Protein fold classification with Grow-and-Learn network
نویسندگان
چکیده
منابع مشابه
Classifying Brain Prints Using Grow And Learn Network
In this paper, a method to recognise persons using brain signal features classified by Grow and Learn (GAL) network is proposed. The features are obtained from brain signals and consist of gamma band spectral power. These brain signals are recorded from 61 electrodes located on the human scalp while the subjects are seeing a visual stimulus in the form of a picture. The experimental results usi...
متن کاملMelody discrimination and protein fold classification
One of the greatest challenges in theoretical biophysics and bioinformatics is the identification of protein folds from sequence data. This can be regarded as a pattern recognition problem. In this paper we report the use of a melody generation software where the inputs are derived from calculations of evolutionary information, secondary structure, flexibility, hydropathy and solvent accessibil...
متن کاملProtein fold Classification with Genetic Algorithms and Feature Selection
Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A sup...
متن کاملFold-specific substitution matrices for protein classification
MOTIVATION Methods that focus on secondary structures, such as Position Specific Scoring Matrices and Hidden Markov Models, have proved useful for assigning proteins to families. However, for assigning proteins to an attribute class within a family these methods may introduce more free parameters than are needed. There are fewer members and there is less variability among sequences within a fam...
متن کاملFeature Selection and Combination Methods for Protein Fold Classification
In this paper, we propose a feature selection and combination method for protein fold classification. To ensure the number of selected features is minimal while the most discriminant information is preserved, we propose a filtering method based on average information gain. For protein fold classification, we follow the hierarchical scheme in the previous study. That is, a sequence is first clas...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES
سال: 2017
ISSN: 1300-0632,1303-6203
DOI: 10.3906/elk-1506-126