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The Effects of Novel Feature Vectors on Metagenomic Classification

Plis, Kevin A.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1399578867

Abstract Details

2014, Master of Science (MS), Ohio University, Computer Science (Engineering and Technology).
Metagenomics plays a crucial role in our understanding of the world around us. Machine learning and bioinformatics methods have struggled to accurately identify the organisms present in metagenomic samples. By using improved feature vectors, higher classification accuracy can be found when using the machine learning classification approach to identify the organisms present in a metagenomic sample. This research is a pilot study that explores novel feature vectors and their effect on metagenomic classification. A synthetic data set was created using the genomes of 32 organisms from the Archaea and Bacteria domains, with 450 fragments of varying length per organism used to train the classification models. By using a novel feature vector one tenth of the size of the currently used feature vectors, a 6.34%, 21.91%, and 15.07% improvement was found over the species level accuracy on 100, 300, and 500 bp fragments, respectively, for this data set. The results of this study also show that using more features does not always translate to a higher classification accuracy, and that higher classification accuracy can be achieved through feature selection.
Lonnie Welch, PhD (Advisor)
109 p.
Artificial Intelligence; Bioinformatics; Computer Science
Metagenomics; Classification; Machine Learning; SVM; Support Vector Machine; Feature Vector; Feature Selection; Bioinformatics

Recommended Citations

Citations

  • Plis, K. A. (2014). The Effects of Novel Feature Vectors on Metagenomic Classification [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1399578867

    APA Style (7th edition)

  • Plis, Kevin. The Effects of Novel Feature Vectors on Metagenomic Classification. 2014. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1399578867.

    MLA Style (8th edition)

  • Plis, Kevin. "The Effects of Novel Feature Vectors on Metagenomic Classification." Master's thesis, Ohio University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1399578867

    Chicago Manual of Style (17th edition)

ohiou1399578867
560
© 2014, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.