ArticleScientific reports2019
CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.
Article in Scientific reports, 2019. 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.
- AI based multiomics integration for cancer diagnosis and prognosis.Journal, genetic engineering & biotechnology · 2026Article
- A multi-representation deep-learning framework for accurate multicancer classification.Journal of translational medicine · 2025Article
- Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.Cancer discovery · 2024Article
- Classification performance assessment for imbalanced multiclass data.Scientific reports · 2024Article
- Multi-omics based artificial intelligence for cancer research.Advances in cancer research · 2024Review
- Integrative analyses and validation of ferroptosis-related genes and mechanisms associated with cerebrovascular and cardiovascular ischemic diseases.BMC genomics · 2023Article
- Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations.Journal of translational medicine · 2023Article
- Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.Genome medicine · 2023Article
- Classification of group A rotavirus VP7 and VP4 genotypes using random forest.Frontiers in genetics · 2023Article
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- GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints.Cancers · 2022Article
- Secure tumor classification by shallow neural network using homomorphic encryption.BMC genomics · 2022Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- Predicting chemotherapy response using a variational autoencoder approach.BMC bioinformatics · 2021Article
- A stacking ensemble deep learning approach to cancer type classification based on TCGA data.Scientific reports · 2021Article
- Cancer Classification with a Cost-Sensitive Naive Bayes Stacking Ensemble.Computational and mathematical methods in medicine · 2021Article
- Performance-weighted-voting model: An ensemble machine learning method for cancer type classification using whole-exome sequencing mutation.Quantitative biology (Beijing, China) · 2020Article
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4 authors.
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Abstract
With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble Model), which is tested on 7,002 samples representing over 31 different cancer types collected from The Cancer Genome Atlas (TCGA) database. We first systematically examined the impact of the input features. Features known to be associated with specific cancers had relatively high importance in our initial prediction model. We further investigated various machine learning classifiers and feature selection methods to derive the ensemble-based cancer type prediction model achieving up to 84% classification accuracy in the nested 10-fold cross-validation. Finally, we narrowed down the target cancers to the six most common types and achieved up to 94% accuracy.
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