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DNA Methylation

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Classification of lung cancer using ensemble-based feature selection and machine learning methods.

Molecular bioSystems
Lung cancer is one of the leading causes of death worldwide. There are three major types of lung cancers, non-small cell lung cancer (NSCLC), small cell lung cancer (SCLC) and carcinoid. NSCLC is further classified into lung adenocarcinoma (LADC), sq...

MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.

Briefings in bioinformatics
DNA methylation plays a crucial role in human diseases pathogenesis. Substantial experimental evidence from clinical and biological studies has confirmed numerous methylation-disease associations, which provide valuable prior knowledge for advancing ...

[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].

Zhonghua zhong liu za zhi [Chinese journal of oncology]
Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesions. Human DNA methylation detection was performed on 224 cervical exfoliated cell specimens from th...

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...

Deep learning-based classifier for carcinoma of unknown primary using methylation quantitative trait loci.

Journal of neuropathology and experimental neurology
Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive patholo...

GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.

Journal of cellular and molecular medicine
Multimodal information provides valuable resources for cancer prognosis and survival prediction. However, the computational integration of this heterogeneous data information poses significant challenges due to the complex interactions between molecu...

Inferring tumor purity using multi-omics data based on a uniform machine learning framework MoTP.

Briefings in bioinformatics
Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mutations, and DNA methylation. Here we proposed the machine learning Multi-omics Tumor Purity predict...

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.

Neuro-oncology
BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the backgro...

Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.

Briefings in bioinformatics
Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it enables direct sequencing of native DNA molecules without the need for prior enzymatic or chemical tre...

Deep5hmC: predicting genome-wide 5-hydroxymethylcytosine landscape via a multimodal deep learning model.

Bioinformatics (Oxford, England)
MOTIVATION: 5-Hydroxymethylcytosine (5hmC), a crucial epigenetic mark with a significant role in regulating tissue-specific gene expression, is essential for understanding the dynamic functions of the human genome. Despite its importance, predicting ...