AIMC Topic: Epigenomics

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Combining mucosal microbiome and host multi-omics data shows prognostic potential in paediatric ulcerative colitis.

Nature communications
Current first-line treatments of paediatric ulcerative colitis (UC) maintain a 6-month remission in only half of the patients. Relapse prediction at diagnosis could enable earlier introduction of immunosuppressants. We collected intestinal biopsies f...

Epigenomic diagnosis and prognosis of Acute Myeloid Leukemia.

Nature communications
Despite the critical role of DNA methylation, clinical implementations harnessing its promise have not been described in acute myeloid leukemia. Utilizing DNA methylation from 3314 leukemia patient samples across 11 harmonized cohorts, we describe th...

Artificial Intelligence in cancer epigenomics: a review on advances in pan-cancer detection and precision medicine.

Epigenetics & chromatin
DNA methylation is a fundamental epigenetic modification that regulates gene expression and maintains genomic stability. Consequently, DNA methylation remains a key biomarker in cancer research, playing a vital role in diagnosis, prognosis, and tailo...

Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.

NAR genomics and bioinformatics
Large-scale quantitative studies have identified significant genetic associations for various neurological disorders. Expression quantitative trait locusĀ (eQTL) studies have shown the effect of single-nucleotide polymorphisms (SNPs) on the differenti...

Recent advances in omics and the integration of multi-omics in osteoarthritis research.

Arthritis research & therapy
Osteoarthritis (OA) is a complex disorder driven by the combination of environmental and genetic factors. Given its high global prevalence and heterogeneity, developing effective and personalized treatment methods is crucial. This requires identifyin...

Artificial intelligence-driven genotype-epigenotype-phenotype approaches to resolve challenges in syndrome diagnostics.

EBioMedicine
BACKGROUND: Decisions to split two or more phenotypic manifestations related to genetic variations within the same gene can be challenging, especially during the early stages of syndrome discovery. Genotype-based diagnostics with artificial intellige...

TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.

Nature communications
It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale mult...

Pathogenomic fingerprinting to identify associations between tumor morphology and epigenetic states.

European journal of cancer (Oxford, England : 1990)
INTRODUCTION: Measuring the chromatin state of a tumor provides a powerful map of its epigenetic commitments; however, as these are generally bulk measurements, it has not yet been possible to connect changes in chromatin accessibility to the patholo...

Genome-wide methylome modeling via generative AI incorporating long- and short-range interactions.

Science advances
Using millions of methylation segments, we developed DiffuCpG, a generative artificial intelligence (AI) diffusion model designed to solve the critical challenge of missing data in high-throughput methylation technologies. DiffuCpG goes beyond conven...

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...