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Part 1: Document Description
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Citation |
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Title: |
Computational identification of physicochemical signatures for host tropism of influenza A virus |
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Identification Number: |
doi:10.21979/N9/5RIS7R |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2019-04-22 |
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Version: |
1 |
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Bibliographic Citation: |
Yin, Rui, 2019, "Computational identification of physicochemical signatures for host tropism of influenza A virus", https://doi.org/10.21979/N9/5RIS7R, DR-NTU (Data), V1 |
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Citation |
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Title: |
Computational identification of physicochemical signatures for host tropism of influenza A virus |
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Identification Number: |
doi:10.21979/N9/5RIS7R |
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Authoring Entity: |
Yin, Rui (Nanyang Technological University) |
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Software used in Production: |
Python |
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Grant Number: |
AcRF Tier 2 grant MOE2014-T2-2-023 |
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Grant Number: |
Tier 1 grant RG21/15 2015-T1-001-169- 11 |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Yin Rui |
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Depositor: |
Yin Rui |
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Date of Deposit: |
2019-04-22 |
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Holdings Information: |
https://doi.org/10.21979/N9/5RIS7R |
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Study Scope |
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Keywords: |
Computer and Information Science, Medicine, Health and Life Sciences, Computer and Information Science, Medicine, Health and Life Sciences, In°uenza; hemagglutinin; host tropism; physicochemical signature; association rules |
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Abstract: |
Avian influenza viruses from migratory birds have managed to cross host species barriers and infected various hosts like human and swine. Epidemics and pandemics might occur when influenza viruses are adapted to humans, causing deaths and enormous economic loss. Receptor-binding specificity of the virus is one of the key factors for the transmission of influenza viruses across species. The determination of host tropism and understanding of molecular properties would help identify the mechanism why zoonotic influenza viruses can cross the species barrier and infect humans. In this study, we have constructed computational models for host tropism prediction on human-adapted subtypes of influenza HA proteins using random forest. The feature vectors of the prediction models were generated based on seven physicochemical properties of amino acids from influenza sequences of three major hosts. Feature aggregation and associative rules were further applied to select top 20 features and extract host-associated physicochemical signatures on the combined model of nonspecific subtypes. The prediction model achieved high performance (Accuracy=0.948, Precision=0.954, MCC=0.922). Support and confidence rates were calculated for the host class-association rules. The results indicated that secondary structure and normalized Van der Waals volume were identified as more important physicochemical signatures in determining the host tropism. |
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Kind of Data: |
.rar |
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Notes: |
Additional files |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Identification Number: |
10.1142/S0219720018400231 |
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Bibliographic Citation: |
Yin, R., Zhou, X., Zheng, J.,& Kwoh, C. K. (2019). Computational identification of physicochemical signatures for host tropism of influenza A virus. Journal of Bioinformatics and Computational Biology, 16(06), 1840023-. |
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Label: |
supplementary.rar |
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Text: |
Additional files for Computational identification of physicochemical signatures for host tropism of influenza A virus |
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Notes: |
application/x-rar-compressed |