Achieving 80% ten‐fold cross‐validated accuracy for secondary structure prediction by large‐scale training
An integrated system of neural networks, called SPINE, is established and optimized for
predicting structural properties of proteins. SPINE is applied to three‐state secondary …
predicting structural properties of proteins. SPINE is applied to three‐state secondary …
An integrated network topology and deep learning model for prediction of Alzheimer disease candidate genes
NS Gnanadesigan, N Dhanasegar, MD Ramasamy… - Soft Computing, 2023 - Springer
Alzheimer's disease (AD) is a neurological illness that causes short-term memory loss.
There are currently no viable therapeutic therapies for this condition that can cure it. The …
There are currently no viable therapeutic therapies for this condition that can cure it. The …
Prediction of protein folds: extraction of new features, dimensionality reduction, and fusion of heterogeneous classifiers
Here, we consider a two-level (four classes in level 1 and 27 folds in level 2) protein fold
determination problem. We propose several new features and use some existing features …
determination problem. We propose several new features and use some existing features …
A balanced secondary structure predictor
Secondary structure (SS) refers to the local spatial organization of a polypeptide backbone
atoms of a protein. Accurate prediction of SS can provide crucial features to form the next …
atoms of a protein. Accurate prediction of SS can provide crucial features to form the next …
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure
Background Protein secondary structure prediction provides insight into protein function and
is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian …
is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian …
[PDF][PDF] Protein structure prediction using support vector machine
ABSTRACT Support Vector Machine (SVM) is used for predict the protein structural.
Bioinformatics method use to protein structure prediction mostly depends on the amino acid …
Bioinformatics method use to protein structure prediction mostly depends on the amino acid …
A new hybrid coding for protein secondary structure prediction based on primary structure similarity
The coding pattern of protein can greatly affect the prediction accuracy of protein secondary
structure. In this paper, a novel hybrid coding method based on the physicochemical …
structure. In this paper, a novel hybrid coding method based on the physicochemical …
Enhanced artificial neural network for protein fold recognition and structural class prediction
P Sudha, D Ramyachitra, P Manikandan - Gene Reports, 2018 - Elsevier
Abstract In Bioinformatics Protein Fold Recognition (PFR) and Structural Class Prediction
(SCP) is a significant problem in predicting protein with a three dimensional structure …
(SCP) is a significant problem in predicting protein with a three dimensional structure …
Methods of constructing biodiverse gene fragment libraries and biological modulators isolated therefrom
PM Watt, WR Thomas, R Hopkins - US Patent 7,803,765, 2010 - Google Patents
US7803765B2 - Methods of constructing biodiverse gene fragment libraries and biological
modulators isolated therefrom - Google Patents US7803765B2 - Methods of constructing …
modulators isolated therefrom - Google Patents US7803765B2 - Methods of constructing …
Physicochemical feature-based classification of amino acid mutations
A huge quantity of gene and protein sequences have become available during the post-
genomic era, and information about genetic variations, including amino acid substitutions …
genomic era, and information about genetic variations, including amino acid substitutions …