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A workflow for the modeling and analysis of biomedical data

Marsolo, Keith Allen
http://rave.ohiolink.edu/etdc/view?acc_num=osu1180309265

Abstract Details

2007, Doctor of Philosophy, Ohio State University, Computer and Information Science.
The use of data mining techniques for the classification of shape and structure can provide critical results when applied biomedical data. On a molecular level, an object's structure influences its function, so structure-based classification can lead to a notion of functional similarity. On a more macro scale, anatomical features can define the pathology of a disease, while changes in those features over time can illustrate its progression. Thus, structural analysis can play a vital role in clinical diagnosis. When examining the problem of structural or shape classification, one would like to develop a solution that satisfies a specific task, yet is general enough to be applied elsewhere. In this work, we propose a workflow that can be used to model and analyze biomedical data, both static and time-varying. This workflow consists of four stages: 1) Modeling, 2) Biomedical Knowledge Discovery, 3) Incorporation of Domain Knowledge and 4) Visual Interpretation and Query-based Retrieval. For each stage we propose either new algorithms or suggest ways to apply existing techniques in a previously-unused manner. We present our work as a series of case studies and extensions. We also address a number of specific research questions. These contributions are as follows: We show that generalized modeling methods can be used to effectively represent data from several biomedical domains. We detail a multi-stage classification technique that seeks to improve performance by first partitioning data based on global, high-level details, then classifying each partition using local, fine-grained features. We create an ensemble-learning strategy that boosts performance by aggregating the results of classifiers built from models of varying spatial resolutions. This allows a user to benefit from models that provide a global, coarse-grained representation of the object as well as those that contain more fine-grained details, without suffering from the loss of information or noise effects that might arise from using only a single selection. Finally, we propose a method to model and characterize the defects and deterioration of function that can be indicative of certain diseases.
Srinivasan Parthasarathy (Advisor)
258 p.
Computer Science
Biomedical Data Modeling; Spatial Modeling; Biomedical Knowledge Discovery; Classification of Structure-based Data.; Bioinformatics; Protein Modeling; Protein Classification

Recommended Citations

Citations

  • Marsolo, K. A. (2007). A workflow for the modeling and analysis of biomedical data [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1180309265

    APA Style (7th edition)

  • Marsolo, Keith. A workflow for the modeling and analysis of biomedical data. 2007. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1180309265.

    MLA Style (8th edition)

  • Marsolo, Keith. "A workflow for the modeling and analysis of biomedical data." Doctoral dissertation, Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1180309265

    Chicago Manual of Style (17th edition)

osu1180309265
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© 2007, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.