ArticleIET systems biology2022
Deep learning-based microarray cancer classification and ensemble gene selection approach.
Article in IET systems biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification.Journal of imaging informatics in medicine · 2026Article
- Hybrid Synthetic Minority Over-sampling Technique (HSMOTE) and Ensemble Deep Dynamic Classifier Model (EDDCM) for big data analytics.Scientific reports · 2025Article
- Stage-based colorectal cancer prediction on uncertain dataset using rough computing and LSTM models.Scientific reports · 2024Article
- Artificial Intelligence in Pediatrics: Learning to Walk Together.Turkish archives of pediatrics · 2024Article
- Cancer genetics and deep learning applications for diagnosis, prognosis, and categorization.Journal of biological methods · 2024Review
- Biomarker detection using corrected degree of domesticity in hybrid social network feature selection for improving classifier performance.BMC bioinformatics · 2023Article
- Article
- Deep learning-based microarray cancer classification and ensemble gene selection approach.IET systems biology · 2022Article
- 5G System Overview for Ongoing Smart Applications: Structure, Requirements, and Specifications.Computational intelligence and neuroscience · 2022Review
- Control System Development and Implementation of a CNC Laser Engraver for Environmental Use with Remote Imaging.Computational intelligence and neuroscience · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k-nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis-related brain tissue lesions were examined to show the generalisability of the model method.
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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.