ArticleTranslational cancer research2020
Developing prognostic gene panel of survival time in lung adenocarcinoma patients using machine learning.
Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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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Who cites it
2 citing papers in PubMed, 6 citations in OpenAlex.
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTranscriptome data generates massive amounts of information that can be used for characterization and prognosis of patient outcomes for many diseases. The goal of our research is to predict the survival time of lung adenocarcinoma patients and improve the accuracy of classifying the long-survival cohort and short-survival cohort.
methodsWe filtered prognostic features related with survival time of lung adenocarcinoma patients by the method of Relief and predicted whether survival time of the patient is >3 years or not-using eight machine learning algorithms (Support Vector Machines, Random Forests, Logistic Regression, Naïve Bayes, Linear Regression, Support Vector Regression (kernel Poly), Support Vector Regression (kernel Linear), and Ridge Regression). Then the best-performed algorithm was chosen to build a predictive model of survival time of lung adenocarcinoma patients. Further, another dataset was used to verify the stability and suitability of this model. We explored the underlying mechanisms of RNA expression changes with the corresponding DNA mutations and DNA methylation patterns in the 22 selected genetic features.
resultsThe best machine learning algorithm was Naïve Bayes (accuracy=75%, AUC =0.81) using the top 22 genetic features, and this algorithm had the stable and great performance on another dataset as well. The coupled mutation number of the long-survival group (>6 years) was less than the short-survival group (<1 year) in 22 genes (P=0.031).
conclusionsThe expression of gene panel can predict the survival time of lung adenocarcinoma patients using Naïve Bayes. These 22 genes do affect the survival time of lung adenocarcinoma.
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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.