ロード中...
Accurate single-sequence prediction of solvent accessible surface area using local and global features
We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window infor...
保存先:
| 出版年: | Proteins |
|---|---|
| 主要な著者: | , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
2014
|
| 主題: | |
| オンライン・アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4307928/ https://ncbi.nlm.nih.gov/pubmed/25204636 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/prot.24682 |
| タグ: |
タグ追加
タグなし, このレコードへの初めてのタグを付けませんか!
|