AIMC Topic: DNA Methylation
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Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.
Scientific reports
Nov 11, 2024
Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This st...
Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts.
Computers in biology and medicine
Nov 4, 2024
Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone...
Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.
BioFactors (Oxford, England)
Oct 11, 2024
The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognos...
A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin.
International journal of clinical oncology
Sep 18, 2024
BACKGROUND: Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of ...
Diagnostic classification based on DNA methylation profiles using sequential machine learning approaches.
PloS one
Sep 6, 2024
Aberrant methylation patterns in human DNA have great potential for the discovery of novel diagnostic and disease progression biomarkers. In this paper we used machine learning algorithms to identify promising methylation sites for diagnosing cancero...
A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.
Interdisciplinary sciences, computational life sciences
Sep 4, 2024
BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into t...
Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer.
PloS one
Sep 3, 2024
MOTIVATION: There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about imp...
Deep learning can predict subgenome dominance in ancient but not in neo/synthetic polyploidized genomes.
The Plant journal : for cell and molecular biology
Aug 12, 2024
Deep learning offers new approaches to investigate the mechanisms underlying complex biological phenomena, such as subgenome dominance. Subgenome dominance refers to the dominant expression and/or biased fractionation of genes in one subgenome of all...
Deep learning based method for predicting DNA N6-methyladenosine sites.
Methods (San Diego, Calif.)
Aug 6, 2024
DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intens...
Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.
NPJ systems biology and applications
Aug 2, 2024
Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks inform...
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