Gene Function Prediction
A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Molecular Biology".
Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 630
Special Issue Editors
Interests: comparative transcriptomics; plant evolution; co-expression; specialized metabolism
Special Issues, Collections and Topics in MDPI journals
Interests: evolution; comparative genomics; phylogenomics
Interests: RNA secondary structure; machine-learning; lncRNAs
Special Issue Information
Dear Colleagues,
Our understanding of how genes work together to form complex traits relies on our ability to correctly assign biological functions to gene products. As an empirical (experimental) elucidation of gene function can take years, in silico gene function prediction methods have become a common approach to suggest the function of genes found in newly sequenced genomes, or of known genes, but with ambiguous or unknown function.
Gene function annotation is fundamental to understand how a cell works, and gene function prediction is crucial for this task. Consequently, a huge variety of computational methods and online databases that predict protein functions have been developed in recent years. These methods take advantage of multiple data sources to increase the accuracy of their predictive power while expanding their training set. For example, these sources can include sequence, expression profile, genomic context, structure, and molecular interaction, and others. The gene function prediction methods can be categorized into three generations. The first-generation methods use one type of data (e.g., protein sequence, gene expression, protein-protein interaction) to link functionally related genes and predict their function. The second-generation methods integrate the different types of data to increase the coverage and confidence of the predictions. Finally, the third-generation ensemble methods integrate the outputs of multiple prediction algorithms to often produce a better prediction than any of the individual algorithms.
To gain a better overview of the recent methods for gene function prediction in plants, this Special Issue welcomes articles that describe new methods, analyses, and databases that address the problem of predicting gene function in plants. We invite original research papers, perspectives, hypotheses, opinions, reviews, modeling approaches, and methods.
Dr. Marek Mutwil
Dr. Irene Julca
Dr. Riccardo Delli Ponti
Guest Editors
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Keywords
- gene function
- methods
- prediction
- databases
- machine learning
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