ArticleBiomolecules2020
Colorectal Cancer Prediction Based on Weighted Gene Co-Expression Network Analysis and Variational Auto-Encoder.
Article in Biomolecules, 2020. 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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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Harnessing AI for advancing pathogenic microbiology: a bibliometric and topic modeling approach.Frontiers in microbiology · 2024Pooled it
- Construction and validation of a hypertensive nephropathy diagnostic model based on mitochondrial-related genes using machine learning combined with WGCNA.International urology and nephrology · 2026Article
- Research status and trends of deep learning in colorectal cancer (2011-2023): Bibliometric analysis and visualization.World journal of gastrointestinal oncology · 2025Article
- Variational autoencoder enhanced analysis of energy metabolism and autophagy in exercising cardiomyocytes.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Identification of characteristic genes in cutaneous squamous cell carcinoma based on weighted gene co-expression network analysis.Frontiers in genetics · 2025Article
- Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer.Scientific reports · 2024Article
- Article
- A novel feature selection algorithm for identifying hub genes in lung cancer.Scientific reports · 2023Article
- Article
- Blood biomarker-based classification study for neurodegenerative diseases.Scientific reports · 2023Article
- Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis.BMC genomics · 2023Article
- Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents.BMC medical genomics · 2023Article
- RN-Autoencoder: Reduced Noise Autoencoder for classifying imbalanced cancer genomic data.Journal of biological engineering · 2023Article
- Construction of Prediction Model for Atrial Fibrillation with Valvular Heart Disease Based on Machine Learning.Reviews in cardiovascular medicine · 2022Article
- Comprehensive Analysis of Tripterine Anti-Ovarian Cancer Effects Using Weighted Gene Co-Expression Network Analysis and Molecular Docking.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure.Frontiers in cell and developmental biology · 2022Article
- Identification of DAPK1 as an autophagy-related biomarker for myotonic dystrophy type 1.Frontiers in genetics · 2022Article
- Deep Generative Learning-Based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests.IEEE transactions on instrumentation and measurement · 2022Article
- Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma.Computational intelligence and neuroscience · 2022Article
- Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis.Biology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
An effective feature extraction method is key to improving the accuracy of a prediction model. From the Gene Expression Omnibus (GEO) database, which includes 13,487 genes, we obtained microarray gene expression data for 238 samples from colorectal cancer (CRC) samples and normal samples. Twelve gene modules were obtained by weighted gene co-expression network analysis (WGCNA) on 173 samples. By calculating the Pearson correlation coefficient (PCC) between the characteristic genes of each module and colorectal cancer, we obtained a key module that was highly correlated with CRC. We screened hub genes from the key module by considering module membership, gene significance, and intramodular connectivity. We selected 10 hub genes as a type of feature for the classifier. We used the variational autoencoder (VAE) for 1159 genes with significantly different expressions and mapped the data into a 10-dimensional representation, as another type of feature for the cancer classifier. The two types of features were applied to the support vector machines (SVM) classifier for CRC. The accuracy was 0.9692 with an AUC of 0.9981. The result shows a high accuracy of the two-step feature extraction method, which includes obtaining hub genes by WGCNA and a 10-dimensional representation by variational autoencoder (VAE).
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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.