Dear Colleagues,

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

Guest Editor