Learning evolutionary and virulence patterns of influenza viruses
Without any warnings, influenzas can strike and take away the precious lives of both humans as well as livestock. They are deadly, uninvited and severe. It still remains as a blur amongst experts on how such viruses can actually result into an epidemic or pandemic. The project that I embarked on...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/76446 |
| _version_ | 1811678943757991936 |
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| author | Tan, Tosy Ying Jie |
| author2 | Kwoh Chee Keong |
| author_facet | Kwoh Chee Keong Tan, Tosy Ying Jie |
| author_sort | Tan, Tosy Ying Jie |
| collection | NTU |
| description | Without any warnings, influenzas can strike and take away the precious lives of both humans as well as livestock. They are deadly, uninvited and severe. It still remains as a blur amongst experts on how such viruses can actually result into an epidemic or pandemic.
The project that I embarked on is a continuation of a current Final Year Project (FYP). This project aims to study on the virulence patterns of influenza viruses, hoping to bring us one step closer to being ahead in the race of evolution of viruses where we will be able to predict the possibility of a pandemic before it actually occurs.
Focusing on Type A and B influenzas, the median lethal dosage value was explored to see how it affects the virulence level of the viruses. A series of data processing steps has to be done before classification can take place. Three different classification methods, namely JRip, OneR and PART, were used in this project.
The influenza viruses were first classified according to the various ribonucleic acid (RNA) segments. The classification metrics that returned the best results out of the three cases tested was further explored where different types of categorisation of the dataset (eg. by host strains and subtypes) were considered. Boosting techniques were also applied to further improve the classification results.
Although the classification results were not as ideal, we managed to conclude that
the median lethal dosage has an influence in determining the virulence level of an
influenza virus. It was also proven that the HA segments contains crucial information on the virulence of influenza viruses as well as shown that categorisation by host strains seems to produce better classification results.
Hence, further works recommended can be to (1) narrow down the scope of focus (eg. solely by subtype or RNA segment) first to have a better and more complete understanding on the virulence patterns or (2) further tuning parameters for boosting to improve the classification performances. |
| first_indexed | 2024-10-01T03:01:18Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/76446 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:01:18Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/764462023-03-03T20:27:16Z Learning evolutionary and virulence patterns of influenza viruses Tan, Tosy Ying Jie Kwoh Chee Keong School of Computer Science and Engineering Bioinformatics Research Centre Fransiskus Xaverius Ivan DRNTU::Engineering::Computer science and engineering Without any warnings, influenzas can strike and take away the precious lives of both humans as well as livestock. They are deadly, uninvited and severe. It still remains as a blur amongst experts on how such viruses can actually result into an epidemic or pandemic. The project that I embarked on is a continuation of a current Final Year Project (FYP). This project aims to study on the virulence patterns of influenza viruses, hoping to bring us one step closer to being ahead in the race of evolution of viruses where we will be able to predict the possibility of a pandemic before it actually occurs. Focusing on Type A and B influenzas, the median lethal dosage value was explored to see how it affects the virulence level of the viruses. A series of data processing steps has to be done before classification can take place. Three different classification methods, namely JRip, OneR and PART, were used in this project. The influenza viruses were first classified according to the various ribonucleic acid (RNA) segments. The classification metrics that returned the best results out of the three cases tested was further explored where different types of categorisation of the dataset (eg. by host strains and subtypes) were considered. Boosting techniques were also applied to further improve the classification results. Although the classification results were not as ideal, we managed to conclude that the median lethal dosage has an influence in determining the virulence level of an influenza virus. It was also proven that the HA segments contains crucial information on the virulence of influenza viruses as well as shown that categorisation by host strains seems to produce better classification results. Hence, further works recommended can be to (1) narrow down the scope of focus (eg. solely by subtype or RNA segment) first to have a better and more complete understanding on the virulence patterns or (2) further tuning parameters for boosting to improve the classification performances. Bachelor of Engineering (Computer Science) 2019-03-11T07:29:27Z 2019-03-11T07:29:27Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/76446 en Nanyang Technological University 47 p. application/pdf application/pdf application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering Tan, Tosy Ying Jie Learning evolutionary and virulence patterns of influenza viruses |
| title | Learning evolutionary and virulence patterns of influenza viruses |
| title_full | Learning evolutionary and virulence patterns of influenza viruses |
| title_fullStr | Learning evolutionary and virulence patterns of influenza viruses |
| title_full_unstemmed | Learning evolutionary and virulence patterns of influenza viruses |
| title_short | Learning evolutionary and virulence patterns of influenza viruses |
| title_sort | learning evolutionary and virulence patterns of influenza viruses |
| topic | DRNTU::Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/76446 |
| work_keys_str_mv | AT tantosyyingjie learningevolutionaryandvirulencepatternsofinfluenzaviruses |