AIMC Topic: Machine Learning

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Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients.

Clinical epigenetics
BACKGROUND: There is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

BMC medical imaging
Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. ...

Predicting severe intraventricular hemorrhage or early death using machine learning algorithms in VLBWI of the Korean Neonatal Network Database.

Scientific reports
Severe intraventricular hemorrhage (IVH) in premature infants can lead to serious neurological complications. This retrospective cohort study used the Korean Neonatal Network (KNN) dataset to develop prediction models for severe IVH or early death in...

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning.

Scientific reports
Cutaneous squamous cell carcinoma (SCC) is an increasingly prevalent global health concern. Current diagnostic and surgical methods are reliable, but they require considerable resources and do not provide metabolomic insight. Matrix-assisted laser de...

Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics.

Scientific reports
Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed...

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.

Scientific reports
This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key mom...

Machine learning algorithm predicts urethral stricture following transurethral prostate resection.

World journal of urology
PURPOSE: To predict the post transurethral prostate resection(TURP) urethral stricture probability by applying different machine learning algorithms using the data obtained from preoperative blood parameters.

Estimating the volume of penumbra in rodents using DTI and stack-based ensemble machine learning framework.

European radiology experimental
BACKGROUND: This study investigates the potential of diffusion tensor imaging (DTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion-diffusion mismatch (PDM), utilizing a stack-based ensemble machine learnin...

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome.

Omics : a journal of integrative biology
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline tha...

A roadmap for model-based bioprocess development.

Biotechnology advances
The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process developmen...