ArticleFrontiers in genetics2021
A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Decreased SNCA expression in whole-blood RNA analysis of Parkinson's disease adjusting for neutrophils.NPJ Parkinson's disease · 2025Article
- Tracing unknown tumor origins with a biological-pathway-based transformer model.Cell reports methods · 2024Article
- Machine learning for pan-cancer classification based on RNA sequencing data.Frontiers in molecular biosciences · 2023Review
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
- Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification.Medical & biological engineering & computing · 2022Article
- A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data.Frontiers in oncology · 2022Article
- Identifying cancer tissue-of-origin by a novel machine learning method based on expression quantitative trait loci.Frontiers in oncology · 2022Article
- Prediction and Screening Model for Products Based on Fusion Regression and XGBoost Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeEstablish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions' diagnostic efficiency.
methodsAfter deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set.
resultsSelecting features with around 800 genes for training, the
conclusionThese findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors' location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.
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