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You searched for +publisher:"University of North Carolina" +contributor:("McMillan, Leonard"). Showing records 1 – 15 of 15 total matches.

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University of North Carolina

1. Wang, Jeremy. Analysis and Visualization of Local Phylogenetic Structure within Species.

Degree: Computer Science, 2013, University of North Carolina

 While it is interesting to examine the evolutionary history and phylogenetic relationship between species, for example, in a sort of tree of life, there is… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Wang, J. (2013). Analysis and Visualization of Local Phylogenetic Structure within Species. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:68ff31e0-1710-4ef6-aba2-ba639ed0fa3a

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):

Wang, Jeremy. “Analysis and Visualization of Local Phylogenetic Structure within Species.” 2013. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:68ff31e0-1710-4ef6-aba2-ba639ed0fa3a.

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

MLA Handbook (7th Edition):

Wang, Jeremy. “Analysis and Visualization of Local Phylogenetic Structure within Species.” 2013. Web. 18 Jan 2021.

Vancouver:

Wang J. Analysis and Visualization of Local Phylogenetic Structure within Species. [Internet] [Thesis]. University of North Carolina; 2013. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:68ff31e0-1710-4ef6-aba2-ba639ed0fa3a.

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

Council of Science Editors:

Wang J. Analysis and Visualization of Local Phylogenetic Structure within Species. [Thesis]. University of North Carolina; 2013. Available from: https://cdr.lib.unc.edu/record/uuid:68ff31e0-1710-4ef6-aba2-ba639ed0fa3a

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


University of North Carolina

2. Huang, Shunping. Correcting Reference Bias in High-throughput Sequencing Analysis.

Degree: Computer Science, 2015, University of North Carolina

 Mapping reads to a reference sequence is a common step when analyzing high throughput sequencing data. The choice of reference is critical because its effect… (more)

Subjects/Keywords: Computer science; Bioinformatics; College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Huang, S. (2015). Correcting Reference Bias in High-throughput Sequencing Analysis. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:44569641-9dd5-4305-845a-4b0b847f0b4a

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):

Huang, Shunping. “Correcting Reference Bias in High-throughput Sequencing Analysis.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:44569641-9dd5-4305-845a-4b0b847f0b4a.

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

MLA Handbook (7th Edition):

Huang, Shunping. “Correcting Reference Bias in High-throughput Sequencing Analysis.” 2015. Web. 18 Jan 2021.

Vancouver:

Huang S. Correcting Reference Bias in High-throughput Sequencing Analysis. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:44569641-9dd5-4305-845a-4b0b847f0b4a.

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

Council of Science Editors:

Huang S. Correcting Reference Bias in High-throughput Sequencing Analysis. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:44569641-9dd5-4305-845a-4b0b847f0b4a

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


University of North Carolina

3. Wang, Weibo. Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data.

Degree: Computer Science, 2015, University of North Carolina

 Copy-number variation (CNV) is a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect… (more)

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Wang, W. (2015). Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:85f9311f-12ae-4a19-97c2-6f4c879a2eb3

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):

Wang, Weibo. “Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:85f9311f-12ae-4a19-97c2-6f4c879a2eb3.

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

MLA Handbook (7th Edition):

Wang, Weibo. “Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data.” 2015. Web. 18 Jan 2021.

Vancouver:

Wang W. Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:85f9311f-12ae-4a19-97c2-6f4c879a2eb3.

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

Council of Science Editors:

Wang W. Detect Copy Number Variations from Read-depth of High-throughput Sequencing Data. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:85f9311f-12ae-4a19-97c2-6f4c879a2eb3

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


University of North Carolina

4. Cheng, Wei. Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data.

Degree: Computer Science, 2015, University of North Carolina

 As a promising tool for dissecting the genetic basis of common diseases, expression quantitative trait loci (eQTL) study has attracted increasing research interest. Traditional eQTL… (more)

Subjects/Keywords: Computer science; Bioinformatics; College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Cheng, W. (2015). Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:a970ec3e-47e9-4603-b2dc-f6f2a8300847

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):

Cheng, Wei. “Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:a970ec3e-47e9-4603-b2dc-f6f2a8300847.

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

MLA Handbook (7th Edition):

Cheng, Wei. “Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data.” 2015. Web. 18 Jan 2021.

Vancouver:

Cheng W. Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:a970ec3e-47e9-4603-b2dc-f6f2a8300847.

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

Council of Science Editors:

Cheng W. Toward Robust Group-Wise eQTL Mapping via Integrating Multi-Domain Heterogeneous Data. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:a970ec3e-47e9-4603-b2dc-f6f2a8300847

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


University of North Carolina

5. Han, Xufeng. LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION.

Degree: Computer Science, 2016, University of North Carolina

 This thesis studies machine learning problems involved in visual recognition on a variety of computer vision tasks. It attacks the challenge of scaling-up learning to… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Han, X. (2016). LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042

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):

Han, Xufeng. “LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042.

