Using pseudo amino acid composition and binary-tree support vector machines to predict protein structural classes
β Scribed by T.-L. Zhang; Y.-S. Ding
- Publisher
- Springer
- Year
- 2007
- Tongue
- English
- Weight
- 90 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0939-4451
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
## Abstract Using the pseudo amino acid (PseAA) composition to represent the sample of a protein can incorporate a considerable amount of sequence pattern information so as to improve the prediction quality for its structural or functional classification. However, how to optimally formulate the Pse
## Abstract The structural class is an important feature widely used to characterize the overall folding type of a protein. How to improve the prediction quality for protein structural classification by effectively incorporating the sequenceβorder effects is an important and challenging problem. Ba
## Abstract The proteins structure can be mainly classified into four classes: allβΞ±, all**β**Ξ², Ξ±/Ξ², and Ξ± + Ξ² protein according to their chain fold topologies. For the purpose of predicting the protein structural class, a new predicting algorithm, in which the increment of diversity combines with