AIMC Topic: DNA Methylation
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CMImpute: cross-species and tissue imputation of species-level DNA methylation samples across mammalian species.
Genome biology
May 20, 2025
The large-scale application of the mammalian methylation array has substantially expanded the availability of DNA methylation data in mammalian species. However, this data captures only a small portion of species-tissue combinations. To address this,...
Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma.
Journal of ovarian research
May 19, 2025
BACKGROUND: Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC)...
Exploring potential methylation markers for ovarian cancer from cervical scraping samples.
Human genomics
May 17, 2025
BACKGROUND: Ovarian cancer has the highest mortality rate among gynecological cancers, making early detection crucial, as the five-year survival rate drops from 92% with early-stage diagnosis compared to 31% with late-stage diagnosis. Current diagnos...
Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.
Scientific reports
May 6, 2025
Acute myeloid leukemia (AML) is a severe hematological malignancy characterized by high recurrence rates, especially in pediatric patients, highlighting the need for reliable prognostic markers. This study proposes methylation signatures associated w...
Epigenome-wide association study identifies a specific panel of DNA methylation signatures for antenatal and postpartum depressive symptoms.
Journal of affective disorders
Apr 24, 2025
Depression during pregnancy and postpartum poses significant risks to both maternal and child well-being. The underlying biological mechanisms are unclear, but epigenetic variation could be exploited as a plausible candidate for early detection. We i...
Artificial intelligence-driven genotype-epigenotype-phenotype approaches to resolve challenges in syndrome diagnostics.
EBioMedicine
Apr 24, 2025
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...
Vascular-related biological stress, DNA methylation, allostatic load and domain-specific cognition: an integrated machine learning and causal inference approach.
BMC neurology
Apr 23, 2025
BACKGROUND: Vascular disease in aging populations spans a wide range of disorders including strokes, circulation disorders and hypertension. As individuals age, vascular disorders co-occur and hence exert combined effects. In the present study we int...
Transformer-based deep learning for accurate detection of multiple base modifications using single molecule real-time sequencing.
Communications biology
Apr 14, 2025
We had previously reported a convolutional neural network (CNN) based approach, called the holistic kinetic model (HK model 1), for detecting 5-methylcytosine (5mC) by single molecule real-time sequencing (Pacific Biosciences). In this study, we cons...
Genome-wide methylome modeling via generative AI incorporating long- and short-range interactions.
Science advances
Apr 11, 2025
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...
DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.
BMC bioinformatics
Apr 8, 2025
Gene expression is the basis for cells to achieve various functions, while DNA methylation constitutes a critical epigenetic mechanism governing gene expression regulation. Here we propose DeepMethyGene, an adaptive recursive convolutional neural net...
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