AIMC Topic: Machine Learning
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Ensemble machine learning-based pre-trained annotation approach for scRNA-seq data using gradient boosting with genetic optimizer.
BMC bioinformatics
Jul 1, 2025
Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression by allowing researchers to analyze the transcriptomes of individual cells. This technology provides unprecedented insights into cellular heterogeneity, cellular st...
Lactylation associated biomarkers and immune infiltration in aortic dissection.
Scientific reports
Jul 1, 2025
Protein lactylation, a novel post-translational modification (PTM), has emerged as a critical factor in disease processes related to glycolysis and immune responses. However, its role in aortic dissection (AD) has yet to be thoroughly investigated. T...
Validation of an AI-enabled exome/transcriptome liquid biopsy platform for early detection, MRD, disease monitoring, and therapy selection for solid tumors.
Scientific reports
Jul 1, 2025
Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and w...
Quantitative ultrasound classification of healthy and chemically degraded ex-vivo cartilage.
Scientific reports
Jul 1, 2025
In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen and proteoglycans, mimicking an osteoarthritis condition in ex-vivo bovine cartilage samples. Most ana...
Exploring the spatiotemporal influence of climate on American avian migration with random forests.
Scientific reports
Jul 1, 2025
Birds have adapted to climatic and ecological cycles to inform their Spring and Fall migration timings, but anthropogenic global warming has affected these long-establish cycles. Understanding these dynamics is critical for conservation during a chan...
Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission.
Scientific reports
Jul 1, 2025
Scientists aim to create a system that can predict the likelihood of newborns being admitted to the neonatal intensive care unit (NICU) by combining various statistical methods. This prediction could potentially reduce the negative health outcomes, d...
A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.
Scientific reports
Jul 1, 2025
Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk of heart disease, stroke, and diabetes. Timely detection and intervention are crucial in preventing severe health outcomes. In this study, we implemen...
Machine learning-based model for behavioural analysis in rodents applied to the forced swim test.
Scientific reports
Jul 1, 2025
The Forced Swim Test (FST) is a widely used preclinical model for assessing antidepressant efficacy, studying stress response, and evaluating depressive-like behaviours in rodents. Over the last 10 years, more than 5500 scientific articles reporting ...
Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.
Scientific reports
Jul 1, 2025
Effective treatment for brain tumors relies on accurate detection because this is a crucial health condition. Medical imaging plays a pivotal role in improving tumor detection and diagnosis in the early stage. This study presents two approaches to th...
AI-driven analysis by identifying risk factors of VL relapse in HIV co-infected patients.
Scientific reports
Jul 1, 2025
Visceral Leishmaniasis (VL), also known as Kala-Azar, poses a significant global public health challenge and is a neglected disease, with relapses and treatment failures leading to increased morbidity and mortality. This study introduces an explainab...
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