Gene Function Prediction

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• gene function
• methods
• prediction
• databases
• machine learning