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Disease, Drug, and Target Association Predictions by Integrating Multiple Heterogeneous Sources

Yang, Sen
http://rave.ohiolink.edu/etdc/view?acc_num=case1342194249

Abstract Details

2012, Master of Sciences (Engineering), Case Western Reserve University, EECS - Computer and Information Sciences.
Computational methods for new drug development can greatly reduce time and costs compared with experimental methods. A core problem in computational drug discovery is to capture the hidden interactions among diseases, drugs, and targets, which includes two sub-problems, i.e. disease-drug association predictions and drug-target association predictions. In this thesis, computational approaches for novel large-scale disease, drug, and target association predictions are proposed. First, a heterogeneous disease-drug graph and a drug-target graph, both of which incorporate the initial interactions and intra-similarities, are constructed. Based on these graphs, a novel local graph-based inference method is introduced for both predicting problems. Second, to further enhance prediction performance, a global heterogeneous graph, which incorporates initial disease-drug and drug-target interactions and intra-similarities of disease-disease, drug-drug, and target-target, is built. A novel global graph-based inference approach is then proposed. Experimental results indicate that the proposed methods in this thesis can greatly improve disease, drug, and target association prediction accuracy on large-scale datasets compared with existing representative methods.
Jing Li (Advisor)
Z. Meral Ozsoyoglu (Committee Member)
Soumya Ray (Committee Member)
Xiang Zhang (Committee Member)
70 p.
Bioinformatics; Computer Science
disease; drug; target; association predictions; multiple heterogeneous information

Recommended Citations

Citations

  • Yang, S. (2012). Disease, Drug, and Target Association Predictions by Integrating Multiple Heterogeneous Sources [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1342194249

    APA Style (7th edition)

  • Yang, Sen. Disease, Drug, and Target Association Predictions by Integrating Multiple Heterogeneous Sources. 2012. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1342194249.

    MLA Style (8th edition)

  • Yang, Sen. "Disease, Drug, and Target Association Predictions by Integrating Multiple Heterogeneous Sources." Master's thesis, Case Western Reserve University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1342194249

    Chicago Manual of Style (17th edition)

case1342194249
866
© 2012, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.