Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice

Background: Influenza A virus (IAV) poses threats to human health and life. Many individual studies have been carried out in mice to uncover the viral factors responsible for the virulence of IAV infections. Nonetheless, a single study may not provide enough confident about virulence factors, hence...

Full description

Saved in:
Bibliographic Details
Main Authors: Ivan, Fransiskus Xaverius, Kwoh, Chee Keong
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
Subjects:
Online Access:https://hdl.handle.net/10356/146319
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-146319
record_format dspace
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Medicine
Influenza A Virus
Mouse Models
spellingShingle Science::Medicine
Influenza A Virus
Mouse Models
Ivan, Fransiskus Xaverius
Kwoh, Chee Keong
Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice
description Background: Influenza A virus (IAV) poses threats to human health and life. Many individual studies have been carried out in mice to uncover the viral factors responsible for the virulence of IAV infections. Nonetheless, a single study may not provide enough confident about virulence factors, hence combining several studies for a meta-analysis is desired to provide better views. For this, we documented more than 500 records of IAV infections in mice, whose viral proteins could be retrieved and the mouse lethal dose 50 or alternatively, weight loss and/or survival data, was/were available for virulence classification. Results: IAV virulence models were learned from various datasets containing aligned IAV proteins and the corresponding two virulence classes (avirulent and virulent) or three virulence classes (low, intermediate and high virulence). Three proven rule-based learning approaches, i.e., OneR, JRip and PART, and additionally random forest were used for modelling. PART models achieved the best performance, with moderate average model accuracies ranged from 65.0 to 84.4% and from 54.0 to 66.6% for the two-class and three-class problems, respectively. PART models were comparable to or even better than random forest models and should be preferred based on the Occam’s razor principle. Interestingly, the average accuracy of the models was improved when host information was taken into account. For model interpretation, we observed that although many sites in HA were highly correlated with virulence, PART models based on sites in PB2 could compete against and were often better than PART models based on sites in HA. Moreover, PART had a high preference to include sites in PB2 when models were learned from datasets containing the concatenated alignments of all IAV proteins. Several sites with a known contribution to virulence were found as the top protein sites, and site pairs that may synergistically influence virulence were also uncovered. Conclusion: Modelling IAV virulence is a challenging problem. Rule-based models generated using viral proteins are useful for its advantage in interpretation, but only achieve moderate performance. Development of more advanced approaches that learn models from features extracted from both viral and host proteins shall be considered for future works.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Ivan, Fransiskus Xaverius
Kwoh, Chee Keong
format Article
author Ivan, Fransiskus Xaverius
Kwoh, Chee Keong
author_sort Ivan, Fransiskus Xaverius
title Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice
title_short Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice
title_full Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice
title_fullStr Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice
title_full_unstemmed Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice
title_sort rule-based meta-analysis reveals the major role of pb2 in influencing influenza a virus virulence in mice
publishDate 2021
url https://hdl.handle.net/10356/146319
_version_ 1726885528268177408
spelling sg-ntu-dr.10356-1463192022-03-08T07:58:22Z Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice Ivan, Fransiskus Xaverius Kwoh, Chee Keong School of Computer Science and Engineering Biomedical Informatics Lab Science::Medicine Influenza A Virus Mouse Models Background: Influenza A virus (IAV) poses threats to human health and life. Many individual studies have been carried out in mice to uncover the viral factors responsible for the virulence of IAV infections. Nonetheless, a single study may not provide enough confident about virulence factors, hence combining several studies for a meta-analysis is desired to provide better views. For this, we documented more than 500 records of IAV infections in mice, whose viral proteins could be retrieved and the mouse lethal dose 50 or alternatively, weight loss and/or survival data, was/were available for virulence classification. Results: IAV virulence models were learned from various datasets containing aligned IAV proteins and the corresponding two virulence classes (avirulent and virulent) or three virulence classes (low, intermediate and high virulence). Three proven rule-based learning approaches, i.e., OneR, JRip and PART, and additionally random forest were used for modelling. PART models achieved the best performance, with moderate average model accuracies ranged from 65.0 to 84.4% and from 54.0 to 66.6% for the two-class and three-class problems, respectively. PART models were comparable to or even better than random forest models and should be preferred based on the Occam’s razor principle. Interestingly, the average accuracy of the models was improved when host information was taken into account. For model interpretation, we observed that although many sites in HA were highly correlated with virulence, PART models based on sites in PB2 could compete against and were often better than PART models based on sites in HA. Moreover, PART had a high preference to include sites in PB2 when models were learned from datasets containing the concatenated alignments of all IAV proteins. Several sites with a known contribution to virulence were found as the top protein sites, and site pairs that may synergistically influence virulence were also uncovered. Conclusion: Modelling IAV virulence is a challenging problem. Rule-based models generated using viral proteins are useful for its advantage in interpretation, but only achieve moderate performance. Development of more advanced approaches that learn models from features extracted from both viral and host proteins shall be considered for future works. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Published version Publication of this supplement was funded by AcRF Tier 2 Grant MOE2014-T2–2-023, Ministry of Education, Singapore and A*STAR-NTU-SUTD AI Partnership Grant RGANS1905. 2021-02-09T05:59:39Z 2021-02-09T05:59:39Z 2019 Journal Article Ivan, F. X., & Kwoh, C. K. (2019). Rule-based meta-analysis reveals the major role of PB2 in influencing influenza A virus virulence in mice. BMC Genomics, 20, 973-. doi:10.1186/s12864-019-6295-8 1471-2164 0000-0001-6491-6358 https://hdl.handle.net/10356/146319 10.1186/s12864-019-6295-8 31874643 2-s2.0-85077133278 20 en MOE2014-T2–2-023 RGANS1905 BMC Genomics 10.21979/N9/ILQBAB © 2019 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. application/pdf