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

MLA Handbook (7th Edition):

Han, Xufeng. “LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION.” 2016. Web. 18 Jan 2021.

Vancouver:

Han X. LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042.

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

Council of Science Editors:

Han X. LEARNING WITH MORE DATA AND BETTER MODELS FOR VISUAL SIMILARITY AND DIFFERENTIATION. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:e712c6a4-e4ff-4c3b-8646-a6c1c872f042

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


University of North Carolina

6. Fu, Chen-Ping. Analysis of Admixed Animals using Indirect Haplotype Information from Existing Technologies.

Degree: Computer Science, 2015, University of North Carolina

 The use of genotyping and sequencing technologies in genetic studies typically involves inspecting variants defined within a single reference genome. While this definition of genetic… (more)

Subjects/Keywords: Computer science; Genetics; Bioinformatics; College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Fu, C. (2015). Analysis of Admixed Animals using Indirect Haplotype Information from Existing Technologies. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:087b876d-f949-4963-929f-84444e9bf85f

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):

Fu, Chen-Ping. “Analysis of Admixed Animals using Indirect Haplotype Information from Existing Technologies.” 2015. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:087b876d-f949-4963-929f-84444e9bf85f.

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

MLA Handbook (7th Edition):

Fu, Chen-Ping. “Analysis of Admixed Animals using Indirect Haplotype Information from Existing Technologies.” 2015. Web. 18 Jan 2021.

Vancouver:

Fu C. Analysis of Admixed Animals using Indirect Haplotype Information from Existing Technologies. [Internet] [Thesis]. University of North Carolina; 2015. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:087b876d-f949-4963-929f-84444e9bf85f.

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

Council of Science Editors:

Fu C. Analysis of Admixed Animals using Indirect Haplotype Information from Existing Technologies. [Thesis]. University of North Carolina; 2015. Available from: https://cdr.lib.unc.edu/record/uuid:087b876d-f949-4963-929f-84444e9bf85f

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


University of North Carolina

7. Morgan, Andrew. Structural variation and the evolution of the mouse genome.

Degree: 2017, University of North Carolina

 Genetic variation in populations is governed by four basic forces: mutation, recombination, natural selection and genetic drift. Mutation is the source of new alleles, which… (more)

Subjects/Keywords: School of Medicine; Curriculum in Bioinformatics and Computational Biology

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APA (6th Edition):

Morgan, A. (2017). Structural variation and the evolution of the mouse genome. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:951a2c33-68fe-4e64-bf53-668fd461f6b2

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):

Morgan, Andrew. “Structural variation and the evolution of the mouse genome.” 2017. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:951a2c33-68fe-4e64-bf53-668fd461f6b2.

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

MLA Handbook (7th Edition):

Morgan, Andrew. “Structural variation and the evolution of the mouse genome.” 2017. Web. 18 Jan 2021.

Vancouver:

Morgan A. Structural variation and the evolution of the mouse genome. [Internet] [Thesis]. University of North Carolina; 2017. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:951a2c33-68fe-4e64-bf53-668fd461f6b2.

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

Council of Science Editors:

Morgan A. Structural variation and the evolution of the mouse genome. [Thesis]. University of North Carolina; 2017. Available from: https://cdr.lib.unc.edu/record/uuid:951a2c33-68fe-4e64-bf53-668fd461f6b2

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


University of North Carolina

8. Welch, Joshua. Computational Methods for Inferring Transcriptome Dynamics.

Degree: Computer Science, 2017, University of North Carolina

 The sequencing of the human genome paved the way for a new type of medicine, in which a molecular-level, cell-by-cell understanding of the genomic control… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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APA (6th Edition):

Welch, J. (2017). Computational Methods for Inferring Transcriptome Dynamics. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4

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):

Welch, Joshua. “Computational Methods for Inferring Transcriptome Dynamics.” 2017. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4.

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

MLA Handbook (7th Edition):

Welch, Joshua. “Computational Methods for Inferring Transcriptome Dynamics.” 2017. Web. 18 Jan 2021.

Vancouver:

Welch J. Computational Methods for Inferring Transcriptome Dynamics. [Internet] [Thesis]. University of North Carolina; 2017. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4.

