AI Article Synopsis
- GISTs are a type of tumor in the gastrointestinal system, with varying characteristics and unclear molecular causes; microRNAs (miRNAs) play a role in cancer development by regulating gene expression.* -
- The study aimed to identify specific miRNA expressions linked to gastric GISTs and to create miRNA profiles that could distinguish GIST patients from healthy individuals.* -
- Results showed that several miRNAs were differently expressed in GIST patients, with hsa-miR-218-5p being the strongest predictor for GIST development, suggesting that machine learning can help assess GIST risk.*
Article Abstract
Background: Gatrointestinal stromal tumors (GISTs) are the main mesenchymal tumors found in the gastrointestinal system. GISTs clinical phenotypes differ significantly and their molecular basis is not yet completely known. microRNAs (miRNAs) have been involved in carcinogenesis pathways by regulating gene expression at post-transcriptional level.
Objective: The aim of the present study was to elucidate the expression profiles of miRNAs relevant to gastric GIST carcinogenesis, and to identify miRNA signatures that can discriminate the GIST from normal cases.
Methods: miRNA expression was tested by miScript™miRNA PCR Array Human Cancer PathwayFinder kit and then we used machine learning in order to find a miRNA profile that can predict the risk for GIST development.
Results: A number of miRNAs were found to be differentially expressed in GIST cases compared to healthy controls. Among them the hsa-miR-218-5p was found to be the best predictor for GIST development in our cohort. Additionally, hsa-miR-146a-5p, hsa-miR-222-3p, and hsa-miR-126-3p exhibit significantly lower expression in GIST cases compared to controls and were among the top predictors in all our predictive models.
Conclusions: A machine learning classification approach may be accurate in determining the risk for GIST development in patients. Our findings indicate that a small number of miRNAs, with hsa-miR218-5p as a focus, may strongly affect the prognosis of GISTs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/CBM-210173 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Adaptive deep feature representation learning for cross-subject EEG decoding.
BMC Bioinformatics
December 2024
College of Computer and Information Engineering/College of Artificial Intelligence, Nanjing Tech University, Nanjing, 210093, China.
Background: The collection of substantial amounts of electroencephalogram (EEG) data is typically time-consuming and labor-intensive, which adversely impacts the development of decoding models with strong generalizability, particularly when the available data is limited. Utilizing sufficient EEG data from other subjects to aid in modeling the target subject presents a potential solution, commonly referred to as domain adaptation. Most current domain adaptation techniques for EEG decoding primarily focus on learning shared feature representations through domain alignment strategies.
View Article and Find Full Text PDFAutomated differentiation of wide QRS complex tachycardia using QRS complex polarity.
Commun Med (Lond)
December 2024
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Background: Wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) remains challenging despite numerous 12-lead electrocardiogram (ECG) criteria and algorithms. Automated solutions leveraging computerized ECG interpretation (CEI) measurements and engineered features offer practical ways to improve diagnostic accuracy. We propose automated algorithms based on (i) WCT QRS polarity direction (WCT Polarity Code [WCT-PC]) and (ii) QRS polarity shifts between WCT and baseline ECGs (QRS Polarity Shift [QRS-PS]).
View Article and Find Full Text PDFAutoML based workflow for design of experiments (DOE) selection and benchmarking data acquisition strategies with simulation models.
Sci Rep
December 2024
Aschaffenburg University of Applied Sciences, Faculty of Engineering, Aschaffenburg, 63743, Germany.
Design of experiments (DOE) is an established method to allocate resources for efficient parameter space exploration. Model based active learning (AL) data sampling strategies have shown potential for further optimization. This paper introduces a workflow for conducting DOE comparative studies using automated machine learning.
View Article and Find Full Text PDFUnderstanding the coupled relationship between regional longevity and physical geographical environment in Hechi, Guangxi, China.
Sci Rep
December 2024
Department of Infrastructure, The University of Melbourne, Melbourne, Australia.
Healthy ageing plays an important role in ageing societies in many countries, and centenarians are a sign of longevity. Longevity and its determinants have become issues of global concern and also a focus of research. Although many disciplines have conducted out a series of studies on longevity phenomena, few studies have systematically considered the impact of geographical environmental factors.
View Article and Find Full Text PDFSelf-supervised denoising of grating-based phase-contrast computed tomography.
Sci Rep
December 2024
Research Group Biomedical Imaging Physics, Department of Physics, TUM School of Natural Sciences, Technical University of Munich, 85748, Garching, Germany.
In the last decade, grating-based phase-contrast computed tomography (gbPC-CT) has received growing interest. It provides additional information about the refractive index decrement in the sample. This signal shows an increased soft-tissue contrast.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!