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You searched for subject:(drug target prediction). Showing records 1 – 10 of 10 total matches.

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Virginia Commonwealth University

1. Wang, Chen. High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome.

Degree: PhD, Computer Science, 2018, Virginia Commonwealth University

  Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the… (more)

Subjects/Keywords: drug-protein interactions; computational prediction; drug target database; drug repurposing; drug side-effects; Bioinformatics

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

APA (6th Edition):

Wang, C. (2018). High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome. (Doctoral Dissertation). Virginia Commonwealth University. Retrieved from https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509

Chicago Manual of Style (16th Edition):

Wang, Chen. “High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome.” 2018. Doctoral Dissertation, Virginia Commonwealth University. Accessed April 23, 2021. https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509.

MLA Handbook (7th Edition):

Wang, Chen. “High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome.” 2018. Web. 23 Apr 2021.

Vancouver:

Wang C. High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome. [Internet] [Doctoral dissertation]. Virginia Commonwealth University; 2018. [cited 2021 Apr 23]. Available from: https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509.

Council of Science Editors:

Wang C. High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome. [Doctoral Dissertation]. Virginia Commonwealth University; 2018. Available from: https://doi.org/10.25772/E2X0-B462 ; https://scholarscompass.vcu.edu/etd/5509


University of Melbourne

2. Hameed, Pathima Nusrath. In silico methods for drug repositioning and drug-drug interaction prediction.

Degree: 2018, University of Melbourne

Drug repositioning and drug-drug interaction (DDI) prediction are two fundamental applications having a large impact on drug development and clinical care. Drug repositioning aims to… (more)

Subjects/Keywords: drug repurposing; biomedical informatics; DDI prediction; drug-target prediction; heterogeneous data integration; cluster evaluation

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

APA (6th Edition):

Hameed, P. N. (2018). In silico methods for drug repositioning and drug-drug interaction prediction. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/219484

Chicago Manual of Style (16th Edition):

Hameed, Pathima Nusrath. “In silico methods for drug repositioning and drug-drug interaction prediction.” 2018. Doctoral Dissertation, University of Melbourne. Accessed April 23, 2021. http://hdl.handle.net/11343/219484.

MLA Handbook (7th Edition):

Hameed, Pathima Nusrath. “In silico methods for drug repositioning and drug-drug interaction prediction.” 2018. Web. 23 Apr 2021.

Vancouver:

Hameed PN. In silico methods for drug repositioning and drug-drug interaction prediction. [Internet] [Doctoral dissertation]. University of Melbourne; 2018. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/11343/219484.

Council of Science Editors:

Hameed PN. In silico methods for drug repositioning and drug-drug interaction prediction. [Doctoral Dissertation]. University of Melbourne; 2018. Available from: http://hdl.handle.net/11343/219484


University of California – Santa Cruz

3. Woehrmann, Marcos H. Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms.

Degree: Biomolecular Engineering and Bioinformatics, 2015, University of California – Santa Cruz

 To develop more effective therapies to treat human diseases, a better method of finding the biological targets and modes of action of new compounds is… (more)

Subjects/Keywords: Bioinformatics; Bioinformatics; Cytological Profiling; Drug Target Prediction; High-throughput Screening; HTS

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

APA (6th Edition):

Woehrmann, M. H. (2015). Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms. (Thesis). University of California – Santa Cruz. Retrieved from http://www.escholarship.org/uc/item/9976p4ch

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):

Woehrmann, Marcos H. “Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms.” 2015. Thesis, University of California – Santa Cruz. Accessed April 23, 2021. http://www.escholarship.org/uc/item/9976p4ch.

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

MLA Handbook (7th Edition):

Woehrmann, Marcos H. “Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms.” 2015. Web. 23 Apr 2021.

Vancouver:

Woehrmann MH. Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms. [Internet] [Thesis]. University of California – Santa Cruz; 2015. [cited 2021 Apr 23]. Available from: http://www.escholarship.org/uc/item/9976p4ch.

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

Council of Science Editors:

Woehrmann MH. Predicting the Mode of Action of Bioactive Compounds via High Throughput Screening and Computational Algorithms. [Thesis]. University of California – Santa Cruz; 2015. Available from: http://www.escholarship.org/uc/item/9976p4ch

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


Indiana University

4. Seal, Abhik. RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES .

Degree: 2016, Indiana University

Prediction of unknown drug target interactions from bioassay data is critical not only for the understanding of various interactions but also crucial for the development… (more)

Subjects/Keywords: Random walk; drug target; link prediction; disease; metabolic pathway; R; Shiny

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

APA (6th Edition):

Seal, A. (2016). RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES . (Thesis). Indiana University. Retrieved from http://hdl.handle.net/2022/20765

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):

Seal, Abhik. “RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES .” 2016. Thesis, Indiana University. Accessed April 23, 2021. http://hdl.handle.net/2022/20765.

