [Masters Thesis]. University of Pretoria; 2009. Available from: http://upetd.up.ac.za/thesis/available/etd-08122009-210201/


University of North Texas

17. Zhang, Guilin. Clustering Algorithms for Time Series Gene Expression in Microarray Data.

Degree: 2012, University of North Texas

 Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements… (more)

Subjects/Keywords: Microarray data; time series; algorithm; clustering analysis; distance matrix; time points

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APA (6th Edition):

Zhang, G. (2012). Clustering Algorithms for Time Series Gene Expression in Microarray Data. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc177269/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Zhang, Guilin. “Clustering Algorithms for Time Series Gene Expression in Microarray Data.” 2012. Thesis, University of North Texas. Accessed March 29, 2020. https://digital.library.unt.edu/ark:/67531/metadc177269/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Guilin. “Clustering Algorithms for Time Series Gene Expression in Microarray Data.” 2012. Web. 29 Mar 2020.

Vancouver:

Zhang G. Clustering Algorithms for Time Series Gene Expression in Microarray Data. [Internet] [Thesis]. University of North Texas; 2012. [cited 2020 Mar 29]. Available from: https://digital.library.unt.edu/ark:/67531/metadc177269/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang G. Clustering Algorithms for Time Series Gene Expression in Microarray Data. [Thesis]. University of North Texas; 2012. Available from: https://digital.library.unt.edu/ark:/67531/metadc177269/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

18. Paul, Jasmin. A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction .

Degree: 2012, University of Guelph

 The analysis of microarray data is a challenging task because of the large dimensionality and small sample size involved. Although a few methods are available… (more)

Subjects/Keywords: Reclassiifcation; microarray data analysis; small sample size; allergy prediction

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APA (6th Edition):

Paul, J. (2012). A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3486

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Paul, Jasmin. “A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction .” 2012. Thesis, University of Guelph. Accessed March 29, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3486.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Paul, Jasmin. “A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction .” 2012. Web. 29 Mar 2020.

Vancouver:

Paul J. A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction . [Internet] [Thesis]. University of Guelph; 2012. [cited 2020 Mar 29]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3486.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Paul J. A New Reclassification Method for Highly Uncertain Microarray Data in Allergy Gene Prediction . [Thesis]. University of Guelph; 2012. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3486

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


New Jersey Institute of Technology

19. Radia, Nidhi. Unsupervised gene regulatory network inference on microarray data.

Degree: MSin Bioinformatics - (M.S.), Computer Science, 2015, New Jersey Institute of Technology

  Obtaining gene regulatory networks (GRNs) from expression data is a challenging and crucial task. Many computational methods and algorithms have been developed to infer… (more)

Subjects/Keywords: Gene regulatory networks; Microarray data; Bioinformatics; Computer Sciences

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APA (6th Edition):

Radia, N. (2015). Unsupervised gene regulatory network inference on microarray data. (Thesis). New Jersey Institute of Technology. Retrieved from https://digitalcommons.njit.edu/theses/242

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Radia, Nidhi. “Unsupervised gene regulatory network inference on microarray data.” 2015. Thesis, New Jersey Institute of Technology. Accessed March 29, 2020. https://digitalcommons.njit.edu/theses/242.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Radia, Nidhi. “Unsupervised gene regulatory network inference on microarray data.” 2015. Web. 29 Mar 2020.

Vancouver:

Radia N. Unsupervised gene regulatory network inference on microarray data. [Internet] [Thesis]. New Jersey Institute of Technology; 2015. [cited 2020 Mar 29]. Available from: https://digitalcommons.njit.edu/theses/242.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Radia N. Unsupervised gene regulatory network inference on microarray data. [Thesis]. New Jersey Institute of Technology; 2015. Available from: https://digitalcommons.njit.edu/theses/242

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Virginia Commonwealth University

20. Chaitankar, Vijender. INFORMATION THEORETIC APPROACHES TOWARDS REGULATORY NETWORK INFERENCE.

Degree: PhD, Engineering, 2012, Virginia Commonwealth University

 In spite of many efforts in the past, inference or reverse engineering of regulatory networks from microarray data remains an unsolved problem in the area… (more)

