ArticleFrontiers in bioengineering and biotechnology2020
Discriminating Origin Tissues of Tumor Cell Lines by Methylation Signatures and Dys-Methylated Rules.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Article
- Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions.BioMed research international · 2022Article
- Identification of protein-protein interaction associated functions based on gene ontology and KEGG pathway.Frontiers in genetics · 2022Article
- Machine Learning Approaches to Classify Primary and Metastatic Cancers Using Tissue of Origin-Based DNA Methylation Profiles.Cancers · 2021Article
- Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile.Frontiers in bioinformatics · 2021Article
- Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes.Frontiers in microbiology · 2021Article
- iMPTCE-Hnetwork: A Multilabel Classifier for Identifying Metabolic Pathway Types of Chemicals and Enzymes with a Heterogeneous Network.Computational and mathematical methods in medicine · 2021Article
- Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods.BioMed research international · 2021Article
- Identification of Protein Subcellular Localization With Network and Functional Embeddings.Frontiers in genetics · 2020Article
- Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure.Frontiers in physiology · 2020Article
- Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection.Frontiers in cell and developmental biology · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
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Abstract
DNA methylation is an essential epigenetic modification for multiple biological processes. DNA methylation in mammals acts as an epigenetic mark of transcriptional repression. Aberrant levels of DNA methylation can be observed in various types of tumor cells. Thus, DNA methylation has attracted considerable attention among researchers to provide new and feasible tumor therapies. Conventional studies considered single-gene methylation or specific loci as biomarkers for tumorigenesis. However, genome-scale methylated modification has not been completely investigated. Thus, we proposed and compared two novel computational approaches based on multiple machine learning algorithms for the qualitative and quantitative analyses of methylation-associated genes and their dys-methylated patterns. This study contributes to the identification of novel effective genes and the establishment of optimal quantitative rules for aberrant methylation distinguishing tumor cells with different origin tissues.
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