Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases

Abstract

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

Editorial: New advances in the biology and pathogenesis of free-living amoebae

“…Furthermore, we expect the emergence of the use of artificial intelligence (AI) in amoebae research. Indeed, the development and application of machine learning (ML) in the field of infectious diseases have gained massive attention in recent years, including other protozoans such as Plasmodium and Trypanosoma (Hu et al, 2022 ). We caught a glimpse of machine learning with Dr. Rice's (unpublished data) and Dr. Debnath's (Shing et al, 2022 ) novel Acanthamoeba cysticidal methodologies at the recent FLAM 2023 conference.…”

Harnessing the Power of Artificial Intelligence for Enhanced Point-of-Care Quality Control in Healthcare

“…For example, they existence of an ML model for diagnosing infectious diseases at POC, achieving an accuracy of 95%, thus outperforming human experts. This demonstrates the ability of AI and ML algorithms to augment human expertise, resulting in improved diagnostic accuracy at the point of care [11,12].…”

Applied machine learning techniques for chronic disease treatment default prediction and its potential benefits for patient outcome: A case series study approach

“…Applied learning technique assessment study for the detection and management of infectious diseases caused by fatal or life-threatening causative agents capable of infecting both animals and humans provided a comprehensive review of machine learning application use in pathogen detection, public health surveillance, host-parasite interaction, drug discovery, omics and vaccine discovery. Emphasis on the use of evaluation metrics (precision and recall) in classification tasks [17] as important performance measure is made in respect of modeling techniques such as support vector machine, random forest and neural networks. Clinical impact is assessed to be the inclusion and emphasis on precision and recall as important evaluation metrics to identify parasitic cells as infected or not infected (positive or negative).…”