Dear Colleagues,

The discovery of new molecules remains 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 have often employed computer programs to list some candidates and validated the candidates using molecular experiments. The “computer program” used is a key issue, which could cut wet experiment costs. High false positive software would lead to high costs in the validation process.

In addition to proteins, we encourage authors to pay attention to noncoding RNA molecules. MicroRNA and other noncoding RNA detections are still openly challenging for bioinformatic researchers. A perfect performance could remove the cost of Northern Blot or rtPCR. RNA function and the RNA–disease relationship are also interesting and welcome. Some network methods, including random walk and matrix factorization, have been employed in the RNA–disease relationship prediction. However, they are not robust. Sometimes, state-of-the-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. Dr. Quan Zou
Dr. Yijie Ding
Guest Editors

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• bioinformatics
• machine learning
• feature selection
• protein classification
• PseAAC features
• anticancer peptides
• cell-penetrating peptides
• oncogene
• DNA/RNA binding proteins
• MHC binding peptide
• noncoding RNA
• microRNA
• RNA–disease relationship
• network