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You searched for subject:(Literature based Discovery). Showing records 1 – 14 of 14 total matches.

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University of Cambridge

1. Baker, Simon. Semantic text classification for cancer text mining.

Degree: PhD, 2018, University of Cambridge

 Cancer researchers and oncologists benefit greatly from text mining major knowledge sources in biomedicine such as PubMed. Fundamentally, text mining depends on accurate text classification.… (more)

Subjects/Keywords: Cancer; Text Mining; Machine Learning; Classification; Literature-based Discovery; Hallmarks of Cancer; Deep Learning; Artificial Intelligence

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APA (6th Edition):

Baker, S. (2018). Semantic text classification for cancer text mining. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/275838 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744844

Chicago Manual of Style (16th Edition):

Baker, Simon. “Semantic text classification for cancer text mining.” 2018. Doctoral Dissertation, University of Cambridge. Accessed August 04, 2020. https://www.repository.cam.ac.uk/handle/1810/275838 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744844.

MLA Handbook (7th Edition):

Baker, Simon. “Semantic text classification for cancer text mining.” 2018. Web. 04 Aug 2020.

Vancouver:

Baker S. Semantic text classification for cancer text mining. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2020 Aug 04]. Available from: https://www.repository.cam.ac.uk/handle/1810/275838 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744844.

Council of Science Editors:

Baker S. Semantic text classification for cancer text mining. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/275838 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744844

2. Cameron, Delroy Huborn. A Context-Driven Subgraph Model for Literature-Based Discovery.

Degree: PhD, Computer Science and Engineering PhD, 2014, Wright State University

Literature-Based Discovery (LBD) refers to the process of uncovering hidden connections that are implicit in scientific literature. Numerous hypotheses have been generated from scientific literature(more)

Subjects/Keywords: Computer Science; Biomedical Research; Information Systems; Semantic Predications; Graph mining; Path clustering; Semantic relatedness; Literature-based discovery

…75 . 77 . 83 . 99 . 105 . 108 . 112 . 115 . 117 . 119 . 123 B.1 Literature-Based Discovery… …Literature-Based Discovery (LBD) continues to be recognized in the broader research… …Answering (QA), 3) Document Summarization, and 4) Literature-Based Discovery… …new knowledge. 2.1.4 Literature-Based Discovery Literature-Based Discovery (LBD)… …leverage assertional and definitional knowledge are discussed. 2.3 Literature-Based Discovery… 

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APA (6th Edition):

Cameron, D. H. (2014). A Context-Driven Subgraph Model for Literature-Based Discovery. (Doctoral Dissertation). Wright State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=wright1417034001

Chicago Manual of Style (16th Edition):

Cameron, Delroy Huborn. “A Context-Driven Subgraph Model for Literature-Based Discovery.” 2014. Doctoral Dissertation, Wright State University. Accessed August 04, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1417034001.

MLA Handbook (7th Edition):

Cameron, Delroy Huborn. “A Context-Driven Subgraph Model for Literature-Based Discovery.” 2014. Web. 04 Aug 2020.

Vancouver:

Cameron DH. A Context-Driven Subgraph Model for Literature-Based Discovery. [Internet] [Doctoral dissertation]. Wright State University; 2014. [cited 2020 Aug 04]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1417034001.

Council of Science Editors:

Cameron DH. A Context-Driven Subgraph Model for Literature-Based Discovery. [Doctoral Dissertation]. Wright State University; 2014. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=wright1417034001

3. Crichton, Gamal Kashaka Omari. Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery.

Degree: PhD, 2019, University of Cambridge

Literature-based Discovery (LBD) uses information from explicit statements in literature to generate new or unstated knowledge. Automated LBD can thus facilitate hypothesis testing and generation… (more)

Subjects/Keywords: Literature-based Discovery; LBD; Neural networks; Named Entity Recognition; NER; Multi-task Learning; LION LBD; knowledge discovery; Natural Language Processing; NLP; Machine Learning; Deep Learning; Biomedical NLP; Biomedical Knowledge Discovery; Link Predcition; Language Technology Laboratory

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APA (6th Edition):

Crichton, G. K. O. (2019). Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/293886

Chicago Manual of Style (16th Edition):

Crichton, Gamal Kashaka Omari. “Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery.” 2019. Doctoral Dissertation, University of Cambridge. Accessed August 04, 2020. https://www.repository.cam.ac.uk/handle/1810/293886.

MLA Handbook (7th Edition):

Crichton, Gamal Kashaka Omari. “Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery.” 2019. Web. 04 Aug 2020.

