Recent Advances in Conotoxin Classification by Using Machine Learning Methods
AbstractConotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer’s disease, Parkinson’s disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Dao, F.-Y.; Yang, H.; Su, Z.-D.; Yang, W.; Wu, Y.; Hui, D.; Chen, W.; Tang, H.; Lin, H. Recent Advances in Conotoxin Classification by Using Machine Learning Methods. Molecules 2017, 22, 1057.
Dao F-Y, Yang H, Su Z-D, Yang W, Wu Y, Hui D, Chen W, Tang H, Lin H. Recent Advances in Conotoxin Classification by Using Machine Learning Methods. Molecules. 2017; 22(7):1057.Chicago/Turabian Style
Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao. 2017. "Recent Advances in Conotoxin Classification by Using Machine Learning Methods." Molecules 22, no. 7: 1057.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.