ArticleScientific reports2023
Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessing the clinical, humanistic, and economic impact of early cancer diagnosis: a systematic literature review.Frontiers in oncology · 2025Pooled it
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Current Insights and Future Directions of Multiomic and Spatial-Omic Analysis in Non-Small Cell Lung Cancer.JCO precision oncology · 2026Review
- Resolving Interpretation Challenges in Machine Learning Feature Selection With an Iterative Approach in Biomedical Pain Data.European journal of pain (London, England) · 2026Article
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- A fusion-based multiomics classification approach for enhanced gene discovery in non-small cell lung cancer.Bioinformatics advances · 2026Article
- Machine learning and WGCNA reveal the PVT1/miR-143-3p/CDK1 ceRNA axis as a key regulator in NSCLC.Biochemistry and biophysics reports · 2025Article
- Article
- MO-GCAN: multi-omics integration based on graph convolutional and attention networks.Bioinformatics (Oxford, England) · 2025Article
- IGCN: integrative graph convolution networks for patient level insights and biomarker discovery in multi-omics integration.Bioinformatics (Oxford, England) · 2025Article
- Investigating the frequencies of EGFR mutations and EGFR single nucleotide polymorphisms genotypes and their predictive role in NSCLC patients in Republic of Serbia.Molecular biology reports · 2025Article
- Heterogeneity in Cancer.Cancers · 2025Review
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- Review
- Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism.Clinical and translational science · 2024Article
- Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment.Journal of hematology & oncology · 2023Review
- Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods.BioData mining · 2023Article
- Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.
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