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You searched for subject:(Literature based Discovery). Showing records 1 – 6 of 6 total matches.

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University of North Texas

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

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 11, 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. 11 Aug 2020.

Vancouver:

Dharmavaram S. Mining Biomedical Data for Hidden Relationship Discovery. [Internet] [Thesis]. University of North Texas; 2019. [cited 2020 Aug 11]. 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


Clemson University

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

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 11, 2020. https://tigerprints.clemson.edu/all_dissertations/2592.

MLA Handbook (7th Edition):

Sybrandt, Justin George. “Exploiting Latent Features of Text and Graphs.” 2020. Web. 11 Aug 2020.

Vancouver:

Sybrandt JG. Exploiting Latent Features of Text and Graphs. [Internet] [Doctoral dissertation]. Clemson University; 2020. [cited 2020 Aug 11]. 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


Virginia Commonwealth University

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

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 11, 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. 11 Aug 2020.

Vancouver:

Henry S. Indirect Relatedness, Evaluation, and Visualization for Literature Based Discovery. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2019. [cited 2020 Aug 11]. 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

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

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 11, 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. 11 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 11]. 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

5. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 11, 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. 11 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 11]. 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

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

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 11, 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. 11 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 11]. 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

.