Subjects/Keywords: Regulatory Networks; Time lags; Inference; Information Theory; Microarray Data; Engineering

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APA (6th Edition):

Chaitankar, V. (2012). INFORMATION THEORETIC APPROACHES TOWARDS REGULATORY NETWORK INFERENCE. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/AZZZ-EN83 ; https://scholarscompass.vcu.edu/etd/2948

Chicago Manual of Style (16th Edition):

Chaitankar, Vijender. “INFORMATION THEORETIC APPROACHES TOWARDS REGULATORY NETWORK INFERENCE.” 2012. Doctoral Dissertation, Virginia Commonwealth University. Accessed March 29, 2020. https://doi.org/10.25772/AZZZ-EN83 ; https://scholarscompass.vcu.edu/etd/2948.

MLA Handbook (7th Edition):

Chaitankar, Vijender. “INFORMATION THEORETIC APPROACHES TOWARDS REGULATORY NETWORK INFERENCE.” 2012. Web. 29 Mar 2020.

Vancouver:

Chaitankar V. INFORMATION THEORETIC APPROACHES TOWARDS REGULATORY NETWORK INFERENCE. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2012. [cited 2020 Mar 29]. Available from: https://doi.org/10.25772/AZZZ-EN83 ; https://scholarscompass.vcu.edu/etd/2948.

Council of Science Editors:

Chaitankar V. INFORMATION THEORETIC APPROACHES TOWARDS REGULATORY NETWORK INFERENCE. [Doctoral Dissertation]. Virginia Commonwealth University; 2012. Available from: https://doi.org/10.25772/AZZZ-EN83 ; https://scholarscompass.vcu.edu/etd/2948


University of KwaZulu-Natal

21. Mohammed, Mohanad Mohammed Adam. A comparison of cancer classification methods based on microarray data.

Degree: 2018, University of KwaZulu-Natal

 Cancer is among the leading causes of death in both developed and developing countries. Through gene expression profiling of tumors, the accuracy of cancer classification… (more)

Subjects/Keywords: Cancer.; Gene expression.; Naive Bayes.; Tumours.; Cancer classification.; Microarray data.

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APA (6th Edition):

Mohammed, M. M. A. (2018). A comparison of cancer classification methods based on microarray data. (Thesis). University of KwaZulu-Natal. Retrieved from https://researchspace.ukzn.ac.za/handle/10413/16920

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Mohammed, Mohanad Mohammed Adam. “A comparison of cancer classification methods based on microarray data.” 2018. Thesis, University of KwaZulu-Natal. Accessed March 29, 2020. https://researchspace.ukzn.ac.za/handle/10413/16920.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Mohammed, Mohanad Mohammed Adam. “A comparison of cancer classification methods based on microarray data.” 2018. Web. 29 Mar 2020.

Vancouver:

Mohammed MMA. A comparison of cancer classification methods based on microarray data. [Internet] [Thesis]. University of KwaZulu-Natal; 2018. [cited 2020 Mar 29]. Available from: https://researchspace.ukzn.ac.za/handle/10413/16920.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mohammed MMA. A comparison of cancer classification methods based on microarray data. [Thesis]. University of KwaZulu-Natal; 2018. Available from: https://researchspace.ukzn.ac.za/handle/10413/16920

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Michigan

22. Hunt, Gregory. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.

Degree: PhD, Statistics, 2018, University of Michigan

 Transformations are an important aspect of data analysis. In this work we explore the impact of data transformation on the analysis of high-throughput -omics data.… (more)

Subjects/Keywords: Cell Type Deconvolution and Transformation of Microenvironment Microarray Data; Statistics and Numeric Data; Science

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APA (6th Edition):

Hunt, G. (2018). Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/147575

Chicago Manual of Style (16th Edition):

Hunt, Gregory. “Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.” 2018. Doctoral Dissertation, University of Michigan. Accessed March 29, 2020. http://hdl.handle.net/2027.42/147575.

MLA Handbook (7th Edition):

Hunt, Gregory. “Cell Type Deconvolution and Transformation of Microenvironment Microarray Data.” 2018. Web. 29 Mar 2020.

