ArticleInternational journal of molecular sciences2024
Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival.
Article in International journal of molecular sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Radon-Induced Radiation Biomarkers: A Scoping Review from Exposure Dosimetry to Early Biological Effects on the Lung.International journal of molecular sciences · 2026Article
- Multimodal deep learning for survival prediction and biomarker discovery in non-small cell lung cancer.BioData mining · 2026Article
- Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.International journal of molecular sciences · 2025Article
- Association between calcium intake and risk of congestive heart failure: a cross-sectional study from the 2003-2018 NHANES database.BMC cardiovascular disorders · 2025Article
- Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation.Scientific reports · 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
- Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.Frontiers in oncology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.
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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.