ArticlePeerJ2018
Integrative machine learning analysis of multiple gene expression profiles in cervical cancer.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Integrated cross-sectional study and functional validation indicate the association of lactobacillus crispatus-derived D-lactic acid with cervical gene expression and precancerous cervical lesions.Journal of translational medicine · 2026Article
- Artificial intelligence-driven screening, early diagnosis, and treatment strategies for cervical cancer: an overview.Infectious agents and cancer · 2025Review
- Development and validation of a deep learning model for individualized survival prediction in advanced cervical cancer.Discover oncology · 2025Article
- The multifaceted roles of ribosomal P complex in cancer.World journal of surgical oncology · 2025Review
- The Impact of YRNAs on HNSCC and HPV Infection.Biomedicines · 2023Article
- Mutational Slime Mould Algorithm for Gene Selection.Biomedicines · 2022Article
- Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer.Cancers · 2022Review
- Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer.Computational and mathematical methods in medicine · 2022Article
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- Silencing theExperimental and therapeutic medicine · 2021Article
- Establishment of multifactor predictive models for the occurrence and progression of cervical intraepithelial neoplasia.BMC cancer · 2020Article
- COPB2 promotes metastasis and inhibits apoptosis of lung adenocarcinoma cells through functioning as a target of miR-216a-3p.Translational cancer research · 2020Article
- Upregulation of the Coatomer Protein Complex Subunit beta 2 (COPB2) Gene Targets microRNA-335-3p in NCI-H1975 Lung Adenocarcinoma Cells to Promote Cell Proliferation and Migration.Medical science monitor : international medical journal of experimental and clinical research · 2020Article
- Comprehensive Analysis of Tumor-Infiltrating Immune Cells and Relevant Therapeutic Strategy in Esophageal Cancer.Disease markers · 2020Article
- MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data.BMC bioinformatics · 2019Article
- Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.American journal of obstetrics and gynecology · 2019Article
- MicroRNAs, Hypoxia and the Stem-Like State as Contributors to Cancer Aggressiveness.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely
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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.