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

MLA Handbook (7th Edition):

Seal, Abhik. “RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES .” 2016. Web. 23 Apr 2021.

Vancouver:

Seal A. RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES . [Internet] [Thesis]. Indiana University; 2016. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/2022/20765.

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

Council of Science Editors:

Seal A. RANDOM WALK APPLIED TO HETEROGENOUS DRUG-TARGET NETWORKS FOR PREDICTING BIOLOGICAL OUTCOMES . [Thesis]. Indiana University; 2016. Available from: http://hdl.handle.net/2022/20765

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


King Abdullah University of Science and Technology

5. Olayan, Rawan S. Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches.

Degree: Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, 2017, King Abdullah University of Science and Technology

 Computational drug repurposing aims at finding new medical uses for existing drugs. The identification of novel drug-target interactions (DTIs) can be a useful part of… (more)

Subjects/Keywords: drug–target interaction prediction; link prediction; Bioinformatics; chemoinformatics; Machine Learning; graph mining

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

APA (6th Edition):

Olayan, R. S. (2017). Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches. (Thesis). King Abdullah University of Science and Technology. Retrieved from http://hdl.handle.net/10754/626424

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):

Olayan, Rawan S. “Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches.” 2017. Thesis, King Abdullah University of Science and Technology. Accessed April 23, 2021. http://hdl.handle.net/10754/626424.

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

MLA Handbook (7th Edition):

Olayan, Rawan S. “Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches.” 2017. Web. 23 Apr 2021.

Vancouver:

Olayan RS. Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches. [Internet] [Thesis]. King Abdullah University of Science and Technology; 2017. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10754/626424.

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

Council of Science Editors:

Olayan RS. Novel computational methods to predict drug–target interactions using graph mining and machine learning approaches. [Thesis]. King Abdullah University of Science and Technology; 2017. Available from: http://hdl.handle.net/10754/626424

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


University of California – San Francisco

6. Yera, Emmanuel Ramon. Rationalizing Drug Pharmacology based on Computational Methods.

Degree: Biological and Medical Informatics, 2013, University of California – San Francisco

 Large-scale experimental determination of the protein targets of small molecules is both time-consuming and costly. Computational methods can be used to predict interactions between small… (more)

Subjects/Keywords: Bioinformatics; drug discovery; drug effects; molecular similarity; natural language processing; target prediction

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

APA (6th Edition):

Yera, E. R. (2013). Rationalizing Drug Pharmacology based on Computational Methods. (Thesis). University of California – San Francisco. Retrieved from http://www.escholarship.org/uc/item/3mf2z296

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):

Yera, Emmanuel Ramon. “Rationalizing Drug Pharmacology based on Computational Methods.” 2013. Thesis, University of California – San Francisco. Accessed April 23, 2021. http://www.escholarship.org/uc/item/3mf2z296.

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

MLA Handbook (7th Edition):

Yera, Emmanuel Ramon. “Rationalizing Drug Pharmacology based on Computational Methods.” 2013. Web. 23 Apr 2021.

Vancouver:

Yera ER. Rationalizing Drug Pharmacology based on Computational Methods. [Internet] [Thesis]. University of California – San Francisco; 2013. [cited 2021 Apr 23]. Available from: http://www.escholarship.org/uc/item/3mf2z296.

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

Council of Science Editors:

Yera ER. Rationalizing Drug Pharmacology based on Computational Methods. [Thesis]. University of California – San Francisco; 2013. Available from: http://www.escholarship.org/uc/item/3mf2z296

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

7. Zoufir, Azedine. Computational analyses of small molecules activity from phenotypic screens.

Degree: PhD, 2019, University of Cambridge

Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining… (more)

Subjects/Keywords: Drug discovery; Cheminformatics; Chemoinformatics; Phenotypic Screening; Target Prediction; Structural Bioinformatics; Machine Learning; Bayesian Statistics; Self Organising Maps; Polycystic Kidney Disease

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

APA (6th Edition):

Zoufir, A. (2019). Computational analyses of small molecules activity from phenotypic screens. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/293251

Chicago Manual of Style (16th Edition):

Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Doctoral Dissertation, University of Cambridge. Accessed April 23, 2021. https://www.repository.cam.ac.uk/handle/1810/293251.

MLA Handbook (7th Edition):

Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Web. 23 Apr 2021.

Vancouver:

Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 Apr 23]. Available from: https://www.repository.cam.ac.uk/handle/1810/293251.

