Omics data classification using constitutive artificial neural network optimized with single candidate optimizer
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View abstract on PubMed
Summary
This summary is machine-generated.A new omics data classification method, ODC-ZOA-CANN-SCO, improves accuracy by leveraging Adaptive variational Bayesian filtering, Zebra Optimization Algorithm, Constitutive Artificial Neural Network, and Single Candidate Optimizer for biological data analysis.
Area Of Science
- Bioinformatics
- Computational Biology
- Genomics
- Proteomics
- Microbionics
Background
- High-throughput omics studies generate vast datasets.
- Analyzing complex omics data presents significant challenges.
- Existing methods struggle with data integration and classification accuracy.
Purpose Of The Study
- To propose a novel method for accurate omics data classification.
- To enhance the performance of omics data analysis through advanced algorithms.
- To address the limitations of current omics data processing techniques.
Main Methods
- Omics Data Classification using Constitutive Artificial Neural Network Optimized with Single Candidate Optimizer (ODC-ZOA-CANN-SCO).
- Data pre-processing using Adaptive variational Bayesian filtering (AVBF) for missing value imputation.
- Dimensionality reduction via Zebra Optimization Algorithm (ZOA).
- Classification using Constitutive Artificial Neural Network (CANN) with weights optimized by Single Candidate Optimizer (SCO).
Main Results
- The ODC-ZOA-CANN-SCO method demonstrated significant accuracy improvements over existing approaches.
- Achieved higher accuracy compared to MOD-AGL-AM-PABI, DL-MODI-RSP-SCM, DDN-DAD-MOD, HCP-MOD-RL-SARSA, and ML-ODBKD-CCEP.
- Accuracy gains ranged from 21.04% to 28.12% depending on the compared method.
Conclusions
- The proposed ODC-ZOA-CANN-SCO method offers a superior approach for omics data classification.
- This method effectively handles missing values and reduces data dimensionality.
- The findings suggest a promising direction for advancing biological data analysis and interpretation.
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