ArticleMedical & biological engineering & computing2017
Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer.
Article in Medical & biological engineering & computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- AI models for the identification of prognostic and predictive biomarkers in lung cancer: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
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- Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2023Article
- Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models.Biomedicines · 2022Article
- Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer.Journal of healthcare engineering · 2022Article
- Machine Learning Analysis of Immune Cells for Diagnosis and Prognosis of Cutaneous Melanoma.Journal of oncology · 2022Article
- Prediction of Gestational Diabetes Mellitus under Cascade and Ensemble Learning Algorithm.Computational intelligence and neuroscience · 2022Article
- Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data.Biomedicines · 2021Review
- EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer.BMC medical genomics · 2021Article
- Evaluation of the value of multiparameter combined analysis of serum markers in the early diagnosis of gastric cancer.World journal of gastrointestinal oncology · 2020Article
- Adaptive back-stepping cancer control using Legendre polynomials.IET systems biology · 2020Article
- Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters.ERJ open research · 2020Article
- [An Overview of the Application of Artificial Neural Networks in Lung Cancer Research].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2019Review
- Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.BioFactors (Oxford, England)Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.
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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.