ReviewFrontiers in oncology2021
Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology.
Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep learning in cancer epigenetics through DNA methylation analysis.Briefings in bioinformatics · 2023Pooled it
- Identification of Clinically Relevant Genes Using Machine Learning Techniques.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Research Trends and Development Dynamics of qPCR-based Biomarkers: A Comprehensive Bibliometric Analysis.Molecular biotechnology · 2026Review
- LncRNA SNHG11 expression in acute myeloid leukemia patients and its relationship with the biology of acute myeloid leukemia cells.Clinical and experimental medicine · 2025Article
- Overview of exosomal non-coding RNAs in cardiovascular disease using high throughput sequencing.European journal of pharmacology · 2025Review
- Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens.Communications biology · 2025Article
- Epigenomic insights and computational advances in hematologic malignancies.Molecular cytogenetics · 2025Review
- Establishment of a machine learning model for predicting splenic hilar lymph node metastasis.NPJ digital medicine · 2025Article
- Integrative analysis reveals early epigenetic alterations in high-grade serous ovarian carcinomas.Experimental & molecular medicine · 2023Article
- Analysis of super-enhancer using machine learning and its application to medical biology.Briefings in bioinformatics · 2023Review
- Expression signature and molecular basis of CDH11 in OSCC detected by a combination of multiple methods.BMC medical genomics · 2023Article
- Tumor Suppressive Role of theJournal of personalized medicine · 2022Article
- Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.Experimental hematology & oncology · 2022Review
- Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.Briefings in bioinformatics · 2022Review
- Article
- Automated Endocardial Border Detection and Left Ventricular Functional Assessment in Echocardiography Using Deep Learning.Biomedicines · 2022Article
- Experimental Nuclear Medicine Meets Tumor Biology.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research.Biomedicines · 2021Review
- Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics.Journal of personalized medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, "precision medicine," which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.