Vancouver:

Crichton GKO. Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2020 Aug 04]. Available from: https://www.repository.cam.ac.uk/handle/1810/293886.

Council of Science Editors:

Crichton GKO. Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/293886


University of Cambridge

4. Crichton, Gamal Kashaka Omari. Improving automated literature-based discovery with neural networks : neural biomedical named entity recognition, link prediction and discovery.

Degree: PhD, 2019, University of Cambridge

Literature-based Discovery (LBD) uses information from explicit statements in literature to generate new or unstated knowledge. Automated LBD can thus facilitate hypothesis testing and generation… (more)

Subjects/Keywords: Literature-based Discovery; LBD; Neural networks; Named Entity Recognition; NER; Multi-task Learning; LION LBD; knowledge discovery; Natural Language Processing; NLP; Machine Learning; Deep Learning; Biomedical NLP; Biomedical Knowledge Discovery; Link Predcition; Language Technology Laboratory

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APA (6th Edition):

Crichton, G. K. O. (2019). Improving automated literature-based discovery with neural networks : neural biomedical named entity recognition, link prediction and discovery. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/293886 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782841

Chicago Manual of Style (16th Edition):

Crichton, Gamal Kashaka Omari. “Improving automated literature-based discovery with neural networks : neural biomedical named entity recognition, link prediction and discovery.” 2019. Doctoral Dissertation, University of Cambridge. Accessed August 04, 2020. https://www.repository.cam.ac.uk/handle/1810/293886 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782841.

MLA Handbook (7th Edition):

Crichton, Gamal Kashaka Omari. “Improving automated literature-based discovery with neural networks : neural biomedical named entity recognition, link prediction and discovery.” 2019. Web. 04 Aug 2020.

Vancouver:

Crichton GKO. Improving automated literature-based discovery with neural networks : neural biomedical named entity recognition, link prediction and discovery. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2020 Aug 04]. Available from: https://www.repository.cam.ac.uk/handle/1810/293886 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782841.

Council of Science Editors:

Crichton GKO. Improving automated literature-based discovery with neural networks : neural biomedical named entity recognition, link prediction and discovery. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/293886 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782841


Virginia Tech

5. Das Neves, Fernando Adrian. Stepping Stones and Pathways:Improving Retrieval by Chains of Relationships between Documents.

Degree: PhD, Computer Science, 2004, Virginia Tech

 The information retrieval (IR) field has been successful in developing techniques to address many types of information needs. However, there are cases in which traditional… (more)

Subjects/Keywords: Information retrieval; Literature-based discovery; Combination of sources of evidence; Indexing of scientific literature

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APA (6th Edition):

Das Neves, F. A. (2004). Stepping Stones and Pathways:Improving Retrieval by Chains of Relationships between Documents. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/29419

Chicago Manual of Style (16th Edition):

Das Neves, Fernando Adrian. “Stepping Stones and Pathways:Improving Retrieval by Chains of Relationships between Documents.” 2004. Doctoral Dissertation, Virginia Tech. Accessed August 04, 2020. http://hdl.handle.net/10919/29419.

MLA Handbook (7th Edition):

Das Neves, Fernando Adrian. “Stepping Stones and Pathways:Improving Retrieval by Chains of Relationships between Documents.” 2004. Web. 04 Aug 2020.

Vancouver:

Das Neves FA. Stepping Stones and Pathways:Improving Retrieval by Chains of Relationships between Documents. [Internet] [Doctoral dissertation]. Virginia Tech; 2004. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/10919/29419.

Council of Science Editors:

Das Neves FA. Stepping Stones and Pathways:Improving Retrieval by Chains of Relationships between Documents. [Doctoral Dissertation]. Virginia Tech; 2004. Available from: http://hdl.handle.net/10919/29419


University of North Texas

6. Dharmavaram, Sirisha. Mining Biomedical Data for Hidden Relationship Discovery.

Degree: 2019, University of North Texas

 With an ever-growing number of publications in the biomedical domain, it becomes likely that important implicit connections between individual concepts of biomedical knowledge are overlooked.… (more)

Subjects/Keywords: Literature Based Discovery; Representation; Learning; Path Clustering; Semantic Analysis

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APA (6th Edition):

Dharmavaram, S. (2019). Mining Biomedical Data for Hidden Relationship Discovery. (Thesis). University of North Texas. Retrieved from https://digital.library.unt.edu/ark:/67531/metadc1538709/

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):

Dharmavaram, Sirisha. “Mining Biomedical Data for Hidden Relationship Discovery.” 2019. Thesis, University of North Texas. Accessed August 04, 2020. https://digital.library.unt.edu/ark:/67531/metadc1538709/.

