Special Issue "Special Protein Molecules Computational Identification"
Deadline for manuscript submissions: closed (15 October 2017) | Viewed by 91984
A printed edition of this Special Issue is available here.
Interests: bioinformatics; machine learning; string algorithm
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For some special protein molecules, it is time consuming and costly to detect new ones. These special proteins include cytokines, enzymes, cell-penetrating peptides, anticancer peptides, cancerlectins, G protein-coupled receptors, etc. Researchers often employed computer programs to list some candidates, and validated the candidates with molecular experiments. The “computer program” is the key issue, which could save on wet experiments costs. High false positive software would lead to high costs in the validation process.
In this Special Issue, we will focus on these “computer program” approaches and algorithms. Some “golden features” from protein primary sequences have been proposed for these problems, such as Chou’s PseAAC (Pseudo Amino Acid Composition). PseAAC has been tried on nearly all kinds of protein identification, together with SVM (support vector machine, a type of classifier). However, I prefer special features, and classification methods should be proposed for special protein molecules. “Golden features” cannot work well on all kinds of proteins. I hope that submissions will focus on a type of special protein molecules, collect related data sets, get better prediction performance (especially low false positives), and develop user-friendly software tools or web servers.
Prof. Quan Zou
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type III secreted proteins
DNA/RNA binding proteins
MHC binding peptide