Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems
The manuscript details the outcomes of a comprehensive study on the application of cluster-bicluster analysis, gene ontology analysis, and convolutional neural network (CNN) for diagnosing cancer and Alzheimer’s disease using gene expression data derived from both DNA microarray experimen...
Saved in:
Main Authors: | Sergii Babichev, Igor Liakh, Jiri Skvor |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10857291/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Transcriptomic Analysis of Pathogenicity Genes in Sclerotinia sclerotiorum Affecting Brassica napus
by: Hengameh Taheri, et al.
Published: (2024-09-01) -
Ontology-based expansion of virtual gene panels to improve diagnostic efficiency for rare genetic diseases
by: Jaemoon Shin, et al.
Published: (2025-02-01) -
Deep learning for stage prediction in neuroblastoma using gene expression data
by: Aron Park, et al.
Published: (2019-09-01) -
Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture
by: Anju Das, et al.
Published: (2024-01-01) -
Classification of Genes Based on Age-Related Differential Expression in Breast Cancer
by: Gunhee Lee, et al.
Published: (2017-12-01)