AIMC Topic: Machine Learning

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VacQuant: a tool to quantify neurodegeneration and associated vacuolation in brain tissue.

Fly
Neurodegenerative diseases are devastating conditions characterized by progressive cognitive decline with few available treatments. Neurodegeneration can be quantified in vertebrate and invertebrate models of disease by analysis of vacuolation - the ...

Molecular Property Prediction Based on Improved Graph Transformer Network and Multitask Joint Learning Strategy.

Journal of chemical information and modeling
Molecular property prediction is of great significance in drug design and materials science. However, due to the complexity and diversity of molecular structures, existing methods often struggle to simultaneously capture both the local chemical envir...

Exploring nationwide patterns of sleep problems from late adolescence to adulthood using machine learning.

Science advances
Sleep problems among young adults pose a major public health challenge. Leveraging nationwide health surveys and registers from Denmark, we investigated patterns of sleep problems from late adolescence to adulthood and explored early life-course dete...

Integrative single-cell and machine learning approach to characterize immunogenic cell death and tumor microenvironment in LUAD.

Journal of translational medicine
BACKGROUND: Immunogenic cell death (ICD) triggers antitumor immune responses and plays a critical role in shaping the tumor microenvironment (TME). However, its specific contribution to lung adenocarcinoma (LUAD) progression and immunotherapy respons...

Machine learning model to predict mortality in patients with skin and soft tissue infection in emergency department.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: Accurately predicting mortality in patients with skin and soft-tissue infections (SSTIs) remains challenging. Machine learning models offer rapid processing, algorithmic impartiality, and strong predictive accuracy, which may improve earl...

Revolutionizing Wilson disease prognosis: a machine learning approach to predict acute-on-chronic liver failure.

Journal of translational medicine
BACKGROUND AND OBJECTIVES: Wilson disease (WD), an inherited copper metabolism disorder, is a cause of acute-on-chronic liver failure (ACLF), posing life-threatening risks due to rapid progression. This study aimed to develop a machine learning (ML)-...

Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning.

Journal of translational medicine
BACKGROUND: Infertility affects approximately 15% of couples globally, with assisted reproductive technologies (ARTs) becoming the primary interventions. Despite the growing use of ARTs, success rates have plateaued at around 30%, highlighting the ne...

Temporal trends and machine learning prediction of depressive symptoms among Chinese middle-aged and elderly individuals: a national cohort study.

BMC public health
BACKGROUND: The prevalence of depression symptoms, the third most disabling disease worldwide, is as high as 11.5%-21.1% in China's middle-aged and elderly population and increases significantly with age. It is crucial to identify high-risk groups ef...

Biomarker genes for model-based prediction of drought-stress perception levels in rice.

BMC plant biology
BACKGROUND: Drought is a global challenge that severely restricts crop yields and threatens food security. Plants respond to drought stress by modulating gene expression before visible phenotypic changes occur. However, most studies of drought resist...

Development of a machine learning-based depression risk identification tool for older adults with asthma.

BMC psychiatry
BACKGROUND: Asthma is a chronic inflammatory disorder that adversely affects the quality of life, particularly in older adults. The coexistence of depression in asthma patients complicates their management and exacerbates health outcomes. This study ...