AIMC Topic: Bayes Theorem

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Predicting prolonged dalbavancin exposure using machine learning: a validated strategy for individualized redosing.

Antimicrobial agents and chemotherapy
Dalbavancin is a long-acting lipoglycopeptide increasingly used off-label for complex Gram-positive infections requiring prolonged therapy. Its extended half-life enables simplified regimens, but interindividual pharmacokinetic variability and pathog...

Fingerprint-Based Machine Learning for SARS-CoV-2 and MERS-CoV Inhibition: Highlighting the Potential of Bayesian Neural Networks.

Journal of chemical information and modeling
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and Middle East respiratory syndrome coronavirus (MERS-CoV) are two important targets in current drug discovery, mainly due to the COVID-19 pandemic and the MERS-CoV outbreaks in recent yea...

Identifying influential determinants of women's empowerment in Bangladesh using machine learning algorithms.

PloS one
BACKGROUND AND OBJECTIVES: Women's empowerment is a vital issue in lower-middle-income developing countries like Bangladesh, where it plays a pivotal role in advancing development across the nation. Thus, this study aimed to identify the influential ...

Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy.

PloS one
Flight delays pose substantial operational and economic challenges for airlines, directly affecting scheduling efficiency, resource allocation, and passenger satisfaction. Accurate prediction of arrival delays is therefore critical for optimizing air...

Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.

Scientific reports
Deep learning (DL) algorithms have increasingly been applied to predict protein-ligand binding affinity, a critical step in drug design. Yet, many models still struggle to generalize to unseen data, and when coupled with the absence of confidence est...

BayesRVAT enhances rare-variant association testing through Bayesian aggregation of functional annotations.

Genome research
Gene-level rare variant association tests (RVATs) are essential for uncovering disease mechanisms and identifying therapeutic targets. Advances in sequence-based machine learning have generated diverse variant pathogenicity scores, creating opportuni...

Development and validation of a machine learning model for critical progression risk in pediatric severe community-acquired pneumonia.

Scientific reports
This study aimed to utilize various machine learning algorithms to develop a predictive model for the progression of severe community-acquired pneumonia (SCAP) in children to critical severe community-acquired pneumonia (cSCAP). Retrospective analysi...

Reconstructing music perception from brain activity using a prior guided diffusion model.

Scientific reports
Reconstructing music directly from brain activity provides insight into the neural representations underlying auditory processing and paves the way for future brain-computer interfaces. We introduce a fully data-driven pipeline that combines cross-su...

TLMACEA: design of a transfer learning model for correlative analysis of auscultation and clinical parameters via explainable AI-based recommender.

Biomedical physics & engineering express
Auscultations are commonly used to analyze lung conditions through signal processing and classification techniques. However, the efficiency of these models is often limited by factors like signal quality, sensor performance, and dataset size. Current...

Random rotational embedding Bayesian optimization for human-in-the-loop personalized music generation.

PloS one
Generative deep learning models, such as those used for music generation, can produce a wide variety of results based on perturbations of random points in their latent space. User preferences can be incorporated in the generative process by replacing...