Artificial Intelligence In Pathogenic Microorganism Research
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Artificial Intelligence in Pathogenic Microorganism Research
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
Machine learning and deep learning applications in pathogenic microbiome research
The pathogenic microbiome is the community of microorganisms that live in humans or animals and cause disease. These microorganisms include bacteria, viruses, fungi, protozoa, etc. They usually live in the host's skin, mouth, intestinal tract, genitourinary tract, etc. Normally, there is a state of equilibrium between the host and these microorganisms, but when this equilibrium is disturbed, these microorganisms become the pathogenic microbiome and cause disease. To advance the field of microbiome research, artificial intelligence methods, especially machine learning and deep learning, have recently been used as important tools due to their powerful predictive and informative potential. Classical machine learning algorithms such as linear regression, random forests, support vector machines, etc. perform well on microbiome data. However, as algorithms have been iteratively updated, these models have long been relegated to the basics. Linear regression models are now more often used to interpret these models more intuitively by using the output of other models as input. Deep learning is a branch of machine learning that involves a large number of neural network structures. Deep learning relies on neurons whose role is to transform the input and propagate it forward to the next neuron. Deep learning is currently being used with spectacular success in areas such as image recognition, text processing and automatic translation. As a result, a growing number of researchers are attempting to apply deep learning techniques to biomedical data analysis. Although there are still challenges in practical applications, such as model interpretability, data availability, model evaluation and selection, machine learning and deep learning are very promising tools in pathogenic microbiome research. This Research Topic, therefore, aims to contribute to the latest advances in machine learning, especially deep learning, and to explore new applications of related techniques in pathogenic microbiome research, trying to find relationships between microbiome and human health as well as the environment by studying high-throughput sequencing data of microbes, laying the foundation for further applications for subsequent treatment or forensic identification. We welcome submissions of Original Research, Brief Research Report, Review, Mini-Review, Methods, Perspective and Opinion articles that focus on, but are not limited to, the utilization of machine learning and deep learning to address the following subtopics. 1. Classification and identification of pathogenic microorganisms 2. Virulence prediction of pathogenic microorganisms 3. Antimicrobial resistance prediction of pathogenic microorganisms 4. Population structure and epidemiology of pathogenic microorganisms-related diseases 5. Immunological studies of pathogenic microorganisms 6. Drug target prediction for pathogenic microorganisms-related diseases
Artificial Intelligence Applications in Chronic Ocular Diseases
With the acceleration of urbanization and aging processes, chronic ocular diseases have become a critical threat to the vision health of the global population . Chronic ocular diseases include a series of disorders and conditions that involve long-term defects in both anterior and posterior segments of the eye, such as cataract, glaucoma, keratoconus (KC), diabetic retinopathy (DR), and age-related macular degeneration (AMD). However, it is still challenging to understand the mechanisms of these diseases and to discover new and reliable biomarkers to identify the diseases and their severities. Moreover, conventional methods in clinical societies are not as effective or efficient as expected, especially in the era of artificial intelligence.