With the avalanche of genomic and proteomic data generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively analyzing and predicting the structure, function, and other properties of DNA and protein. Researchers realize the importance of machine learning methods and feature selection algorithms for potential knowledge finding tasks in genomics and proteomics. Recent years have shown tremendous advances in the properties prediction of DNA fragments and protein sequences by various pattern recognition methods. These techniques provide economical and time-saving solutions for identifying the properties of DNA and protein. This special issue will focus on various aspects of the application of machine learning methods in genomics and proteomics bioinformatics. The recent developments on the prediction of protein subcellular localization, posttranslational modification sites, DNA-binding site, protein-protein interaction, nucleosome positioning, transcription factor binding site, exon/intron splice site, translation initiation site, and transcription start site will be included in the special issue.

Potential topics include, but are not limited to:

• Protein folding
• Protein subcellular localization
• Protein posttranslational modification sites
• Protein family
• Binding sites in proteins
• Mechanism of protein-protein interaction
• Gene prediction and annotation
• Transcription factor binding site
• Exon/intron splice site, including alternative splice site
• Translation initiation site and transcription start site
• Noncoding RNA
• Gene function and activity
• Molecular mutation and evolution
• Biomarker