Latest AI and machine learning research in infectious disease for healthcare professionals.
Antimicrobial peptides (AMPs) have emerged as a promising alternative to conventional drugs due to their potential applications in combating multidrug-resistant pathogens. Various computational approaches have been developed for AMP prediction, ranging from shallow learning methods to advanced deep learning techniques. Additionally, the performance of shallow learning models based on self-learning...
Tuberculosis (TB) remains a significant global public health threat. Achieving the 2035 target for TB elimination requires interrupting its community transmission, which depends critically on enhancing case detection. Active screening for TB in community populations is crucial in order to address the current shortfall in case detection through passive, symptom-based approaches. However, obtaining ...
The rapid advancement of Large Language Models (LLMs) has significantly improved code generation, yet most models remain text-only, neglecting cruci...
AMRScan is a hybrid bioinformatics toolkit implemented in both R and [Nextflow](https://www.nextflow.io/) for the rapid and reproducible detection o...
Sequence data, such as DNA, RNA, and protein sequences, exhibit intricate, multi-scale structures that pose significant challenges for conventional ...
Medical Hyperspectral Imaging (MHSI) has emerged as a promising tool for enhanced disease diagnosis, particularly in computational pathology, offeri...
Most CYP51 inhibitors act competitively and are prone to resistance, whereas allosteric inhibitors hold promise but are difficult to develop. In this ...
Plain X-ray is one of the most common image modalities for clinical diagnosis (e.g. bone fracture, pneumonia, cancer screening, etc.). X-ray image s...
Non-autonomous differential equations are crucial for modeling systems influenced by external signals, yet fitting these models to data becomes part...
Uniform and excessive herbicide application in modern agriculture contributes to increased input costs, environmental pollution, and the emergence o...
The use of generative artificial intelligence (AI) models is becoming ubiquitous in many fields. Though progress continues to be made, general purpo...
Machine learning methods are increasingly applied to analyze health-related public discourse based on large-scale data, but questions remain regardi...
COVID-19 is a severe and acute viral disease that can cause symptoms consistent with pneumonia in which inflammation is caused in the alveolous regi...
Vaccine infodemics, driven by misinformation, disinformation, and inauthentic online behaviours, pose significant threats to global public health. T...
Bioactivity optimization is a crucial and technical task in the early stages of drug discovery, traditionally carried out through iterative substituen...
Vaccination plays a vital role in global public health, yet healthcare professionals often struggle to access immunization guidelines quickly and ef...
We present our solution for the Multi-Source COVID-19 Detection Challenge, which classifies chest CT scans from four distinct medical centers. To ad...
Accurate bacterial gene prediction is essential for understanding microbial functions and advancing biotechnology. Traditional methods based on sequen...
Image logs are crucial in capturing high-quality geological information about subsurface formations. Among the various geological features that can ...
HIV epidemiological data is increasingly complex, requiring advanced computation for accurate cluster detection and forecasting. We employed quantum...