Overview of PREDICATE developed to create viral biomass reactions and predict host-based antiviral targets using host-virus models. by Nantia Leonidou (14833068)

Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

“…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. NIMO provides a framework for integrating AI into robotic experimental systems that are controlled by other OS platforms based on both Python and non-Python languages. …”

Workflow overview to reconstruct integrated host-virus genome-scale models and detect promising compounds with an antiviral activity. by Nantia Leonidou (14833068)

Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence by Yao Xu (247302)

“…This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. …”

Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence by Yao Xu (247302)

“…This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. …”

Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg by Zhenze Zhang (22011422)

“…Background<p>Inflammatory bowel disease (IBD) poses significant mortality risks for critically ill patients requiring intensive care unit (ICU) admission, driven by complications such as malnutrition, thromboembolism, and multi-organ dysfunction. …”

Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx by Zhenze Zhang (22011422)

“…Background<p>Inflammatory bowel disease (IBD) poses significant mortality risks for critically ill patients requiring intensive care unit (ICU) admission, driven by complications such as malnutrition, thromboembolism, and multi-organ dysfunction. …”

Data Sheet 1_Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients—Machine learning models for clinical decision support.do... by Yuanguo Xiong (20135991)

“…Thirteen distinct machine learning (ML) algorithms were trained and benchmarked to construct an optimal risk stratification framework. …”

Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx by Fanhai Bu (22315168)

“…The least absolute shrinkage and selection operator (LASSO) regression selected predictors from clinical/neuroimaging/laboratory variables. Eight ML algorithms (including Logistic Regression, Random Forest, Extreme Gradient Boosting, Multilayer Perceptron, Support Vector Machine, Light Gradient Boosting Machine, Decision Tree, and K-Nearest Neighbors) were trained using 10-fold cross-validation and evaluated on test/external sets via the area under the curve (AUC), accuracy, precision, recall and F1-score. …”