ArticleJournal of computer-aided molecular design2026
Gene-based lung cancer detection system through omix data and optimized convolutional neural network.
Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Multimodal machine learning and deep graph neural networks for the prediction of molecular inhibitory activity and disease associations.Journal of computer-aided molecular design · 2026Article
- Multimodal machine learning and deep graph neural networks for the prediction of molecular inhibitory activity and disease associations.Journal of computer-aided molecular design · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among the cancers that pose the greatest threat to life worldwide is lung cancer. According to estimates from the World Cancer Research Fund International, there will be 1.8 million new instances of this disease diagnosed in 2022. When medical personnel diagnose and classify patients' conditions proactively, they may treat them safely and efficiently. The advent of the microarray method has made it possible to examine the connections between genes and various diseases, including lung malignancies. Numerous methods have been developed to forecast gene-based diseases, but they still have problems with high computational cost, time consumption, complex data, and inaccurate prediction. Therefore, create an efficient lung cancer detection system in this research by designing an Improved Convolutional Neural Network with Honey Bee Mating Optimization (ICNN-HBMO). First, the system is trained using Omix data, and the dataset is normalized using min-max normalization. Then Kernel Principal Component Analysis (KPCA) technique is employed for feature reduction. Furthermore, an enhanced CNN is employed to classify lung cancer using HBMO. The HBMO algorithm optimizes the weight and bias parameters of the ICNN to improve prediction performance. The developed method is implemented in the Matlab tool, and the improved performance is compared to other existing methods. The developed technique attains high accuracy and high precision of 99.2% and 99%.
Indexed as
Identifiers
41772156What Socratic 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 Socratic graph.