Deriving protein-protein interactions of dengue from literature by using automatic content extraction features
Dengue Fever is one of the most severe diseases spread throughout the tropics. However, due to the immunity response elicited among the serotypes of dengue virus, it is very difficult to develop vaccines to protect human from dengue infections. However, with the advancement in technology, researcher...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/10356/62840 |
| _version_ | 1811679123078119424 |
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| author | Huang, Yizhou |
| author2 | Rajapakse Jagath Chandana |
| author_facet | Rajapakse Jagath Chandana Huang, Yizhou |
| author_sort | Huang, Yizhou |
| collection | NTU |
| description | Dengue Fever is one of the most severe diseases spread throughout the tropics. However, due to the immunity response elicited among the serotypes of dengue virus, it is very difficult to develop vaccines to protect human from dengue infections. However, with the advancement in technology, researchers have focused on the area of genetic structure to develop vaccines. This project aims to regulate Automatic Content Extraction features and uses these features to derive protein interactions and gene regulation relations through text mining. The human gene list and dengue gene list are downloaded from online genome mapping repository while the texts are retrieved from abstracts of biomedical literature. Sentences are then pre-processed for further analysis. Biological knowledge and facts on gene regulations and protein interactions are generated with optimized methods and techniques. In this project, the keyword-tag and word-relation-word features are extracted to describe the regulation relations. To investigate the performance of different feature sets, this project makes use of Stanford Natural Language Processing Tools to analyse the semantic structure of sentences. A decision tree classifier is trained to learn the extracted patterns to perform the prediction job. The accuracy based on keyword-tag and word-relation-word feature have reached 99.4%. The reason for high accuracy is that the feature sets also contain some features extracted from the testing dataset. To improve this problem, more datasets will be involved to evaluate the performance. |
| first_indexed | 2024-10-01T03:04:09Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/62840 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:04:09Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | ntu-10356/628402023-03-03T20:37:26Z Deriving protein-protein interactions of dengue from literature by using automatic content extraction features Huang, Yizhou Rajapakse Jagath Chandana School of Computer Engineering Centre for Computational Intelligence DRNTU::Engineering::Computer science and engineering::Data Dengue Fever is one of the most severe diseases spread throughout the tropics. However, due to the immunity response elicited among the serotypes of dengue virus, it is very difficult to develop vaccines to protect human from dengue infections. However, with the advancement in technology, researchers have focused on the area of genetic structure to develop vaccines. This project aims to regulate Automatic Content Extraction features and uses these features to derive protein interactions and gene regulation relations through text mining. The human gene list and dengue gene list are downloaded from online genome mapping repository while the texts are retrieved from abstracts of biomedical literature. Sentences are then pre-processed for further analysis. Biological knowledge and facts on gene regulations and protein interactions are generated with optimized methods and techniques. In this project, the keyword-tag and word-relation-word features are extracted to describe the regulation relations. To investigate the performance of different feature sets, this project makes use of Stanford Natural Language Processing Tools to analyse the semantic structure of sentences. A decision tree classifier is trained to learn the extracted patterns to perform the prediction job. The accuracy based on keyword-tag and word-relation-word feature have reached 99.4%. The reason for high accuracy is that the feature sets also contain some features extracted from the testing dataset. To improve this problem, more datasets will be involved to evaluate the performance. Bachelor of Engineering (Computer Science) 2015-04-30T02:00:00Z 2015-04-30T02:00:00Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/62840 en Nanyang Technological University 59 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Data Huang, Yizhou Deriving protein-protein interactions of dengue from literature by using automatic content extraction features |
| title | Deriving protein-protein interactions of dengue from literature by using automatic content extraction features |
| title_full | Deriving protein-protein interactions of dengue from literature by using automatic content extraction features |
| title_fullStr | Deriving protein-protein interactions of dengue from literature by using automatic content extraction features |
| title_full_unstemmed | Deriving protein-protein interactions of dengue from literature by using automatic content extraction features |
| title_short | Deriving protein-protein interactions of dengue from literature by using automatic content extraction features |
| title_sort | deriving protein protein interactions of dengue from literature by using automatic content extraction features |
| topic | DRNTU::Engineering::Computer science and engineering::Data |
| url | http://hdl.handle.net/10356/62840 |
| work_keys_str_mv | AT huangyizhou derivingproteinproteininteractionsofdenguefromliteraturebyusingautomaticcontentextractionfeatures |