The Use of Machine Learning and Predictive Modelling Methods in the Identification of Hosts for Viral Infections: Scoping Review Protocol

Alberts, Famke
Keay, Sheila
Poljak, Zvonimir
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Background: Advanced in-silico predictive modelling techniques combining methods of machine learning and bioinformatics have been applied to predict the reservoir of a virus and all hosts that exist within that reservoir. However, a systematic compilation of this body of research does not exist. Objectives: This protocol describes the methods that will be used to conduct a formal scoping review of current literature to address the question: “What machine learning methods have been applied to influenza virus and coronavirus genome data for identification of the potential reservoirs?”. Eligibility Criteria: Eligible studies will be primary research studies, in English, from any geographic location, published between 2000-2021, conducted using machine learning techniques within the context of understanding or predicting influenza virus or coronavirus host-range or transmission. Sources of Evidence: The following databases will be searched: PubMed, MEDLINE, ProQuest, Engineering Village, and Web of Science from 2000-2021. Charting Methods: We will extract data on general and specific study characteristics, identifying the steps taken in data gathering, processing, and analysis.

machine learning, coronavirus, genome data, influenza virus, protocol