Computational Intelligence and Deep Learning in Bioinformatics
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 396
Special Issue Editor
2. Center of Biotechnology, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Interests: machine learning; heuristic search methods; massive parallel computing; explainable artificial intelligence; structural bioinformatics; genomics and transcriptomics data analysis; systems biology; forensic genetics
Special Issue Information
Dear Colleagues,
The increasing availability of large-scale biological data, coupled with the complex nature of these interactions, has made it necessary to develop new computational approaches for data analysis and modeling. Computational intelligence and deep learning have emerged as powerful tools for addressing these challenges, providing new avenues for discovering biological insights.
This Special Issue aims to provide a comprehensive overview of the latest advancements in applying computational intelligence and deep learning in bioinformatics. We welcome original research articles, reviews, and perspectives that address the following topics:
- Interpretability and explainability of deep learning models in bioinformatics;
- Integration of multi-omics data using deep learning;
- Applications of deep reinforcement learning and generative models in bioinformatics;
- Computational intelligence methods for gene expression analysis, genetic association studies, and genomic data integration;
- Computational intelligence and deep learning for epigenetics;
- Computational intelligence and deep learning for functional genomics;
- Deep learning and computational intelligence for metagenome analysis;
- Deep learning for gene editing and synthetic biology.
Dr. Marcio Dorn
Guest Editor
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Keywords
- interpretability and explainability of deep learning models in bioinformatics
- integration of multi-omics data using deep learning
- applications of deep reinforcement learning and generative models in bioinformatics
- computational intelligence methods for gene expression analysis, genetic association studies, and genomic data integration
- computational intelligence and deep learning for epigenetics
- computational intelligence and deep learning for functional genomics
- deep learning and computational intelligence for metagenome analysis
- deep learning for gene editing and synthetic biology
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