About: Improved Prediction of MHC Class I Binders/Non-Binders Peptides Through Artificial Neural Network Using Variable Learning Rate: SARS Corona Virus, a Case Study

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About: Improved Prediction of MHC Class I Binders/Non-Binders Peptides Through Artificial Neural Network Using Variable Learning Rate: SARS Corona Virus, a Case Study Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Improved Prediction of MHC Class I Binders/Non-Binders Peptides Through Artificial Neural Network Using Variable Learning Rate: SARS Corona Virus, a Case Study
Creator	Arabnia, H Bhasker, Bharat Mishra, B Nath, Bhartendu Soam, Sudhir
source	Medline; PMC
abstract	Fundamental step of an adaptive immune response to pathogen or vaccine is the binding of short peptides (also called epitopes) to major histocompatibility complex (MHC) molecules. The various prediction algorithms are being used to capture the MHC peptide binding preference, allowing the rapid scan of entire pathogen proteomes for peptide likely to bind MHC, saving the cost, effort, and time. However, the number of known binders/non-binders (BNB) to a specific MHC molecule is limited in many cases, which still poses a computational challenge for prediction. The training data should be adequate to predict BNB using any machine learning approach. In this study, variable learning rate has been demonstrated for training artificial neural network and predicting BNB for small datasets. The approach can be used for large datasets as well. The dataset for different MHC class I alleles for SARS Corona virus (Tor2 Replicase polyprotein 1ab) has been used for training and prediction of BNB. A total of 90 datasets (nine different MHC class I alleles with tenfold cross validation) have been retrieved from IEDB database for BNB. For fixed learning rate approach, the best value of AROC is 0.65, and in most of the cases it is 0.5, which shows the poor predictions. In case of variable learning rate, of the 90 datasets the value of AROC for 76 datasets is between 0.806 and 1.0 and for 7 datasets the value is between 0.7 and 0.8 and for rest of 7 datasets it is between 0.5 and 0.7, which indicates very good performance in most of the cases.
has issue date	2010-11-13(xsd:dateTime)
bibo:doi	10.1007/978-1-4419-7046-6_22
bibo:pmid	21431562
has license	no-cc
sha1sum (hex)	6b1685f9fa1420213ccd261454c50ab930682100
schema:url	https://doi.org/10.1007/978-1-4419-7046-6_22
resource representing a document's title	Improved Prediction of MHC Class I Binders/Non-Binders Peptides Through Artificial Neural Network Using Variable Learning Rate: SARS Corona Virus, a Case Study
has PubMed Central identifier	PMC7123181
has PubMed identifier	21431562
schema:publication	Software Tools and Algorithms for Biological Systems
resource representing a document's body	covid:6b1685f9fa1420213ccd261454c50ab930682100#body_text
is schema:about of	named entity 'polyprotein' named entity 'large' named entity 'training' named entity 'pathogen' named entity 'predict' named entity 'time' named entity 'Prediction' named entity 'PEPTIDE' named entity 'SHORT' named entity 'CORONA' named entity 'ARTIFICIAL NEURAL NETWORK' named entity 'MAJOR HISTOCOMPATIBILITY COMPLEX' named entity 'ADEQUATE' named entity '806' named entity 'VACCINE' named entity 'USED' named entity 'VARIABLE' named entity 'PERFORMANCE' named entity 'RESPONSE TO' named entity 'DATASET' named entity 'MACHINE LEARNING' named entity 'PEPTIDES' named entity 'SCAN' named entity 'FIXED' named entity 'LEARNING RATE' named entity 'PREDICTION' named entity 'ALLELES' named entity 'LIMITED' named entity 'ADAPTIVE IMMUNE RESPONSE' named entity 'BINDERS' named entity 'COST' named entity 'VARIOUS' named entity 'BEING' named entity 'VERY GOOD' named entity 'ARTIFICIAL NEURAL NETWORK' named entity 'VARIABLE' named entity 'RAPID' named entity '0.65' named entity 'BIND' named entity 'MHC CLASS I' named entity 'PEPTIDES' named entity 'BINDERS' named entity 'CASE STUDY' named entity 'IMPROVED' named entity 'SARS' named entity 'BINDING' named entity 'HAVE' named entity 'STEP' named entity 'DATABASE' named entity 'PREFERENCE' named entity 'TRAINING' named entity 'TIME' named entity 'EPITOPES' named entity 'PREDICTION' named entity 'VIRUS' named entity 'PREDICTING' named entity 'TOTAL' named entity 'CORONA' named entity 'USING' named entity 'STUDY' named entity 'SMALL' named entity 'MOLECULES' named entity 'PATHOGEN' named entity '0.8' named entity 'PREDICT' named entity 'PROTEOMES' named entity 'VIRUS' named entity 'CAPTURE' named entity 'PEPTIDE BINDING' named entity 'SPECIFIC' named entity 'MHC' named entity 'MHC CLASS I' named entity 'CROSS VALIDATION' named entity 'USING' named entity 'MOLECULE' named entity 'POOR' named entity 'ALLOWING'

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