Machine Learning Techniques for High-Throughput Function Analysis in Proteomics and Genomics
With the development of high-throughput sequencing techniques, more and more sequencing data is available, including genomic reads, transcriptome data, and proteomic sequences. In order to fully utilize this information, it is critical to also investigate underlying mechanisms of biological function. Usually, researchers need to distinguish, or cluster, the sequence data from proteomic and genomic functional analyses. Genomic functions can also be identified from classification results, such as motif and regulatory region identification, and even some epigenomics and disease relationship predictions.
Machine learning methods are important techniques for these tasks, especially for ensemble learning, large scale data processing, various kernel designs, and imbalanced classification methods.
We invite authors to contribute original research manuscripts or reviews, which introduce advanced machine learning algorithms and their application in protein or genome sequence analysis.
Potential topics include, but are not limited to:
● Protein structure prediction with machine learning methods
● Special protein identification methods
● Epigenomics and disease relationship prediction
● Motif and regulatory element(s) identification from high-throughput data
● Advanced machine learning methods with the application to bioinformatics
● Cloud computing and parallel machine learning techniques for protein structure and genomics function analysis
Prof. Dr. Leyi Wei
Manuscripts should be submitted online at https://jour.ipublishment.com/bri by registering and logging into this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be 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. 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 double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page.
Please visit the Instructions for Authors page before submitting a manuscript. Submitted papers should be well formatted and written in clear, concise English and should contain all essential data in order to make the presentation clear and the results of the study replicable. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see
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