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

Council of Science Editors:

Welch J. Computational Methods for Inferring Transcriptome Dynamics. [Thesis]. University of North Carolina; 2017. Available from: https://cdr.lib.unc.edu/record/uuid:0ffc9c2d-6027-40a5-b9bb-8f04c81058f4

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


University of North Carolina

9. Holt, James. USING THE MULTI-STRING BURROW-WHEELER TRANSFORM FOR HIGH-THROUGHPUT SEQUENCE ANALYSIS.

Degree: Computer Science, 2016, University of North Carolina

 The throughput of sequencing technologies has created a bottleneck where raw sequence files are stored in an un-indexed format on disk. Alignment to a reference… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Holt, J. (2016). USING THE MULTI-STRING BURROW-WHEELER TRANSFORM FOR HIGH-THROUGHPUT SEQUENCE ANALYSIS. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:2dd1d367-ba4b-4cdb-a4ff-30063b519b43

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):

Holt, James. “USING THE MULTI-STRING BURROW-WHEELER TRANSFORM FOR HIGH-THROUGHPUT SEQUENCE ANALYSIS.” 2016. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:2dd1d367-ba4b-4cdb-a4ff-30063b519b43.

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

MLA Handbook (7th Edition):

Holt, James. “USING THE MULTI-STRING BURROW-WHEELER TRANSFORM FOR HIGH-THROUGHPUT SEQUENCE ANALYSIS.” 2016. Web. 18 Jan 2021.

Vancouver:

Holt J. USING THE MULTI-STRING BURROW-WHEELER TRANSFORM FOR HIGH-THROUGHPUT SEQUENCE ANALYSIS. [Internet] [Thesis]. University of North Carolina; 2016. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:2dd1d367-ba4b-4cdb-a4ff-30063b519b43.

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

Council of Science Editors:

Holt J. USING THE MULTI-STRING BURROW-WHEELER TRANSFORM FOR HIGH-THROUGHPUT SEQUENCE ANALYSIS. [Thesis]. University of North Carolina; 2016. Available from: https://cdr.lib.unc.edu/record/uuid:2dd1d367-ba4b-4cdb-a4ff-30063b519b43

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


University of North Carolina

10. Zhang, Zhaojun. Efficient Computational Genetics Methods for Multiparent Crosses.

Degree: Computer Science, 2014, University of North Carolina

 Multiparent crosses are genetic populations bred in a controlled manner from a finite number of known founders. They represent experimental resources that are of potentially… (more)

Subjects/Keywords: Computer science; College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Zhang, Z. (2014). Efficient Computational Genetics Methods for Multiparent Crosses. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:81496805-1c98-4416-a013-efb7fba8ee15

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, Zhaojun. “Efficient Computational Genetics Methods for Multiparent Crosses.” 2014. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:81496805-1c98-4416-a013-efb7fba8ee15.

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

MLA Handbook (7th Edition):

Zhang, Zhaojun. “Efficient Computational Genetics Methods for Multiparent Crosses.” 2014. Web. 18 Jan 2021.

Vancouver:

Zhang Z. Efficient Computational Genetics Methods for Multiparent Crosses. [Internet] [Thesis]. University of North Carolina; 2014. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:81496805-1c98-4416-a013-efb7fba8ee15.

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

Council of Science Editors:

Zhang Z. Efficient Computational Genetics Methods for Multiparent Crosses. [Thesis]. University of North Carolina; 2014. Available from: https://cdr.lib.unc.edu/record/uuid:81496805-1c98-4416-a013-efb7fba8ee15

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


University of North Carolina

11. Welsh, Catherine. Computational Tools to Aid the Design and Development of a Genetic Reference Population.

Degree: Computer Science, 2014, University of North Carolina

 Model organisms are important tools used in biological and medical research. A key component of a genetics model organism is a known and reproducible genome.… (more)

Subjects/Keywords: Computer science; Bioinformatics; College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Welsh, C. (2014). Computational Tools to Aid the Design and Development of a Genetic Reference Population. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:34f1001c-bc13-492c-9782-479dc0d4e987

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):

Welsh, Catherine. “Computational Tools to Aid the Design and Development of a Genetic Reference Population.” 2014. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:34f1001c-bc13-492c-9782-479dc0d4e987.

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

MLA Handbook (7th Edition):

Welsh, Catherine. “Computational Tools to Aid the Design and Development of a Genetic Reference Population.” 2014. Web. 18 Jan 2021.

Vancouver:

Welsh C. Computational Tools to Aid the Design and Development of a Genetic Reference Population. [Internet] [Thesis]. University of North Carolina; 2014. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:34f1001c-bc13-492c-9782-479dc0d4e987.

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

Council of Science Editors:

Welsh C. Computational Tools to Aid the Design and Development of a Genetic Reference Population. [Thesis]. University of North Carolina; 2014. Available from: https://cdr.lib.unc.edu/record/uuid:34f1001c-bc13-492c-9782-479dc0d4e987

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


University of North Carolina

12. Kao, Chia-Yu. A Deep Learning Architecture For Histology Image Classification.

Degree: Computer Science, 2018, University of North Carolina

 Over the past decade, a machine learning technique called deep-learning has gained prominence in computer vision because of its ability to extract semantics from natural… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Kao, C. (2018). A Deep Learning Architecture For Histology Image Classification. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:363beb32-6a94-43ff-9540-baf14d36ec56

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):

Kao, Chia-Yu. “A Deep Learning Architecture For Histology Image Classification.” 2018. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:363beb32-6a94-43ff-9540-baf14d36ec56.

