Integrating Chicken Swarm Optimization with Deep Learning for Microarray Gene Expression Classification DOI

B. Shyamala Gowri,

Sanjay Nair,

K. P. Sanal Kumar

et al.

2022 6th International Conference on Devices, Circuits and Systems (ICDCS), Journal Year: 2024, Volume and Issue: unknown, P. 229 - 233

Published: April 23, 2024

Microarray Gene Expression classification is a computational model that vital in interpreting biological data encoded profiles of gene expression. Using microarray technology, which permits synchronized measurement numerous genes, this proposes to classify samples into different clusters dependent upon their expression patterns. By examining the massive amount produced from experimentations, researchers can discover significant insights procedures, possible biomarkers for diseases, and improve understanding cellular devices. The procedure includes preprocessing raw data, removing appropriate features, using sophisticated methods precisely allocate exact pathological or phenotypic types, donating considerably areas biomedical genomics research. In study, Classification Chicken Swarm Optimization with Deep Learning (MGEC-CSODL) presented, intended optimize accuracy efficacy classification. technique combines adaptive histogram-based enhance representation, uses DenseNet201 as an influential feature extraction strong learning, employs Optimizer (CSO) hyperparameter fine-tuning performance, Graph Convolutional Network (GCN) precise experimental outcomes establish MGEC-CSODL model, showcasing important growths when equated present models. This state-of-the-art not only progresses area bioinformatics but also delivers effective tool effectual analysis era high-throughput genomics.

Language: Английский

Recent advances in DNA nanotechnology for cancer detection and therapy: A review DOI

Donya Esmaeilpour,

Matineh Ghomi, Ehsan Nazarzadeh Zare‬

et al.

International Journal of Biological Macromolecules, Journal Year: 2025, Volume and Issue: unknown, P. 142136 - 142136

Published: March 1, 2025

Language: Английский

Citations

0
Integrating Chicken Swarm Optimization with Deep Learning for Microarray Gene Expression Classification DOI

B. Shyamala Gowri,

Sanjay Nair,

K. P. Sanal Kumar

et al.

2022 6th International Conference on Devices, Circuits and Systems (ICDCS), Journal Year: 2024, Volume and Issue: unknown, P. 229 - 233

Published: April 23, 2024

Microarray Gene Expression classification is a computational model that vital in interpreting biological data encoded profiles of gene expression. Using microarray technology, which permits synchronized measurement numerous genes, this proposes to classify samples into different clusters dependent upon their expression patterns. By examining the massive amount produced from experimentations, researchers can discover significant insights procedures, possible biomarkers for diseases, and improve understanding cellular devices. The procedure includes preprocessing raw data, removing appropriate features, using sophisticated methods precisely allocate exact pathological or phenotypic types, donating considerably areas biomedical genomics research. In study, Classification Chicken Swarm Optimization with Deep Learning (MGEC-CSODL) presented, intended optimize accuracy efficacy classification. technique combines adaptive histogram-based enhance representation, uses DenseNet201 as an influential feature extraction strong learning, employs Optimizer (CSO) hyperparameter fine-tuning performance, Graph Convolutional Network (GCN) precise experimental outcomes establish MGEC-CSODL model, showcasing important growths when equated present models. This state-of-the-art not only progresses area bioinformatics but also delivers effective tool effectual analysis era high-throughput genomics.

Language: Английский

Citations

0