GutBugDB: A Web Resource to Predict the Human Gut Microbiome-Mediated Biotransformation of Biotic and Xenobiotic Molecules

Chemistry, Medicinal Chemistry, Computational Chemistry, Information Science, Artificial Intelligence, Medicine, Gastroenterology, Pharmacy, Life Sciences, Biochemistry, Microbiology,
biotransformation   database   human gut microbiome   machine learning   xenobiotic metabolism  

In recent years, the significant role of the human gut microbiota (HGM) in the metabolism of drugs and nutrients has gradually been recognized. The gut microbiota not only affects the bioavailability of orally administered drugs but also participates in the biotransformation of drugs and bioactive molecules through its metabolic enzymes, thereby in...

Evaluating Generalizability of Oncology Trial Results to Real-World Patients Using Machine Learning-Based Trial Emulations

Information Science, Artificial Intelligence, Medicine, Oncology,
oncology   randomized controlled trials   machine learning   generalizability   real-world data   prognostic models   survival analysis  

Evaluation of the Generalizability of Oncology Trial Results Using Machine Learning-Based Trial Emulations Academic Background Randomized Controlled Trials (RCTs) are the gold standard for evaluating the efficacy of anti-cancer drugs, but their results often cannot be directly generalized to real-world oncology patients. RCTs typically employ stric...

Multi-scale and Multi-level Feature Assessment Framework for Classification of Parkinson’s Disease State from Short-term Motor Tasks

Information Science, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
Parkinson’s disease   feature assessment   machine learning   wearable devices   short-term motor tasks  

Academic Background Parkinson’s Disease (PD) is the second most common chronic neurodegenerative disease, primarily affecting individuals aged 65 and above. With the global population aging, the prevalence of Parkinson’s disease is projected to increase from 7 million in 2015 to 13 million by 2040. Currently, the diagnosis of Parkinson’s disease ma...

Heart Rate and Body Temperature Relationship in Children Admitted to PICU - A Machine Learning Approach

Information Science, Artificial Intelligence, Medicine, Pediatrics, Life Sciences, Bioengineering,
heart rate   body temperature   pediatric intensive care unit   machine learning   quantile regression  

Machine Learning Study on the Relationship Between Heart Rate and Body Temperature in Pediatric Intensive Care Units Academic Background In the pediatric intensive care unit (PICU), heart rate (HR) and body temperature (BT) are crucial clinical indicators that reflect a patient’s physiological status. Although the relationship between HR and BT has...

Analyzing the Visual Road Scene for Driver Stress Estimation

Engineering, Transportation Engineering, Information Science, Computer Science, Artificial Intelligence,
driver stress estimation   road scene analysis   driver assistance technologies   machine learning   visual analysis  

Research on Driver Stress Estimation Based on Visual Road Scenes Academic Background Driver stress is a significant factor contributing to traffic accidents, injuries, and fatalities. Studies show that 94% of traffic accidents are related to drivers, with inattention, internal and external distractions, and improper speed control all closely linked...

Multimodal Learning for Mapping Genotype–Phenotype Dynamics

Information Science, Computer Science, Artificial Intelligence, Medicine, Life Sciences, Cell Biology, Biochemistry, Bioengineering, Genetics,
multimodal learning   genotype-phenotype relationship   single-cell RNA sequencing   machine learning   cellular heterogeneity  

Multimodal Learning Reveals Genotype–Phenotype Dynamics Background The complex relationship between genotype and phenotype has long been a central question in biology. Genotype refers to the genetic information of an organism, while phenotype is the manifestation of this genetic information in a specific environment. Although Wilhelm Johannsen intr...

Approaching Coupled-Cluster Accuracy for Molecular Electronic Structures with Multi-Task Learning

Chemistry, Computational Chemistry, Materials Science, Information Science, Artificial Intelligence,
multi-task learning   coupled-cluster methods   molecular electronic structure   quantum chemistry   machine learning  

Machine Learning Boosts Quantum Chemistry: Predicting Molecular Electronic Structures Approaching Coupled-Cluster Accuracy Academic Background In physics, chemistry, and materials science, computational methods are key tools for uncovering the mechanisms behind diverse physical phenomena and accelerating materials design. However, quantum chemistry...

Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling

Chemistry, Chemical Engineering, Information Science, Automation, Artificial Intelligence,
preference prediction   multiobjective evolutionary optimization   gasoline blending   scheduling optimization   machine learning   cost efficiency   industrial applications  

Preference Prediction-Based Evolutionary Multiobjective Optimization for Gasoline Blending Scheduling Background Introduction With the continuous evolution of the global energy market, gasoline production and blending processes face increasing challenges. As a key product of the oil industry, gasoline’s blending and scheduling processes directly af...

Multilevel Ensemble Membership Inference Attack

Information Science, Electronic Information, Computer Science, Artificial Intelligence,
machine learning   privacy attack   membership inference   data leakage   AI security  

In-depth Analysis of the Research Paper: MEMIA: Multilevel Ensemble Membership Inference Attack Introduction to the Research Background With the rapid development of digital technologies, artificial intelligence (AI) and machine learning (ML) have deeply permeated multiple domains, including healthcare, finance, retail, education, and social media....

Partial Multi-Label Learning via Label-Specific Feature Corrections

Information Science, Computer Science, Artificial Intelligence,
partial multi-label learning   label-specific features   feature corrections   multi-label learning   machine learning   meta-learning   weak supervision  

Frontier Research in Partial Multi-Label Learning: A New Method Based on Label-Specific Feature Corrections In recent years, partial multi-label learning (PML) has become a hot research topic in the field of machine learning. With the rise of crowdsourcing platforms, the cost of data annotation has dropped significantly, but the quality of annotati...