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

MLA Handbook (7th Edition):

Kao, Chia-Yu. “A Deep Learning Architecture For Histology Image Classification.” 2018. Web. 18 Jan 2021.

Vancouver:

Kao C. A Deep Learning Architecture For Histology Image Classification. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:363beb32-6a94-43ff-9540-baf14d36ec56.

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

Council of Science Editors:

Kao C. A Deep Learning Architecture For Histology Image Classification. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:363beb32-6a94-43ff-9540-baf14d36ec56

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


University of North Carolina

13. Liu, Guodong. A Data-driven, Piecewise Linear Approach to Modeling Human Motions.

Degree: Computer Science, 2007, University of North Carolina

 Motion capture, or mocap, is a prevalent technique for capturing and analyzing human articulations. Nowadays, mocap data are becoming one of the primary sources of… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Liu, G. (2007). A Data-driven, Piecewise Linear Approach to Modeling Human Motions. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:c31de009-e471-4ac1-9818-aac44a5fa0d8

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):

Liu, Guodong. “A Data-driven, Piecewise Linear Approach to Modeling Human Motions.” 2007. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:c31de009-e471-4ac1-9818-aac44a5fa0d8.

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

MLA Handbook (7th Edition):

Liu, Guodong. “A Data-driven, Piecewise Linear Approach to Modeling Human Motions.” 2007. Web. 18 Jan 2021.

Vancouver:

Liu G. A Data-driven, Piecewise Linear Approach to Modeling Human Motions. [Internet] [Thesis]. University of North Carolina; 2007. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:c31de009-e471-4ac1-9818-aac44a5fa0d8.

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

Council of Science Editors:

Liu G. A Data-driven, Piecewise Linear Approach to Modeling Human Motions. [Thesis]. University of North Carolina; 2007. Available from: https://cdr.lib.unc.edu/record/uuid:c31de009-e471-4ac1-9818-aac44a5fa0d8

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


University of North Carolina

14. Bennett, Eric P. Computational Video Enhancement.

Degree: Computer Science, 2007, University of North Carolina

 During a video, each scene element is often imaged many times by the sensor. I propose that by combining information from each captured frame throughout… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Bennett, E. P. (2007). Computational Video Enhancement. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:797a4fd8-23c8-4e28-adb6-98d347e2c0e7

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):

Bennett, Eric P. “Computational Video Enhancement.” 2007. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:797a4fd8-23c8-4e28-adb6-98d347e2c0e7.

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

MLA Handbook (7th Edition):

Bennett, Eric P. “Computational Video Enhancement.” 2007. Web. 18 Jan 2021.

Vancouver:

Bennett EP. Computational Video Enhancement. [Internet] [Thesis]. University of North Carolina; 2007. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:797a4fd8-23c8-4e28-adb6-98d347e2c0e7.

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

Council of Science Editors:

Bennett EP. Computational Video Enhancement. [Thesis]. University of North Carolina; 2007. Available from: https://cdr.lib.unc.edu/record/uuid:797a4fd8-23c8-4e28-adb6-98d347e2c0e7

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


University of North Carolina

15. Zhang, Jingdan. Object detection and segmentation using discriminative learning.

Degree: Computer Science, 2009, University of North Carolina

 Object detection and segmentation algorithms need to use prior knowledge of objects' shape and appearance to guide solutions to correct ones. A promising way of… (more)

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

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

APA (6th Edition):

Zhang, J. (2009). Object detection and segmentation using discriminative learning. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:2c917907-5689-46cb-83ad-49e742b2eb54

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, Jingdan. “Object detection and segmentation using discriminative learning.” 2009. Thesis, University of North Carolina. Accessed January 18, 2021. https://cdr.lib.unc.edu/record/uuid:2c917907-5689-46cb-83ad-49e742b2eb54.

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

MLA Handbook (7th Edition):

Zhang, Jingdan. “Object detection and segmentation using discriminative learning.” 2009. Web. 18 Jan 2021.

Vancouver:

Zhang J. Object detection and segmentation using discriminative learning. [Internet] [Thesis]. University of North Carolina; 2009. [cited 2021 Jan 18]. Available from: https://cdr.lib.unc.edu/record/uuid:2c917907-5689-46cb-83ad-49e742b2eb54.

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

Council of Science Editors:

Zhang J. Object detection and segmentation using discriminative learning. [Thesis]. University of North Carolina; 2009. Available from: https://cdr.lib.unc.edu/record/uuid:2c917907-5689-46cb-83ad-49e742b2eb54

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

.