About: Alignment using genetic programming with causal trees for identification of protein functions

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Alignment using genetic programming with causal trees for identification of protein functions
Creator	Chang, Ming-Shi Huang, Yueh-Min Hung, Chun-Min
source	Elsevier; Medline; PMC
abstract	A hybrid evolutionary model is used to propose a hierarchical homology of protein sequences to identify protein functions systematically. The proposed model offers considerable potentials, considering the inconsistency of existing methods for predicting novel proteins. Because some novel proteins might align without meaningful conserved domains, maximizing the score of sequence alignment is not the best criterion for predicting protein functions. This work presents a decision model that can minimize the cost of making a decision for predicting protein functions using the hierarchical homologies. Particularly, the model has three characteristics: (i) it is a hybrid evolutionary model with multiple fitness functions that uses genetic programming to predict protein functions on a distantly related protein family, (ii) it incorporates modified robust point matching to accurately compare all feature points using the moment invariant and thin-plate spline theorems, and (iii) the hierarchical homologies holding up a novel protein sequence in the form of a causal tree can effectively demonstrate the relationship between proteins. This work describes the comparisons of nucleocapsid proteins from the putative polyprotein SARS virus and other coronaviruses in other hosts using the model.
has issue date	2006-09-01(xsd:dateTime)
bibo:doi	10.1016/j.na.2005.09.048
bibo:pmid	32288048
has license	no-cc
sha1sum (hex)	0bccc69fb138e2f1ffc9a510195be229486ef8ce
schema:url	https://doi.org/10.1016/j.na.2005.09.048
resource representing a document's title	Alignment using genetic programming with causal trees for identification of protein functions
has PubMed Central identifier	PMC7117053
has PubMed identifier	32288048
schema:publication	Nonlinear Anal Theory Methods Appl
resource representing a document's body	covid:0bccc69fb138e2f1ffc9a510195be229486ef8ce#body_text
is schema:about of	named entity 'matching' named entity 'coronaviruses' named entity 'model' named entity 'predicting' named entity 'proposed' named entity 'causal' named entity 'ALIGNMENT' named entity 'FUNCTIONS' named entity 'COST' named entity 'PROTEIN SEQUENCES' named entity 'PROTEINS' named entity 'GENETIC PROGRAMMING' named entity 'TREES' named entity 'PROTEIN ' named entity 'POTENTIALS' named entity 'NOVEL' named entity 'MULTIPLE' named entity 'USED' named entity 'PREDICTING' named entity 'BEST' named entity 'POLYPROTEIN' named entity 'SEQUENCE ALIGNMENT' named entity 'TREE' named entity 'RELATIONSHIP' covid:arg/0bccc69fb138e2f1ffc9a510195be229486ef8ce named entity 'CORONAVIRUSES' named entity 'PROPOSED' named entity 'MATCHING' named entity 'PRESENTS' named entity 'CONSERVED' named entity 'CHARACTERISTICS' named entity 'PROTEIN SEQUENCE' named entity 'FUNCTIONS' named entity 'HOSTS' named entity 'USING' named entity 'PROTEIN ' named entity 'FEATURE' named entity 'HYBRID' named entity 'EVOLUTIONARY MODEL' named entity 'DECISION' named entity 'FITNESS' named entity 'PLATE' named entity 'MODEL' named entity 'IDENTIFICATION' named entity 'ALIGN' named entity 'HOMOLOGY' named entity 'USES' named entity 'GENETIC PROGRAMMING' named entity 'RELATED' named entity 'DECISION MODEL' named entity 'METHODS' named entity 'MODIFIED' named entity 'HOLDING' named entity '28I' named entity 'USING' named entity 'CONSIDERING' named entity 'POINT' named entity 'NUCLEOCAPSID PROTEINS' named entity 'TO IDENTIFY' named entity 'ROBUST' named entity 'PREDICT' named entity 'COMPARE' named entity 'CONSIDERABLE' named entity 'IS A' named entity 'INVARIANT' named entity 'MOMENT' named entity 'SCORE' named entity 'HIERARCHICAL' named entity 'CRITERION' named entity 'NOVEL PROTEIN' named entity 'SARS VIRUS' named entity 'PROTEIN FAMILY' named entity 'WORK' named entity 'DISTANTLY' named entity 'FORM' named entity 'points' named entity 'conserved domains' named entity 'modified' named entity 'evolutionary model'

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