About this Research Topic
Infections caused by pathogenic microorganisms, including bacteria, viruses, fungi, and other eukaryotic microbes, seriously threaten human health. Traditional research methods and laboratory techniques have many limitations and focus more on the identification and classification of pathogenic microorganisms. In recent years, technologies such as whole genome sequencing and advanced bioinformatics analysis have promoted the research of pathogenic microorganisms. However, with the interplay of multiple factors like global climate change, ecological and environmental changes, urbanization, social behavior, and lifestyle changes, pathogenic microorganisms' transmission patterns and impact scope are gradually changing. There is an urgent need for multidimensional technological approaches to achieve epidemiological monitoring and evolutionary direction prediction of pathogenic microorganisms. Additionally, more robust data processing and analysis capabilities are required for rapid identification and diagnosis, monitoring of drug resistance, development of antimicrobial drugs and vaccines, and optimization of treatment plans. Therefore, Artificial Intelligence (AI) has entered our field of vision.
In the field of pathogenic microorganisms, AI has shown tremendous potential. In epidemiological research, AI technology can quickly and automatically collect, integrate and analyze the epidemic data of infectious diseases from different regions, so as to predict the trend and scope of disease transmission, and track the source of infection. In the process of diagnosis and treatment of infectious diseases, machine learning can not only analyze the microscopic images of pathogens, but also analyze the genome sequences of multiple pathogens in a short time, and predict their sensitivity or resistance to specific antibiotics, greatly improving the efficiency and accuracy of diagnosis and treatment of infectious diseases. In drug or vaccine development, researchers can use AI models to predict efficient antigens for diseases such as HIV and influenza, and thus design more effective vaccine candidates. AI models can also analyze the interactions between drugs, pathogens, and patients, in order to design the optimal dosing regimen for each patient. In a word, AI can help human beings better deal with infectious diseases.
We welcome original reviews, articles, and other contributions in related fields, which mainly include the following aspects:
(1) The application of AI in the differential diagnosis of pathogenic microorganisms
(2) The application of AI in the formulation of anti-infection treatment plans
(3) The application of AI in monitoring and predicting the prevalence of pathogenic microorganisms
(4) Application of AI in the prediction and prevention of infectious diseases caused by pathogenic microorganisms
(5) The application of AI in the research and development of anti-infective drugs and vaccines
In the field of pathogenic microorganisms, AI has shown tremendous potential. In epidemiological research, AI technology can quickly and automatically collect, integrate and analyze the epidemic data of infectious diseases from different regions, so as to predict the trend and scope of disease transmission, and track the source of infection. In the process of diagnosis and treatment of infectious diseases, machine learning can not only analyze the microscopic images of pathogens, but also analyze the genome sequences of multiple pathogens in a short time, and predict their sensitivity or resistance to specific antibiotics, greatly improving the efficiency and accuracy of diagnosis and treatment of infectious diseases. In drug or vaccine development, researchers can use AI models to predict efficient antigens for diseases such as HIV and influenza, and thus design more effective vaccine candidates. AI models can also analyze the interactions between drugs, pathogens, and patients, in order to design the optimal dosing regimen for each patient. In a word, AI can help human beings better deal with infectious diseases.
We welcome original reviews, articles, and other contributions in related fields, which mainly include the following aspects:
(1) The application of AI in the differential diagnosis of pathogenic microorganisms
(2) The application of AI in the formulation of anti-infection treatment plans
(3) The application of AI in monitoring and predicting the prevalence of pathogenic microorganisms
(4) Application of AI in the prediction and prevention of infectious diseases caused by pathogenic microorganisms
(5) The application of AI in the research and development of anti-infective drugs and vaccines
Keywords: pathogenic microorganisms, bacteria, viruses, fungi, parasites, rickettsia, mycoplasma, artificial intelligence (AI), machine learning (ML), diagnosis, treatment, prevention, epidemiology, antibiotics, antiviral drugs, antiparasitic drugs, vaccines
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
