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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...

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書誌詳細
出版年:Proteins
主要な著者: Faraggi, Eshel, Zhou, Yaoqi, Kloczkowski, Andrzej
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
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