Vancouver:

Hunt G. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. [Internet] [Doctoral dissertation]. University of Michigan; 2018. [cited 2020 Mar 29]. Available from: http://hdl.handle.net/2027.42/147575.

Council of Science Editors:

Hunt G. Cell Type Deconvolution and Transformation of Microenvironment Microarray Data. [Doctoral Dissertation]. University of Michigan; 2018. Available from: http://hdl.handle.net/2027.42/147575


IUPUI

23. Choudhury, Bhavna. Sibios as a Framework for Biomarker Discovery Using Microarray Data.

Degree: 2006, IUPUI

Submitted to the Faculty of the School of Informatics in parial fulfillment of the requirements for the degree of Master of Schience in Bioinformatics Indiana… (more)

Subjects/Keywords: SIBIOS; Microarray Data; genomic data; bioinformatics

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APA (6th Edition):

Choudhury, B. (2006). Sibios as a Framework for Biomarker Discovery Using Microarray Data. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/623

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Choudhury, Bhavna. “Sibios as a Framework for Biomarker Discovery Using Microarray Data.” 2006. Thesis, IUPUI. Accessed March 29, 2020. http://hdl.handle.net/1805/623.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Choudhury, Bhavna. “Sibios as a Framework for Biomarker Discovery Using Microarray Data.” 2006. Web. 29 Mar 2020.

Vancouver:

Choudhury B. Sibios as a Framework for Biomarker Discovery Using Microarray Data. [Internet] [Thesis]. IUPUI; 2006. [cited 2020 Mar 29]. Available from: http://hdl.handle.net/1805/623.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Choudhury B. Sibios as a Framework for Biomarker Discovery Using Microarray Data. [Thesis]. IUPUI; 2006. Available from: http://hdl.handle.net/1805/623

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Esteves, Gustavo Henrique. Métodos estatísticos para a análise de dados de cDNA microarray em um ambiente computacional integrado.

Degree: PhD, Bioinformática, 2007, University of São Paulo

Análise de expressão gênica em larga escala é de fundamental importância para a biologia molecular atual pois possibilita a medida dos níveis de expressão de… (more)

Subjects/Keywords: Análise de Dados; Classificação de Grupos Gênicos; Classification of Gene Networks; Data Analysis; Microarray; Microarray; Redes de Relevância; Relevance Networks

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APA (6th Edition):

Esteves, G. H. (2007). Métodos estatísticos para a análise de dados de cDNA microarray em um ambiente computacional integrado. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/95/95131/tde-03062007-210232/ ;

Chicago Manual of Style (16th Edition):

Esteves, Gustavo Henrique. “Métodos estatísticos para a análise de dados de cDNA microarray em um ambiente computacional integrado.” 2007. Doctoral Dissertation, University of São Paulo. Accessed March 29, 2020. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-03062007-210232/ ;.

MLA Handbook (7th Edition):

Esteves, Gustavo Henrique. “Métodos estatísticos para a análise de dados de cDNA microarray em um ambiente computacional integrado.” 2007. Web. 29 Mar 2020.

Vancouver:

Esteves GH. Métodos estatísticos para a análise de dados de cDNA microarray em um ambiente computacional integrado. [Internet] [Doctoral dissertation]. University of São Paulo; 2007. [cited 2020 Mar 29]. Available from: http://www.teses.usp.br/teses/disponiveis/95/95131/tde-03062007-210232/ ;.

Council of Science Editors:

Esteves GH. Métodos estatísticos para a análise de dados de cDNA microarray em um ambiente computacional integrado. [Doctoral Dissertation]. University of São Paulo; 2007. Available from: http://www.teses.usp.br/teses/disponiveis/95/95131/tde-03062007-210232/ ;


University of Cincinnati

25. SARTOR, MAUREEN A. TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS.

Degree: PhD, Medicine : Biostatistics (Environmental Health), 2007, University of Cincinnati

 DNA microarrays are a revolutionary technology able to measure the expression levels of thousands of genes simultaneously, providing a snapshot in time of a tissue… (more)

Subjects/Keywords: microarray; empirical Bayes; hierarchical Bayesian model; splines; gene set enrichment analysis; microarray data analysis; posterior predictive p-values

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APA (6th Edition):

SARTOR, M. A. (2007). TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673

Chicago Manual of Style (16th Edition):

SARTOR, MAUREEN A. “TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS.” 2007. Doctoral Dissertation, University of Cincinnati. Accessed March 29, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.

