AIMC Topic: Machine Learning

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GlyTrait: A Versatile Bioinformatics Tool for Glycomics Analysis.

Journal of proteome research
We developed GlyTrait, a Python-based framework designed to enhance Glycomics analysis through the innovative calculation and interpretation of derived traits from -glycome data. Glycomics research often grapples with the interpretability and biologi...

Predicting COVID-19 patient recovery or mortality using deep neural decision tree and forest.

BMC research notes
OBJECTIVE: Identifying patients at high risk of mortality is crucial for emergency physicians to allocate hospital resources effectively, particularly in regions with limited medical services. This need becomes even more pressing during global health...

MX1 is a novel crucial prognostic and therapeutic target inducing chemoresistance in right-sided colon cancer: insights from machine learning-based multi-omics analysis.

Human genomics
BACKGROUND: Recent studies have increasingly emphasized the poorer survival outcomes and reduced treatment responses associated with right-sided colon cancer (RCC). However, the underlying molecular mechanisms remain poorly understood. This study aim...

Identification of coagulation-related biomarkers in osteoarthritis and immune infiltration analysis based on bioinformatics.

Hereditas
BACKGROUND: Osteoarthritis (OA) is a common degenerative disorder characterized primarily by articular cartilage degradation and chronic inflammation. Although direct evidence elucidating the specific mechanisms underlying the coagulation-immune axis...

Can machine learning improve on the early prediction of upper limb recovery after stroke?

Journal of neuroengineering and rehabilitation
BACKGROUND: Early prediction of upper limb recovery is important to optimise rehabilitation and inform patients but remains challenging due to inter-individual variability. This study aims to (1) develop and validate a machine learning model to predi...

Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...

The prognostic value of POD24 for multiple myeloma: a comprehensive analysis based on traditional statistics and machine learning.

BMC cancer
BACKGROUND: In multiple myeloma, progression within 24 months (POD24) is a strong adverse prognostic factor. However, its impact on overall survival (OS) remains underexplored through machine learning.

The effect of kinesiophobia and successful aging on quality of life in older adults: machine learning approach.

BMC geriatrics
BACKGROUND: Kinesiophobia and successful aging are key factors affecting quality of life in older adults; kinesiophobia, the fear of movement, can lead to reduced physical activity, while successful aging promotes overall well-being.

PND.heter.cluster: An R package for estimating cluster-specific treatment effects in partially nested designs.

Behavior research methods
Partially nested designs-where clustering occurs in some but not all study arms-are common in psychological and behavioral research. In these designs, clustering often arises in the treatment arm due to the treatment delivery, such as individuals clu...

Applying machine learning to predict quality ANC determinants in Bangladesh: a BDHS-2022 cross-sectional study.

Scientific reports
Quality antenatal care (ANC) is critical for maternal and neonatal health. Despite improvements in healthcare, disparities in ANC access and quality persist, particularly in underserved areas of Bangladesh. This study aimed to identify the key determ...