Council of Science Editors:

Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://www.repository.cam.ac.uk/handle/1810/293251

8. Zoufir, Azedine. Computational analyses of small molecules activity from phenotypic screens.

Degree: PhD, 2019, University of Cambridge

Drug discovery is no longer relying on the one gene-one disease paradigm nor on target-based screening alone to discover new drugs. Phenotypic-based screening is regaining… (more)

Subjects/Keywords: Drug discovery; Cheminformatics; Chemoinformatics; Phenotypic Screening; Target Prediction; Structural Bioinformatics; Machine Learning; Bayesian Statistics; Self Organising Maps; Polycystic Kidney Disease

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

APA (6th Edition):

Zoufir, A. (2019). Computational analyses of small molecules activity from phenotypic screens. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760

Chicago Manual of Style (16th Edition):

Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Doctoral Dissertation, University of Cambridge. Accessed April 23, 2021. https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760.

MLA Handbook (7th Edition):

Zoufir, Azedine. “Computational analyses of small molecules activity from phenotypic screens.” 2019. Web. 23 Apr 2021.

Vancouver:

Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Internet] [Doctoral dissertation]. University of Cambridge; 2019. [cited 2021 Apr 23]. Available from: https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760.

Council of Science Editors:

Zoufir A. Computational analyses of small molecules activity from phenotypic screens. [Doctoral Dissertation]. University of Cambridge; 2019. Available from: https://doi.org/10.17863/CAM.40403 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782760


Universitat Pompeu Fabra

9. Martínez-Jiménez, Francisco, 1988-. Structural study of the therapeutic potential of protein-ligand interactions.

Degree: Departament de Ciències Experimentals i de la Salut, 2016, Universitat Pompeu Fabra

 La mayor´ıa de las funciones celulares est´an dirigidas por peque˜nas mol´eculas que selectivamente se unen a sus prote´ınas diana. Es tal su importancia que la… (more)

Subjects/Keywords: Ligand-target prediction; Structural bioinformatics; Tuberculosis drug discovery; Drug resistance; Targeted cancer therapy; Pequeñas moléculas; Bioinformática estructural; Tuberculosis; Resistencia a tratamiento; Terapias dirigidas; 615

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

APA (6th Edition):

Martínez-Jiménez, Francisco, 1. (2016). Structural study of the therapeutic potential of protein-ligand interactions. (Thesis). Universitat Pompeu Fabra. Retrieved from http://hdl.handle.net/10803/565402

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):

Martínez-Jiménez, Francisco, 1988-. “Structural study of the therapeutic potential of protein-ligand interactions.” 2016. Thesis, Universitat Pompeu Fabra. Accessed April 23, 2021. http://hdl.handle.net/10803/565402.

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

MLA Handbook (7th Edition):

Martínez-Jiménez, Francisco, 1988-. “Structural study of the therapeutic potential of protein-ligand interactions.” 2016. Web. 23 Apr 2021.

Vancouver:

Martínez-Jiménez, Francisco 1. Structural study of the therapeutic potential of protein-ligand interactions. [Internet] [Thesis]. Universitat Pompeu Fabra; 2016. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/10803/565402.

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

Council of Science Editors:

Martínez-Jiménez, Francisco 1. Structural study of the therapeutic potential of protein-ligand interactions. [Thesis]. Universitat Pompeu Fabra; 2016. Available from: http://hdl.handle.net/10803/565402

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


Leiden University

10. Taneja, A. PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement.

Degree: 2013, Leiden University

 Chronic pain is a significant health problem that greatly impacts the quality of life of individual patients and imparts high costs to society. Despite intense… (more)

Subjects/Keywords: Translation; Biomarkers; Chronic pain; Neuropathic pain; Prediction; Engagement; Target; Drug development; Analgesic; Relationships; PKPD

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

APA (6th Edition):

Taneja, A. (2013). PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement. (Doctoral Dissertation). Leiden University. Retrieved from http://hdl.handle.net/1887/22300

Chicago Manual of Style (16th Edition):

Taneja, A. “PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement.” 2013. Doctoral Dissertation, Leiden University. Accessed April 23, 2021. http://hdl.handle.net/1887/22300.

MLA Handbook (7th Edition):

Taneja, A. “PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement.” 2013. Web. 23 Apr 2021.

Vancouver:

Taneja A. PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement. [Internet] [Doctoral dissertation]. Leiden University; 2013. [cited 2021 Apr 23]. Available from: http://hdl.handle.net/1887/22300.

Council of Science Editors:

Taneja A. PKPD relationships and dose rationale in analgesic drug development : towards the prediction of target engagement. [Doctoral Dissertation]. Leiden University; 2013. Available from: http://hdl.handle.net/1887/22300

.