MLA Handbook (7th Edition):

SARTOR, MAUREEN A. “TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS.” 2007. Web. 29 Mar 2020.

Vancouver:

SARTOR MA. TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS. [Internet] [Doctoral dissertation]. University of Cincinnati; 2007. [cited 2020 Mar 29]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.

Council of Science Editors:

SARTOR MA. TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS. [Doctoral Dissertation]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673

26. Fujita, André. Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias.

Degree: PhD, Bioinformática, 2007, University of São Paulo

A análise da expressão gênica através de dados gerados em experimentos de microarrays de DNA vem possibilitando uma melhor compreensão da dinâmica e dos mecanismos… (more)

Subjects/Keywords: DNA microarray data normalization; DVAR; DVAR; GEDI; microarray; microarray; normalização de microarrays de DNA; redes regulatórias; regulatory networks; SVAR; SVAR; VAR; VAR

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APA (6th Edition):

Fujita, A. (2007). Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/95/95131/tde-14092007-173758/ ;

Chicago Manual of Style (16th Edition):

Fujita, André. “Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias.” 2007. Doctoral Dissertation, University of São Paulo. Accessed March 29, 2020. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-14092007-173758/ ;.

MLA Handbook (7th Edition):

Fujita, André. “Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias.” 2007. Web. 29 Mar 2020.

Vancouver:

Fujita A. Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias. [Internet] [Doctoral dissertation]. University of São Paulo; 2007. [cited 2020 Mar 29]. Available from: http://www.teses.usp.br/teses/disponiveis/95/95131/tde-14092007-173758/ ;.

Council of Science Editors:

Fujita A. Análise de dados de expressão gênica: normalização de microarrays e modelagem de redes regulatórias. [Doctoral Dissertation]. University of São Paulo; 2007. Available from: http://www.teses.usp.br/teses/disponiveis/95/95131/tde-14092007-173758/ ;

27. Espezua Llerena, Soledad. Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit.

Degree: PhD, Sistemas Dinâmicos, 2013, University of São Paulo

Reduzir a dimensão de bancos de dados é um passo importante em processos de reconhecimento de padrões e aprendizagem de máquina. Projection Pursuit (PP) tem… (more)

Subjects/Keywords: Classificação; Classification; Dados de microarranjo; Dimentionality reduction; Microarray data; Projection Pursuit; Projection Pursuit; Redução dimensional

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APA (6th Edition):

Espezua Llerena, S. (2013). Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit. (Doctoral Dissertation). University of São Paulo. Retrieved from http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/ ;

Chicago Manual of Style (16th Edition):

Espezua Llerena, Soledad. “Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit.” 2013. Doctoral Dissertation, University of São Paulo. Accessed March 29, 2020. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/ ;.

MLA Handbook (7th Edition):

Espezua Llerena, Soledad. “Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit.” 2013. Web. 29 Mar 2020.

Vancouver:

Espezua Llerena S. Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit. [Internet] [Doctoral dissertation]. University of São Paulo; 2013. [cited 2020 Mar 29]. Available from: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/ ;.

Council of Science Editors:

Espezua Llerena S. Redução dimensional de dados de alta dimensão e poucas amostras usando Projection Pursuit. [Doctoral Dissertation]. University of São Paulo; 2013. Available from: http://www.teses.usp.br/teses/disponiveis/18/18153/tde-10102013-150240/ ;


University of Akron

28. Shaik Abdul, Ameer Basha. SVM Classification and Analysis of Margin Distance on Microarray Data.

Degree: MS, Computer Science, 2011, University of Akron

 Support vector machine is statistical classification algorithm that classifies data by separating two classes with the help of a functional hyper plane. SVM is known… (more)

Subjects/Keywords: Bioinformatics; Computer Science; SVM; Data mining; classification; microarray; support vectors; margin distance

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Shaik Abdul, A. B. (2011). SVM Classification and Analysis of Margin Distance on Microarray Data. (Masters Thesis). University of Akron. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924

Chicago Manual of Style (16th Edition):

Shaik Abdul, Ameer Basha. “SVM Classification and Analysis of Margin Distance on Microarray Data.” 2011. Masters Thesis, University of Akron. Accessed March 29, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924.

