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Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.
Medicine
Sep 27, 2024
Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predict...
Deep contrastive learning for predicting cancer prognosis using gene expression values.
Briefings in bioinformatics
Sep 23, 2024
Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor trans...
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.
Briefings in bioinformatics
Sep 23, 2024
The gut microbiota plays a vital role in human health, and significant effort has been made to predict human phenotypes, especially diseases, with the microbiota as a promising indicator or predictor with machine learning (ML) methods. However, the a...
Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study.
Briefings in bioinformatics
Sep 23, 2024
Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the IS functional outcome is crucial for precise risk stratification and effective therapeutic interventi...
Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis.
Briefings in bioinformatics
Sep 23, 2024
We sought to develop and validate a machine learning (ML) model for predicting multidimensional frailty based on clinical and laboratory data. Moreover, an explainable ML model utilizing SHapley Additive exPlanations (SHAP) was constructed. This stud...
DGCL: dual-graph neural networks contrastive learning for molecular property prediction.
Briefings in bioinformatics
Sep 23, 2024
In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraini...
A Machine Learning-Based Risk Score for Prediction of Infective Endocarditis Among Patients With Staphylococcus aureus Bacteremia-The SABIER Score.
The Journal of infectious diseases
Sep 23, 2024
BACKGROUND: Early risk assessment is needed to stratify Staphylococcus aureus infective endocarditis (SA-IE) risk among patients with S. aureus bacteremia (SAB) to guide clinical management. The objective of the current study was to develop a novel r...
[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection].
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
Sep 20, 2024
OBJECTIVE: We propose an autoencoder model based on a one-dimensional convolutional neural network (1DCNN) as the feature extraction network for efficient detection of epileptic EEG anomalies.
Prediction of Glioma enhancement pattern using a MRI radiomics-based model.
Medicine
Sep 6, 2024
Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients. This study aimed to develop a machine learning rad...
A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data.
Translational vision science & technology
Sep 3, 2024
PURPOSE: To propose and validate a meta-learning approach for detecting retinal vein occlusion (RVO) from multimodal images with only a few samples.
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