AIMC Topic: Machine Learning

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High-throughput screening accelerated by machine learning for the morphology of silica nanoparticles with high cell permeability.

Nanoscale
In recent years, silica nanoparticles have garnered tremendous attention as drug-delivery carriers. However, the cell permeability of nanoparticles remains a major obstacle that limits the drug-delivery efficiency of drug carriers. It is a common pra...

The additive effect of the estimated glucose disposal rate and a body shape index on cardiovascular disease: A cross-sectional study.

PloS one
BACKGROUND: The glucose disposal rate (eGDR) and a body shape index (ABSI) are predictors strongly associated with cardiovascular disease (CVD) and outcomes. However, whether they have additive effects on CVD risk is unknown. This study aimed to inve...

A hybrid approach for forecasting peak expiratory flow rate in asthma patients using combined linear regression and random forest model.

PloS one
Asthma is a frequent and long-lasting disorder associated with airway inflammation. The disease severity may lead to serious health concerns and even mortality. In this work, we propose a novel hybrid approach using machine learning models and simila...

Advancing fall risk prediction in older adults with cognitive frailty: A machine learning approach using 2-year clinical data.

PloS one
Falls are a critical concern in older adults with cognitive frailty (CF). However, previous studies have not fully examined whether machine learning models can predict falls in older individuals with CF. The 2-year longitudinal data set from the Kore...

Hybrid machine learning approach for prediction and design optimization of marshall stability in graphene oxide-modified asphalt concrete.

Environmental research
Marshall Stability (MS) is a key, yet costly and time-consuming, metric for designing asphalt concrete (AC) in general, and Graphene Oxide (GO)-modified AC in particular. To address this, this study introduces a novel hybrid machine learning framewor...

Predicting hydrocarbon presence in marine cold seep sediments using machine learning models trained with benthic bacterial 16S rRNA taxonomy.

Microbiology spectrum
UNLABELLED: Hydrocarbon seepage in marine sediments exerts selective pressure on benthic microbiomes. Accordingly, microbial community composition in these sediments can reflect the presence of hydrocarbons, with specific groups being more prolific i...

Quantum Transport Informed Machine Learning Mapping of Current-Voltage Characteristics for Precision Deoxyribonucleic Acid Sequencing.

The journal of physical chemistry. A
Quantum tunneling-based DNA sequencing promises to transform genomic analysis by improving long-read accuracy and enabling high-throughput sequencing, particularly the precise measurement of electrical conductance and tunneling current signatures ass...

AlzFormer: Multi-modal framework for Alzheimer's classification using MRI and graph-embedded demographics guided by adaptive attention gating.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Alzheimer's disease (AD) is the most common neurodegenerative progressive disorder and the fifth-leading cause of death in older people. The detection of AD is a very challenging task for clinicians and radiologists due to the complex nature of this ...

Protein functional site annotation using local structure embeddings.

Proceedings of the National Academy of Sciences of the United States of America
The rapid expansion of protein sequence and structure databases has resulted in a significant number of proteins with ambiguous or unknown function. While advances in machine learning techniques hold great potential to fill this annotation gap, curre...