MLA Handbook (7th Edition):

Shaik Abdul, Ameer Basha. “SVM Classification and Analysis of Margin Distance on Microarray Data.” 2011. Web. 29 Mar 2020.

Vancouver:

Shaik Abdul AB. SVM Classification and Analysis of Margin Distance on Microarray Data. [Internet] [Masters thesis]. University of Akron; 2011. [cited 2020 Mar 29]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924.

Council of Science Editors:

Shaik Abdul AB. SVM Classification and Analysis of Margin Distance on Microarray Data. [Masters Thesis]. University of Akron; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=akron1302618924


NSYSU

29. Lin, Chung-Hsun. Using Microarray Time Series Data and Gene Ontology for Gene Clustering and Network Reconstruction.

Degree: Master, Information Management, 2013, NSYSU

 In recent years, using microarray time series data to reconstruct gene regulatory network, has become a very popular way. However, the number of these genes… (more)

Subjects/Keywords: gene cluster; boolnet; gene regulatory network; gene ontology; microarray time series data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lin, C. (2013). Using Microarray Time Series Data and Gene Ontology for Gene Clustering and Network Reconstruction. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0706113-113208

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Lin, Chung-Hsun. “Using Microarray Time Series Data and Gene Ontology for Gene Clustering and Network Reconstruction.” 2013. Thesis, NSYSU. Accessed March 29, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0706113-113208.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Lin, Chung-Hsun. “Using Microarray Time Series Data and Gene Ontology for Gene Clustering and Network Reconstruction.” 2013. Web. 29 Mar 2020.

Vancouver:

Lin C. Using Microarray Time Series Data and Gene Ontology for Gene Clustering and Network Reconstruction. [Internet] [Thesis]. NSYSU; 2013. [cited 2020 Mar 29]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0706113-113208.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lin C. Using Microarray Time Series Data and Gene Ontology for Gene Clustering and Network Reconstruction. [Thesis]. NSYSU; 2013. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0706113-113208

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universidade de Lisboa

30. Rebouças, Sílvia Maria Dias Pedro, 1978-. Metodologias de classificação supervisionada para análise de dados de microarrays.

Degree: 2011, Universidade de Lisboa

Tese de doutoramento, Estatística e Investigação Operacional (Probabilidades e Estatística), Universidade de Lisboa, Faculdade de Ciências, 2011

Uma das principais características dos dados de microarrays… (more)

Subjects/Keywords: Microarray; Classificação supervisionada; Análise de componentes principais; Data piling; Teses de doutoramento - 2011

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rebouças, Sílvia Maria Dias Pedro, 1. (2011). Metodologias de classificação supervisionada para análise de dados de microarrays. (Thesis). Universidade de Lisboa. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/3749

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Rebouças, Sílvia Maria Dias Pedro, 1978-. “Metodologias de classificação supervisionada para análise de dados de microarrays.” 2011. Thesis, Universidade de Lisboa. Accessed March 29, 2020. http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/3749.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rebouças, Sílvia Maria Dias Pedro, 1978-. “Metodologias de classificação supervisionada para análise de dados de microarrays.” 2011. Web. 29 Mar 2020.

Vancouver:

Rebouças, Sílvia Maria Dias Pedro 1. Metodologias de classificação supervisionada para análise de dados de microarrays. [Internet] [Thesis]. Universidade de Lisboa; 2011. [cited 2020 Mar 29]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/3749.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rebouças, Sílvia Maria Dias Pedro 1. Metodologias de classificação supervisionada para análise de dados de microarrays. [Thesis]. Universidade de Lisboa; 2011. Available from: http://www.rcaap.pt/detail.jsp?id=oai:repositorio.ul.pt:10451/3749

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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