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deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.
J Chem Inf Model
January 2025
School of Information and Artificial Intelligence, Anhui Provincial Engineering Research Center for Beidou Precision Agriculture Information, Key Laboratory of Agricultural Sensors for Ministry of Agriculture and Rural Affairs, Anhui Agricultural University, Hefei, Anhui 230036, China.
Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years.
View Article and Find Full Text PDFMachine-learning-based cost prediction models for inpatients with mental disorders in China.
BMC Psychiatry
January 2025
Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Background: Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.
View Article and Find Full Text PDFMachine learning-based prediction model integrating ultrasound scores and clinical features for the progression to rheumatoid arthritis in patients with undifferentiated arthritis.
Clin Rheumatol
January 2025
Department of Interventional Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, People's Republic of China.
Objectives: Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches combining clinical assessments and ultrasonography (US) often lack accuracy due to the complex interaction of clinical variables, and routine extensive US is impractical. Machine learning (ML) models, particularly those integrating the 18-joint ultrasound scoring system (US18), have shown potential to address these issues but remain underexplored.
View Article and Find Full Text PDFA scaling law to model the effectiveness of identification techniques.
Nat Commun
January 2025
Data Science Institute, Imperial College London, London, UK.
AI techniques are increasingly being used to identify individuals both offline and online. However, quantifying their effectiveness at scale and, by extension, the risks they pose remains a significant challenge. Here, we propose a two-parameter Bayesian model for exact matching techniques and derive an analytical expression for correctness (κ), the fraction of people accurately identified in a population.
View Article and Find Full Text PDFPerformance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting.
Sleep Health
January 2025
Department of Human and Development and Family Studies, Pennsylvania State University, University Park, Pennsylvania, USA.
Goal And Aims: One challenge using wearable sensors is nonwear time. Without a nonwear (e.g.
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