AIMC Topic: Epigenomics

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Personalized medicine for cardiovascular diseases: how next generation epigenetic technologies can contribute?

Epigenomics
Advances in DNA methylation and artificial intelligence have led to new methods for assessing risk and diagnosing coronary heart disease (CHD), the leading cause of death. However, whether these technologies can also be harnessed to generate new phar...

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

International journal of medical informatics
BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis ...

The predictive power of profiling the DNA methylome in human health and disease.

Epigenomics
Early and accurate diagnosis significantly improves the chances of disease survival. DNA methylation (5mC), the major DNA modification in the human genome, is now recognized as a biomarker of immense clinical potential. This is due to its ability to ...

scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.

Nature communications
Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our u...

ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.

Genome biology
We introduce ChromActivity, a computational framework for predicting and annotating regulatory activity across the genome through integration of multiple epigenomic maps and various functional characterization datasets. ChromActivity generates genome...

Graph neural networks for single-cell omics data: a review of approaches and applications.

Briefings in bioinformatics
Rapid advancement of sequencing technologies now allows for the utilization of precise signals at single-cell resolution in various omics studies. However, the massive volume, ultra-high dimensionality, and high sparsity nature of single-cell data ha...

Probabilistic and machine-learning methods for predicting local rates of transcription elongation from nascent RNA sequencing data.

Nucleic acids research
Rates of transcription elongation vary within and across eukaryotic gene bodies. Here, we introduce new methods for predicting elongation rates from nascent RNA sequencing data. First, we devise a probabilistic model that predicts nucleotide-specific...

Integrating single-cell multimodal epigenomic data using 1D convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Recent experimental developments enable single-cell multimodal epigenomic profiling, which measures multiple histone modifications and chromatin accessibility within the same cell. Such parallel measurements provide exciting new opportuni...

LOGOWheat: deep learning-based prediction of regulatory effects for noncoding variants in wheats.

Briefings in bioinformatics
Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, ...

A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Briefings in bioinformatics
Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologi...