ArticleAmerican journal of cancer research2020
Classify multicategory outcome in patients with lung adenocarcinoma using clinical, transcriptomic and clinico-transcriptomic data: machine learning versus multinomial models.
Article in American journal of cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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Who cites it
23 citing papers in PubMed, 22 citations in OpenAlex.
- MRPL15 as a Key Gene in the Malignant Proliferation of LUAD.Biochemical genetics · 2026Article
- Associations of Normalization and Regularization with Machine Learning Overfitting in Cross-dataset Classification of Deaths Using Transcriptomic and Clinical Data: A Secondary Analysis of Publicly Available Databases.Journal of clinical and translational pathology · 2026Article
- The HKDC1-ASS1-ACSBG2 axis reprograms lipid metabolism to drive therapeutic resistance in hepatocellular carcinoma.Journal of translational medicine · 2026Article
- KMT5B in Cancerous and Noncancerous Diseases: Clinical and Mechanical Considerations.International journal of medical sciences · 2026Review
- Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients.Briefings in bioinformatics · 2025Article
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- Biomarker discovery with quantum neural networks: a case-study in CTLA4-activation pathways.BMC bioinformatics · 2024Article
- A pan-cancer analysis uncovering the function of CRHBP in tumor immunity, prognosis and drug response: especially its function in LIHC.Scientific reports · 2024Article
- Multicategory Survival Outcomes Classification via Overlapping Group Screening Process Based on Multinomial Logistic Regression Model With Application to TCGA Transcriptomic Data.Cancer informatics · 2024Article
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- Enhancing prognostic accuracy in head and neck squamous cell carcinoma chemotherapy via a lipid metabolism-related clustered polygenic model.Cancer cell international · 2023Article
- Ancient segmentally duplicated LCORL retrocopies in equids.PloS one · 2023Article
- Prognostic Lysosome-Related Biomarkers for Predicting Drug Candidates in Hepatocellular Carcinoma: An Insilco Analysis.Journal of hepatocellular carcinoma · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Serum Proteomics Combined with Metabolomics Analysis Explore the Molecular Biological Characteristics of Eruptive Syringoma.Clinical, cosmetic and investigational dermatology · 2023Article
- Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models.Laboratory investigation; a journal of technical methods and pathology · 2022Article
- Article
- Signature based on RNA-binding protein-related genes for predicting prognosis and guiding therapy in non-small cell lung cancer.Frontiers in genetics · 2022Article
- SP1-Mediated Upregulation of circFAM126A Promotes Proliferation and Epithelial-Mesenchymal Transition of Oral Squamous Cell CarcinomaFrontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
Abstract
Classification of multicategory survival-outcome is important for precision oncology. Machine learning (ML) algorithms have been used to accurately classify multi-category survival-outcome of some cancer-types, but not yet that of lung adenocarcinoma. Therefore, we compared the performances of 3 ML models (random forests, support vector machine [SVM], multilayer perceptron) and multinomial logistic regression (Mlogit) models for classifying 4-category survival-outcome of lung adenocarcinoma using the TCGA. Mlogit model overall performed similar to SVM and multilayer perceptron models (micro-average area under curve=0.82), while random forests model was inferior. Surprisingly, transcriptomic data alone and clinico-transcriptomic data appeared sufficient to accurately classify the 4-category survival-outcome in these patients, but no models using clinical data alone performed well. Notably,
Indexed as
Identifiers
33415023PMC7783755W3119723890What OpenQuestion holds
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.