Journal of Robotics, Networking and Artificial Life

Volume 2, Issue 2, September 2015, Pages 115 - 119

Analysis of Genetic Disease Haemophilia A by Using Machine Learning

Authors
Kenji Aoki, Makoto Sakamoto, Hiroshi Furutani
Corresponding Author
Kenji Aoki
Available Online 1 September 2015.
DOI
https://doi.org/10.2991/jrnal.2015.2.2.11How to use a DOI?
Keywords
Haemophilia A, Machine Learning, Factor VIII, Amino-acid, Mutation
Abstract

Haemophilia A is a genetic disease resulting from deficiency of factor VIII. The database of mutations causing haemophilia A has been developed by the world wide collaboration. In this study, we examined the relation between activity of factor VIII and the missense mutation by using machine learning. As parameters, we used four physical-chemical parameters of amino acids. We predicted the severity of haemophilia A by using machine learning in factor VIII. As the result, logistic regression is not better than other methods in the prediction of haemophilia A severity. The result of the prediction improved in order to SVM, bagging, boosting and random forest. These results suggested that we can predict the haemophilia A severity by using these methods, and random forest was the best method in these five methods to predict the haemophilia A severity.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
2 - 2
Pages
115 - 119
Publication Date
2015/09/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.2015.2.2.11How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Kenji Aoki
AU  - Makoto Sakamoto
AU  - Hiroshi Furutani
PY  - 2015
DA  - 2015/09/01
TI  - Analysis of Genetic Disease Haemophilia A by Using Machine Learning
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 115
EP  - 119
VL  - 2
IS  - 2
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2015.2.2.11
DO  - https://doi.org/10.2991/jrnal.2015.2.2.11
ID  - Aoki2015
ER  -