02382nam 2200361 450 991059790550332120230329072337.0(CKB)4920000000095125(NjHacI)994920000000095125(EXLCZ)99492000000009512520230329d2018 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierSpecial Protein Molecules Computational Identification /edited by Quan ZouBasel, Switzerland :MDPI - Multidisciplinary Digital Publishing Institute,2018.1 online resource (308 pages)3-03897-043-3 It is time consuming and costly to detect new molecules of some special proteins. These special proteins include cytokines, enzymes, cell-penetrating peptides, anticancer peptides, cancer lectins, G-protein-coupled receptors, etc. Researchers often employ computer programs to list some candidates, and to validate the candidates with molecular experiments. These computer programs are key to possible savings on wet experiment costs. Software results with high false positive will lead to high costs in the validation process. In this Special Issue, we 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 machines, 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 molecule, collect related data sets, obtain better prediction performance (especially low false positives), and develop user-friendly software tools or web servers.Computational chemistryPeriodicalsProteinsComputational chemistryProteins.547.75Zou QuanNjHacINjHaclBOOK9910597905503321Special Protein Molecules Computational Identification2929134UNINA