Assessment of vector-host-pathogen relationships using data mining and machine learning

Computational and Structural Biotechnology Journal
Diing D M AganyEtienne Z Gnimpieba

Abstract

Infectious diseases, including vector-borne diseases transmitted by arthropods, are a leading cause of morbidity and mortality worldwide. In the era of big data, addressing broad-scale, fundamental questions regarding the complex dynamics of these diseases will increasingly require the integration of diverse datasets to produce new biological knowledge. This review provides a current snapshot of the systematic assessment of the relationships between microbial pathogens, arthropod vectors and mammalian hosts using data mining and machine learning. We employ PRISMA to identify 32 key papers relevant to this topic. Our analysis shows an increasing use of data mining and machine learning tasks and techniques, including prediction, classification, clustering, association rules mining, and deep learning, over the last decade. However, it also reveals a number of critical challenges in applying these to the study of vector-host-pathogen interactions at various systems biology levels. Here, relevant studies, current limitations and future directions are discussed. Furthermore, the quality of data in relevant papers was assessed using the FAIR (Findable, Accessible, Interoperable, Reusable) compliance criteria to evaluate and encourage ...Continue Reading

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Citations

Jun 2, 2021·Journal of Molecular Evolution·Keren Halabi, Itay Mayrose
Sep 16, 2021·Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine·Zhiyi ChenZhenyu Liu
May 6, 2021·Current Medicinal Chemistry·Olga Tarasova, Vladimir Poroikov
Nov 2, 2021·Frontiers in Medicine·Haifeng LuZhongwen Wu

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Methods Mentioned

BETA
scanning-electron microscopy
ELISA
PCR

Software Mentioned

GBK
SVM
Gnimpieba
SGE
DM
Google Forms
LB
SMOPredT4SE
EpiExploreR
Ensemble

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