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Special Issue "Special Protein or RNA Molecules Computational Identification 2018"
Deadline for manuscript submissions: closed (31 October 2018).
New molecules discovery is still an important and challenging task. For some special proteins or RNA molecules, it is difficult, 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. Some noncoding RNAs are also required to be annotated in the sequencing data, such as microRNA, snoRNA, snRNA, circle RNA, tRNA, etc. Researchers often employed computer programs to list some candidates, and validated the candidates using molecular experiments. The “computer program” is a key issue, which could save on wet experiments costs. High false positive software would lead to high costs in the validation process.
We have successfully organized a related Special Issue last year (see Int. J. Mol. Sci. 2018, 19, 536 as a summary). Eighteen papers were published in the Special Issue. I hope more follow-up study could appear in the new coming issue. For all the new submissions, please state the relationship and differences from the papers in the 2017 Special Issue. You can refer to the Editorial of the last edition Int. J. Mol. Sci. 2018, 19, 536.
In addition to proteins, we encourage authors to pay attention to noncoding RNA molecules. MicroRNA and other noncoding RNA detections are still open challenging for bioinformatic researchers. Perfect performance could save the cost of Northern Blot or rtPCR. RNA function and RNA-disease relationship is also interesting and welcome. Some network methods, including random walk and matrix factorization, were employed in the RNA–disease relationship prediction. However, they are not robust. Sometimes, state-of-art methods would be invalid upon updating the datasets. I hope to see more novel and robust methods and golden benchmark datasets in the new Special Issue.
Prof. Quan Zou
Manuscript Submission Information
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- machine learning
- feature selection
- protein classification
- PseAAC features
- anticancer peptides
- Cell-Penetrating Peptides
- DNA/RNA binding proteins
- MHC binding peptide
- noncoding RNA
- RNA-disease relationship