ArticleIET systems biology2016
Lung cancer prediction from microarray data by gene expression programming.
Article in IET systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases.Scientific reports · 2025Article
- The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.Scientific reports · 2025Article
- Article
- Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data-In Pursuit of Precision.Bioengineering (Basel, Switzerland) · 2024Article
- Lung cancer diagnosis based on weighted convolutional neural network using gene data expression.Scientific reports · 2024Article
- Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.Bioengineering (Basel, Switzerland) · 2023Article
- A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques.Journal of healthcare engineering · 2021Article
- Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification.IET systems biology · 2020Article
- Deep gene selection method to select genes from microarray datasets for cancer classification.BMC bioinformatics · 2019Article
- Helicase lymphoid-specifics and selenoprotein P1 are potential candidate genes in the progression and prognosis of lung adenocarcinoma.Translational cancer research · 2019Article
- Differentially Expressed Gene Screening, Biological Function Enrichment, and Correlation with Prognosis in Non-Small Cell Lung Cancer.Medical science monitor : international medical journal of experimental and clinical research · 2019Article
- Cancer adjuvant chemotherapy prediction model for non-small cell lung cancer.IET systems biology · 2019Article
- A novel gene selection algorithm for cancer classification using microarray datasets.BMC medical genomics · 2019Article
- An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming.Medical & biological engineering & computing · 2018Article
- Prediction of NSCLC recurrence from microarray data with GEP.IET systems biology · 2017Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.
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