AIMC Topic: DNA Methylation

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Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms.

BMC genomics
BACKGROUND: Feed efficiency (FE) is an essential trait in livestock species because of the constant demand to increase the productivity and sustainability of livestock production systems. A better understanding of the biological mechanisms associated...

A predictive model for MGMT promoter methylation status in glioblastoma based on terahertz spectral data.

Analytical biochemistry
O-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a crucial biomarker in glioblastoma (GBM) that influences response to temozolomide. Traditional detection methods, such as gene sequencing, are time-consuming and limited to postope...

N6-methyladenine identification using deep learning and discriminative feature integration.

BMC medical genomics
N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasin...

The relationship between epigenetic biomarkers and the risk of diabetes and cancer: a machine learning modeling approach.

Frontiers in public health
INTRODUCTION: Epigenetic biomarkers are molecular indicators of epigenetic changes, and some studies have suggested that these biomarkers have predictive power for disease risk. This study aims to analyze the relationship between 30 epigenetic biomar...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

IEEE journal of biomedical and health informatics
Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of ...

Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.

Acta neurochirurgica
PURPOSE: Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms...

Spatial recognition and semi-quantification of epigenetic events in pancreatic cancer subtypes with multiplexed molecular imaging and machine learning.

Scientific reports
Genomic alterations are the driving force behind pancreatic cancer (PC) tumorigenesis, but they do not fully account for its diverse phenotypes. Investigating the epigenetic landscapes of PC offers a more comprehensive understanding and could identif...

Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

Cell genomics
DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low covera...

Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.

Nature communications
We have recently developed a machine learning classifier that enables fast, accurate, and affordable classification of brain tumors based on genome-wide DNA methylation profiles that is widely employed in the clinic. Neuro-oncology research would ben...

Identification of a novel hypermethylation marker, ZSCAN18, and construction of a diagnostic model in cervical cancer.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Cervical cancer (CC), a common female malignancy, has been linked to alterations in DNA methylation. This study employed an integrated "dry-wet lab" strategy combining bioinformatics, machine learning, and experimental validation to identify...