AIMC Topic: Genetic Markers
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An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples.
Forensic science international. Genetics
Aug 9, 2024
Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate st...
Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms.
BMC cardiovascular disorders
Aug 2, 2024
BACKGROUND: Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers.
Exploring T-cell exhaustion features in Acute myocardial infarction for a Novel Diagnostic model and new therapeutic targets by bio-informatics and machine learning.
BMC cardiovascular disorders
May 24, 2024
BACKGROUND: T-cell exhaustion (TEX), a condition characterized by impaired T-cell function, has been implicated in numerous pathological conditions, but its role in acute myocardial Infarction (AMI) remains largely unexplored. This research aims to i...
Development of a two-layer machine learning model for the forensic application of legal and illegal poppy classification based on sequence data.
Forensic science international. Genetics
May 22, 2024
Poppies are beneficial plants with a variety of applications, including medicinal, edible, ornamental, and industrial purposes. Some Papaver species are forensically significant plants because they contain opium, a narcotic substance. Internationally...
Discrimination of Genetic Biomarkers of Disease through Machine-Learning-Based Hypothesis Testing of Direct SERS Spectra of DNA and RNA.
ACS sensors
Apr 29, 2024
Cancer is globally a leading cause of death that would benefit from diagnostic approaches detecting it in its early stages. However, despite much research and investment, cancer early diagnosis is still underdeveloped. Owing to its high sensitivity, ...
Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease.
Computers in biology and medicine
Apr 5, 2024
Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical re...
Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable Artificial Intelligence.
Cancer communications (London, England)
Apr 2, 2024
Exploring a novel seven-gene marker and mitochondrial gene TMEM38A for predicting cervical cancer radiotherapy sensitivity using machine learning algorithms.
Frontiers in endocrinology
Jan 24, 2024
BACKGROUND: Radiotherapy plays a crucial role in the management of Cervical cancer (CC), as the development of resistance by cancer cells to radiotherapeutic interventions is a significant factor contributing to treatment failure in patients. However...
c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer's disease.
BMC medical genomics
Oct 13, 2023
BACKGROUND: Alzheimer's disease (AD) is an incurable, debilitating neurodegenerative disorder. Current biomarkers for AD diagnosis require expensive neuroimaging or invasive cerebrospinal fluid sampling, thus precluding early detection. Blood-based b...
Explainable artificial intelligence model for identifying COVID-19 gene biomarkers.
Computers in biology and medicine
Feb 1, 2023
AIM: COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COV...
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