Application of Bioinformatics in Plants
A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 12166
Special Issue Editor
Interests: plant biotechnology; computational molecular biology; bioinformatics applications in plants; abiotic stress tolerance in plant; membrane transport proteins in plants
Special Issue Information
Dear Colleagues,
The discovery of high-throughput sequencing (HTS) and its employment in genomics, transcriptomics and other omics disciplines paved the way for the production of a staggering amount of big biological data. The inference of meaningful results from ever-increasing biological data regarding structures and functions of genes, their evolutions over time, protein–protein or protein–ligand interactions and to shed light on metabolic processes and pathways all depend on the effective and accurate processing of big biological data. In this context, bioinformatics plays a major role in deciphering behaviors, interactions and networks of biological molecules and structures. Today, the employment of neural network methods under artificial intelligence and machine learning gives further impetus to understand living systems through more efficient and effective data mining and data processing of the accumulated biological big data.
With this Special Issue, I wish to invite the submission of high-quality original research manuscripts or review articles on any topic related to: “Application of Bioinformatics in Plants”.
In order to contribute to the understanding of the complex molecular worlds of plants, I request you to submit your research and join in our Special Issue.
You will find all the necessary information and instructions and a team up to the task at the Genes editorial office.
Prof. Dr. Ertugrul Filiz
Guest Editor
Manuscript Submission Information
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Keywords
- genome-wide analyses
- comparative transcriptomics
- comparative transcriptome meta-analysis
- phylogenomics and evolutionary analyses
- gene co-expression network analysis
- machine learning in plant science
- comparative genomics
- protein sequence analyses and 3D modeling
- molecular docking analysis
- virtual screening for drug discovery
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