AIMC Topic: DNA Methylation

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Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.

Scientific reports
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
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
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
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
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
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
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...

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...