AIMC Topic: Drug Resistance
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms.
Malaria journal
Jul 24, 2025
BACKGROUND: Machine learning algorithms have been used to predict malaria risk and severity, identify immunity biomarkers for malaria vaccine candidates, and determine molecular biomarkers of antimalarial drug resistance. Developing these prediction ...
Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model.
PloS one
Jul 9, 2025
Intravenous immunoglobulin (IVIG) has been established as the first-line therapy for Kawasaki disease (KD). However, approximately 10%-20% of pediatric patients exhibit IVIG resistance. Current machine learning (ML) models demonstrate suboptimal pred...
Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease.
Italian journal of pediatrics
Jun 9, 2025
BACKGROUND: Kawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10-20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. E...
Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.
Paediatric drugs
Apr 3, 2025
Kawasaki disease (KD) is a common pediatric vasculitis, with coronary artery lesions (CALs) representing its most severe complication. Early identification of high-risk patients, including those with disease resistant to first-line treatments, is ess...
Predictive modeling of consecutive intravenous immunoglobulin treatment resistance in Kawasaki disease: A nationwide study.
Scientific reports
Jan 6, 2025
Kawasaki disease (KD) is a leading cause of acquired heart disease in children, often resulting in coronary artery complications such as dilation, aneurysms, and stenosis. While intravenous immunoglobulin (IVIG) is effective in reducing immunologic i...
Predicting laboratory aspirin resistance in Chinese stroke patients using machine learning models by GP1BA polymorphism.
Pharmacogenomics
Oct 23, 2024
This study aims to use machine learning model to predict laboratory aspirin resistance (AR) in Chinese stroke patients by incorporating patient characteristics and single nucleotide polymorphisms of and . 2405 patients were analyzed to measure the M...
Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.
BMC neurology
Aug 26, 2024
BACKGROUND: The objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plas...
Development of an immunoinflammatory indicator-related dynamic nomogram based on machine learning for the prediction of intravenous immunoglobulin-resistant Kawasaki disease patients.
International immunopharmacology
May 3, 2024
BACKGROUND: Approximately 10-20% of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance, placing them at higher risk of developing coronary artery aneurysms. Therefore, we aimed to construct an IVIG resistance pred...
MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites.
Genome biology
Oct 16, 2023
Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSE...
PTPRC promoted CD8+ T cell mediated tumor immunity and drug sensitivity in breast cancer: based on pan-cancer analysis and artificial intelligence modeling of immunogenic cell death-based drug sensitivity stratification.
Frontiers in immunology
Jun 14, 2023
BACKGROUND: Immunogenic cell death (ICD) is a result of immune cell infiltration (ICI)-mediated cell death, which is also a novel acknowledgment to regulate cellular stressor-mediated cell death, including drug therapy and radiotherapy.
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