AIMC Topic: Machine Learning

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Detection of cortical arousals in sleep using multimodal wearable sensors and machine learning.

Scientific reports
Cortical arousals are brief brain activations that disrupt sleep continuity and contribute to cardiovascular, cognitive, and behavioral impairments. Although polysomnography is the gold standard for arousal detection, its cost and complexity limit us...

Candidate genes for anthracnose resistance in Senegalese sorghum: a machine learning-based exploration.

Functional & integrative genomics
Anthracnose, caused by the hemibiotrophic fungal pathogen Colletotrichum sublineola, is a significant constraint to sorghum production worldwide. Developing resistant cultivars is the most sustainable control strategy, but it requires constant additi...

Geospatial modeling and forecasting of urban land use change using Google Earth Engine and machine learning.

PloS one
Urban expansion and Land Use Land Cover (LULC) change pose critical challenges for sustainable urban planning and risks to food security. This study analyzes multi-temporal Landsat imagery from 1990 to 2020 for five major cities, Islamabad, Karachi, ...

Revealing the anti-tumor mechanisms of aromatic oil from Amomum villosum through integrated network pharmacology, bioinformatics, machine learning, single-cell sequencing, and cell experiments.

Biochemical and biophysical research communications
The dry fruits of Amomum villosum (Av) are a traditional Chinese medicine used for gastrointestinal disease. Aromatic oil has been reported to have anti-tumor properties. However, its therapeutic potential and molecular mechanisms remain unclear. Int...

Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.

Journal of chemical information and modeling
Enzyme kinetic parameters, including , , /, and , are critical for guiding applications in enzyme engineering, metabolic modeling, and synthetic biology by providing quantitative information on enzyme activity under various conditions. Experimental d...

A Framework for Identifying Serum Exosomal Lipid Biomarkers in Alzheimer's Disease.

ACS chemical neuroscience
The escalating global burden of Alzheimer's disease (AD), projected to reach $16.9 trillion by 2050 with disproportionate impacts on low- and middle-income countries and racial minorities, underscores an urgent need for accessible early detection too...

TC check: a web app for thyroid cancer recurrence prediction using explainable machine learning.

Journal of cancer research and clinical oncology
BACKGROUND: Thyroid cancer (TC) is one of the most prevalent endocrine malignancies, and its recurrence presents a major clinical challenge that can adversely affect patient prognosis and treatment outcomes. Despite the progress in diagnostic methods...

[Formula: see text] : explainable attentive transformers for identifying the factors influencing dental visits to enhance dental data completeness.

BMC oral health
BACKGROUND: Access to routine dental care is a cornerstone of preventive healthcare. Regular dental check-ups, which include professional cleanings, examinations, and preventive treatments, play a crucial role in preventing advanced dental diseases s...

Site-specific pain dynamics: associations between accelerometer-measured physical activity patterns and pain in older adults.

The journal of headache and pain
BACKGROUND: Physical activity (PA) has emerged as a promising non-pharmacological intervention for pain management, the relationship between objectively measured PA patterns and multi-site pain remains poorly understood. This exploratory study invest...