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

MLA Handbook (7th Edition):

Dharmavaram, Sirisha. “Mining Biomedical Data for Hidden Relationship Discovery.” 2019. Web. 04 Aug 2020.

Vancouver:

Dharmavaram S. Mining Biomedical Data for Hidden Relationship Discovery. [Internet] [Thesis]. University of North Texas; 2019. [cited 2020 Aug 04]. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538709/.

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

Council of Science Editors:

Dharmavaram S. Mining Biomedical Data for Hidden Relationship Discovery. [Thesis]. University of North Texas; 2019. Available from: https://digital.library.unt.edu/ark:/67531/metadc1538709/

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


University of Arizona

7. Hahn-Powell, Gus. Machine Reading for Scientific Discovery .

Degree: 2018, University of Arizona

 The aim of this work is to accelerate scientific discovery by advancing machine reading approaches designed to extract claims and assertions made in the literature,… (more)

Subjects/Keywords: assembly; causal ordering; hypothesis generation; literature-based discovery; machine reading; Swanson linking

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APA (6th Edition):

Hahn-Powell, G. (2018). Machine Reading for Scientific Discovery . (Doctoral Dissertation). University of Arizona. Retrieved from http://hdl.handle.net/10150/630562

Chicago Manual of Style (16th Edition):

Hahn-Powell, Gus. “Machine Reading for Scientific Discovery .” 2018. Doctoral Dissertation, University of Arizona. Accessed August 04, 2020. http://hdl.handle.net/10150/630562.

MLA Handbook (7th Edition):

Hahn-Powell, Gus. “Machine Reading for Scientific Discovery .” 2018. Web. 04 Aug 2020.

Vancouver:

Hahn-Powell G. Machine Reading for Scientific Discovery . [Internet] [Doctoral dissertation]. University of Arizona; 2018. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/10150/630562.

Council of Science Editors:

Hahn-Powell G. Machine Reading for Scientific Discovery . [Doctoral Dissertation]. University of Arizona; 2018. Available from: http://hdl.handle.net/10150/630562


Virginia Commonwealth University

8. Henry, Sam. Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery.

Degree: PhD, Computer Science, 2019, Virginia Commonwealth University

  The exponential growth of scientific literature is creating an increased need for systems to process and assimilate knowledge contained within text. Literature Based Discovery(more)

Subjects/Keywords: Literature Based Discovery; Semantic Association; Semantic Relatedness; Natural Language Processing; Data Mining; Text Processing; Text Mining; Other Computer Sciences

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APA (6th Edition):

Henry, S. (2019). Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855

Chicago Manual of Style (16th Edition):

Henry, Sam. “Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery.” 2019. Doctoral Dissertation, Virginia Commonwealth University. Accessed August 04, 2020. https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855.

MLA Handbook (7th Edition):

Henry, Sam. “Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery.” 2019. Web. 04 Aug 2020.

Vancouver:

Henry S. Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2019. [cited 2020 Aug 04]. Available from: https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855.

Council of Science Editors:

Henry S. Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery. [Doctoral Dissertation]. Virginia Commonwealth University; 2019. Available from: https://doi.org/10.25772/C1P9-WG56 ; https://scholarscompass.vcu.edu/etd/5855

9. Ozgur, Arzucan. Text and Network Mining for Literature-Based Scientific Discovery in Biomedicine.

Degree: PhD, Computer Science & Engineering, 2010, University of Michigan

 Most of the new and important findings in biomedicine are only available in the text of the published scientific articles. The first goal of this… (more)

Subjects/Keywords: Information Extraction; Natural Language Processing; Text Mining; Bioinformatics; Literature-based Discovery; Network Analysis; Computer Science; Engineering; Science

…TEXT AND NETWORK MINING FOR LITERATURE-BASED SCIENTIFIC DISCOVERY IN BIOMEDICINE by ¨ ur… …Literature-Based Discovery . . . . Guide to Remaining Chapters… …62 64 69 69 69 71 72 73 76 76 77 78 83 V. Literature-Based Discovery of Vaccine Mediated… …Description of the literature-based discovery system for identifying gene-disease associations… …73 5.1 General framework of the literature-based discovery approach… 

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APA (6th Edition):

Ozgur, A. (2010). Text and Network Mining for Literature-Based Scientific Discovery in Biomedicine. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/78956

Chicago Manual of Style (16th Edition):

Ozgur, Arzucan. “Text and Network Mining for Literature-Based Scientific Discovery in Biomedicine.” 2010. Doctoral Dissertation, University of Michigan. Accessed August 04, 2020. http://hdl.handle.net/2027.42/78956.

