Applying one-class learning algorithms to predict phage-bacteria interactions

López, Juan Fernando (Universidad Autónoma de Occidente,Department of Electrical and Automation,Cali,Colombia) ; López Sotelo, Jesús Alfonso (Universidad Autónoma de Occidente,Department of Electrical and Automation,Cali,Colombia) ; Leite, Diogo (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Peña-Reyes, Carlos (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland)

The need to predict phage-bacteria interactions is a nowadays concern to overcome bacterial resistance issue; public genome databases contain highly imbalanced datasets which have hindered this task. Throughout this paper we will investigate, implement and evaluate One-Class Learning algorithms in order to predict phage-bacteria interactions using only positive samples. We will use the programming language Python aided by Scikit-Learn, Tensorflow and keras to develop the machine learning models and test them with real phage-bacteria interactions datasets. We trained the models using cross validation technique generating a gridsearch with all the datasets to find several combinations of hyperparameters available. Furthermore, we optimized those hyperparameters by using Pareto fronts based on seven different performance metrics, improving the efficiency of each algorithm for a given dataset. To refine each algorithm's performance separately we used the ensemble learning technique with an odd number of algorithms by simple voting. Finally, we managed to achieve an overall performance of 80% in predicting phage-bacteria interactions trained only with positive classes, this percentage in practice means that when a patient has an infection resistant to antibiotics, we have 80% of saving the life rather than maybe a 0% while finding the correct phage for the pathogenic host.


Keywords:
Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Publisher:
Guayaquil, Ecuador, 11-15 November 2019
Date:
2019-11
Guayaquil, Ecuador
11-15 November 2019
Pagination:
6 p.
Published in:
Proceedings of 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 11-15 November 2019, Guayaquil, Ecuador
DOI:
ISBN:
978-1-7281-5666-8
Appears in Collection:

Note: The status of this file is: restricted


 Record created 2020-05-19, last modified 2020-05-22

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