ArticleLife (Basel, Switzerland)2021
Cancer Type Classification in Liquid Biopsies Based on Sparse Mutational Profiles Enabled through Data Augmentation and Integration.
Article in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- DeepGene-BC: Deep Learning-Based Breast Cancer Subtype Prediction via Somatic Point Mutation Profiles.Cancers · 2026Article
- Multiple-instance learning of somatic mutations for the classification of tumour type and the prediction of microsatellite status.Nature biomedical engineering · 2024Article
- Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping.Genome medicine · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Identifying the cell of origin of cancer is important to guide treatment decisions. Machine learning approaches have been proposed to classify the cell of origin based on somatic mutation profiles from solid biopsies. However, solid biopsies can cause complications and certain tumors are not accessible. Liquid biopsies are promising alternatives but their somatic mutation profile is sparse and current machine learning models fail to perform in this setting. We propose an improved method to deal with sparsity in liquid biopsy data. Firstly, data augmentation is performed on sparse data to enhance model robustness. Secondly, we employ data integration to merge information from: (i) SNV density; (ii) SNVs in driver genes and (iii) trinucleotide motifs. Our adapted method achieves an average accuracy of 0.88 and 0.65 on data where only 70% and 2% of SNVs are retained, compared to 0.83 and 0.41 with the original model, respectively. The method and results presented here open the way for application of machine learning in the detection of the cell of origin of cancer from liquid biopsy data.
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