Peptide Binding at Class I Major Histocompatibility Complex Scored with Linear Functions and Support Vector Machines
スポンサーリンク
概要
- 論文の詳細を見る
We explore two different methods to predict the binding ability of nonapeptides at the class I major histocompatibility complex using a general linear scoring function that defines a separating hyperplane in the feature space of sequences. In absence of suitable data on non-binding nonapeptides we generated sequences randomly from a selected set of proteins from the protein data bank. The parameters of the scoring function were determined by a generalized least square optimization (LSM) and alternatively by the support vector machine (SVM). With the generalized LSM impaired data for learning with a small set of binding peptides and a large set of non-binding peptides can be treated in a balanced way rendering LSM more successful than SVM, while for symmetric data sets SVM has a slight advantage compared to LSM.
- 日本バイオインフォマティクス学会の論文
日本バイオインフォマティクス学会 | 論文
- Performance Improvement in Protein N-Myristoyl Classification by BONSAI with Insignificant Indexing Symbol
- A combined pathway to simulate CDK-dependent phosphorylation and ARF-dependent stabilization for p53 transcriptional activity
- A versatile petri net based architecture for modeling and simulation of complex biological processes
- XML documentation of biopathways and their simulations in Genomic Object Net
- Prediction of debacle points for robustness of biological pathways by using recurrent neural networks