Use of adaptive networks to define highly predictable protein secondary-structure classes
نویسندگان
چکیده
منابع مشابه
Neural Network Definitions of Highly Predictable Protein Secondary Structure Classes
We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computationa...
متن کاملNeural Network Definition of Highly Predictable Protein Secondary Structure Classes
We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computationa...
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Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by us...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1995
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00993381