MLA Handbook (7th Edition):

Ozgur, Arzucan. “Text and Network Mining for Literature-Based Scientific Discovery in Biomedicine.” 2010. Web. 04 Aug 2020.

Vancouver:

Ozgur A. Text and Network Mining for Literature-Based Scientific Discovery in Biomedicine. [Internet] [Doctoral dissertation]. University of Michigan; 2010. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/2027.42/78956.

Council of Science Editors:

Ozgur A. Text and Network Mining for Literature-Based Scientific Discovery in Biomedicine. [Doctoral Dissertation]. University of Michigan; 2010. Available from: http://hdl.handle.net/2027.42/78956

10. Sarafraz, Farzaneh. Finding Conflicting Statements in the Biomedical Literature.

Degree: 2012, University of Manchester

 The main archive of life sciences literature currently contains more than 18,000,000 references, and it is virtually impossible for any human to stay up-to-date with… (more)

Subjects/Keywords: Text Mining; Natural Language Processing; Information Extraction; Biomedical Text Mining; Bioinformatics; Negation; Contradiction; Contrast; Literature-based discovery; Molecular Event Extraction

…Figure 6.9: Event numbers in the literature over time… …236 Table 6.13: Literature-scale event extraction counts… …270 13 Abstract The main archive of life sciences literature currently contains more than… …literature is novel and distinct. Scientists report repeat experiments, or refer to previous… …are all shades of agreement between the claimed facts in the literature, and considering the… 

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APA (6th Edition):

Sarafraz, F. (2012). Finding Conflicting Statements in the Biomedical Literature. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:157382

Chicago Manual of Style (16th Edition):

Sarafraz, Farzaneh. “Finding Conflicting Statements in the Biomedical Literature.” 2012. Doctoral Dissertation, University of Manchester. Accessed August 04, 2020. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:157382.

MLA Handbook (7th Edition):

Sarafraz, Farzaneh. “Finding Conflicting Statements in the Biomedical Literature.” 2012. Web. 04 Aug 2020.

Vancouver:

Sarafraz F. Finding Conflicting Statements in the Biomedical Literature. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2020 Aug 04]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:157382.

Council of Science Editors:

Sarafraz F. Finding Conflicting Statements in the Biomedical Literature. [Doctoral Dissertation]. University of Manchester; 2012. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:157382


University of Manchester

11. Sarafraz, Farzaneh. Finding conflicting statements in the biomedical literature.

Degree: PhD, 2012, University of Manchester

 The main archive of life sciences literature currently contains more than 18,000,000 references, and it is virtually impossible for any human to stay up-to-date with… (more)

Subjects/Keywords: 006.312; Text Mining; Natural Language Processing; Information Extraction; Biomedical Text Mining; Bioinformatics; Negation; Contradiction; Contrast; Literature-based discovery; Molecular Event Extraction

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APA (6th Edition):

Sarafraz, F. (2012). Finding conflicting statements in the biomedical literature. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/finding-conflicting-statements-in-the-biomedical-literature(963e490a-eeea-4f4c-864d-fb318899beed).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553430

Chicago Manual of Style (16th Edition):

Sarafraz, Farzaneh. “Finding conflicting statements in the biomedical literature.” 2012. Doctoral Dissertation, University of Manchester. Accessed August 04, 2020. https://www.research.manchester.ac.uk/portal/en/theses/finding-conflicting-statements-in-the-biomedical-literature(963e490a-eeea-4f4c-864d-fb318899beed).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553430.

MLA Handbook (7th Edition):

Sarafraz, Farzaneh. “Finding conflicting statements in the biomedical literature.” 2012. Web. 04 Aug 2020.

Vancouver:

Sarafraz F. Finding conflicting statements in the biomedical literature. [Internet] [Doctoral dissertation]. University of Manchester; 2012. [cited 2020 Aug 04]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/finding-conflicting-statements-in-the-biomedical-literature(963e490a-eeea-4f4c-864d-fb318899beed).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553430.

