Prediction of functionality important sites from protein sequences
57 p.
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| Tác giả khác: | |
| Định dạng: | Luận văn |
| Được phát hành: |
2011
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| Những chủ đề: | |
| Truy cập trực tuyến: | http://hdl.handle.net/10356/46760 |
| _version_ | 1826128819928432640 |
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| author | Muralidharan Nandakumar. |
| author2 | Ponnuthurai Nagaratnam Suganthan |
| author_facet | Ponnuthurai Nagaratnam Suganthan Muralidharan Nandakumar. |
| author_sort | Muralidharan Nandakumar. |
| collection | NTU |
| description | 57 p. |
| first_indexed | 2024-10-01T07:30:46Z |
| format | Thesis |
| id | ntu-10356/46760 |
| institution | Nanyang Technological University |
| last_indexed | 2024-10-01T07:30:46Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | ntu-10356/467602023-07-04T16:03:23Z Prediction of functionality important sites from protein sequences Muralidharan Nandakumar. Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering DRNTU::Engineering 57 p. The dissertation analyses the procedure for training the SVM for an imbalance multiclass dataset and two-class dataset and thereby maximize the prediction accuracy. One against all approach is followed for the multiclass problem and standard binary SVM for the two-class dataset. Experiments were performed to find the prediction accuracy using the proposed algorithm. The algorithm is tested on five datasets having 5261 samples (1444 features), 5261 samples (61 features), 768 samples, 197 samples (23 features), 267 samples (45 features). The probability estimates and also the decision functions values are found for the multiclass datasets. The one against all accuracies and prediction accuracies for the datasets considered are tabulated. The classification accuracy using the proposed method is tabulated below for each dataset. The SCOP datasets were classified with accuracy of 55.638 and 55.42 percentages. The SCOP datasets are multiclass datasets, whereas the two-class datasets as Pima dataset, Parkinson's dataset and SPECTF heart dataset were classified with the accuracy of 77, 93.2254 and 80.769 percentage respectively. The Algorithm used in this project needs to be tested on more datasets in the future. Master of Science (Computer Control and Automation) 2011-12-23T07:44:24Z 2011-12-23T07:44:24Z 2010 2010 Thesis http://hdl.handle.net/10356/46760 Nanyang Technological University application/pdf |
| spellingShingle | DRNTU::Engineering Muralidharan Nandakumar. Prediction of functionality important sites from protein sequences |
| title | Prediction of functionality important sites from protein sequences |
| title_full | Prediction of functionality important sites from protein sequences |
| title_fullStr | Prediction of functionality important sites from protein sequences |
| title_full_unstemmed | Prediction of functionality important sites from protein sequences |
| title_short | Prediction of functionality important sites from protein sequences |
| title_sort | prediction of functionality important sites from protein sequences |
| topic | DRNTU::Engineering |
| url | http://hdl.handle.net/10356/46760 |
| work_keys_str_mv | AT muralidharannandakumar predictionoffunctionalityimportantsitesfromproteinsequences |