Council of Science Editors:

Sarafraz F. Finding conflicting statements in the biomedical literature. [Doctoral Dissertation]. University of Manchester; 2012. Available from: https://www.research.manchester.ac.uk/portal/en/theses/finding-conflicting-statements-in-the-biomedical-literature(963e490a-eeea-4f4c-864d-fb318899beed).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.553430


Clemson University

12. Sybrandt, Justin George. Exploiting Latent Features of Text and Graphs.

Degree: PhD, School of Computing, 2020, Clemson University

  As the size and scope of online data continues to grow, new machine learning techniques become necessary to best capitalize on the wealth of… (more)

Subjects/Keywords: Conditional Text Generation; Graph Embedding; Hypergraph Partitioning; Hypothesis Generation; Literature-based Discovery; Text Embedding

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APA (6th Edition):

Sybrandt, J. G. (2020). Exploiting Latent Features of Text and Graphs. (Doctoral Dissertation). Clemson University. Retrieved from https://tigerprints.clemson.edu/all_dissertations/2592

Chicago Manual of Style (16th Edition):

Sybrandt, Justin George. “Exploiting Latent Features of Text and Graphs.” 2020. Doctoral Dissertation, Clemson University. Accessed August 04, 2020. https://tigerprints.clemson.edu/all_dissertations/2592.

MLA Handbook (7th Edition):

Sybrandt, Justin George. “Exploiting Latent Features of Text and Graphs.” 2020. Web. 04 Aug 2020.

Vancouver:

Sybrandt JG. Exploiting Latent Features of Text and Graphs. [Internet] [Doctoral dissertation]. Clemson University; 2020. [cited 2020 Aug 04]. Available from: https://tigerprints.clemson.edu/all_dissertations/2592.

Council of Science Editors:

Sybrandt JG. Exploiting Latent Features of Text and Graphs. [Doctoral Dissertation]. Clemson University; 2020. Available from: https://tigerprints.clemson.edu/all_dissertations/2592


Georgia Tech

13. Uppal, Karan. Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization.

Degree: PhD, Biology, 2015, Georgia Tech

 This thesis focuses on developing a computational framework to support the Precision Medicine Initiative. The newly developed tools and algorithms use machine learning, text mining… (more)

Subjects/Keywords: Literature mining; Literature-based discovery; Text mining; Text summarization; Visualization; Content recognition; Feature selection; Data mining

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APA (6th Edition):

Uppal, K. (2015). Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55548

Chicago Manual of Style (16th Edition):

Uppal, Karan. “Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization.” 2015. Doctoral Dissertation, Georgia Tech. Accessed August 04, 2020. http://hdl.handle.net/1853/55548.

MLA Handbook (7th Edition):

Uppal, Karan. “Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization.” 2015. Web. 04 Aug 2020.

Vancouver:

Uppal K. Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/1853/55548.

Council of Science Editors:

Uppal K. Translational bioinformatics for personalized medicine and integrative biology: Data integration, extraction, knowledge discovery, and visualization. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/55548

14. Wang, Yu. Literature-based discovery of known and potential new mechanisms for relating the status of cholesterol to the progression of breast cancer.

Degree: MS, Bioinformatics, 2019, University of Illinois – Urbana-Champaign

 Breast cancer has been studied for a long period of time and from a variety of perspectives in order to understand its pathogeny. The pathogeny… (more)

Subjects/Keywords: Literature-based discovery; literature review; breast cancer; cholesterol; mechanisms

…also be divided into five intrinsic molecular-based subclasses: basal-like (BL)… …amount of literature focused on both cholesterol and breast cancer published to PubMed per year… …literature on cholesterol and breast cancer on PubMed using the heavily restricted search term; and… …x5D; Second, the set of literature was restricted using the medially restricted search term… …with the term in the section “Potential New Mechanisms.” 3.2. Second Stage – Literature… 

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APA (6th Edition):

Wang, Y. (2019). Literature-based discovery of known and potential new mechanisms for relating the status of cholesterol to the progression of breast cancer. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104825

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, Yu. “Literature-based discovery of known and potential new mechanisms for relating the status of cholesterol to the progression of breast cancer.” 2019. Thesis, University of Illinois – Urbana-Champaign. Accessed August 04, 2020. http://hdl.handle.net/2142/104825.

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

MLA Handbook (7th Edition):

Wang, Yu. “Literature-based discovery of known and potential new mechanisms for relating the status of cholesterol to the progression of breast cancer.” 2019. Web. 04 Aug 2020.

Vancouver:

Wang Y. Literature-based discovery of known and potential new mechanisms for relating the status of cholesterol to the progression of breast cancer. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2019. [cited 2020 Aug 04]. Available from: http://hdl.handle.net/2142/104825.

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

Council of Science Editors:

Wang Y. Literature-based discovery of known and potential new mechanisms for relating the status of cholesterol to the progression of breast cancer. [Thesis]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104825

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

.