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<>
	foaf:primaryTopic <http://crdd.osdd.net/open/id/eprint/3387>;
	rdfs:comment "The repository administrator has not yet configured an RDF license."^^xsd:string .

<http://crdd.osdd.net/open/3387/>
	dc:format "text/html";
	dc:title "HTML Summary of #3387 \n\nComputational method for aromatase-related proteins using machine learning approach\n\n";
	foaf:primaryTopic <http://crdd.osdd.net/open/id/eprint/3387> .

<http://crdd.osdd.net/open/id/eprint/3387#authors>
	rdf:_1 <http://crdd.osdd.net/open/id/person/ext-97ab54a3c8a07032b7a63b0a4fe1fd8d>;
	rdf:_2 <http://crdd.osdd.net/open/id/person/ext-2fd016991a0935f4369d624dfb9defd2> .

<http://crdd.osdd.net/open/id/eprint/3387>
	bibo:abstract "Human aromatase enzyme is a microsomal cytochrome P450 and catalyzes aromatization of androgens into estrogens during steroidogenesis. For breast cancer therapy, third-generation aromatase inhibitors (AIs) have proven to be effective; however patients acquire resistance to current AIs. Thus there is a need to predict aromatase-related proteins to develop efficacious AIs. A machine learning method was established to identify aromatase-related proteins using a five-fold cross validation technique. In this study, different SVM approach-based models were built using the following approaches like amino acid, dipeptide composition, hybrid and evolutionary profiles in the form of position-specific scoring matrix (PSSM); with maximum accuracy of 87.42%, 84.05%, 85.12%, and 92.02% respectively. Based on the primary sequence, the developed method is highly accurate to predict the aromatase-related proteins. Prediction scores graphs were developed using the known dataset to check the performance of the method. Based on the approach described above, a webserver for predicting aromatase-related proteins from primary sequence data was developed and implemented at https://bioinfo.imtech.res.in/servers/muthu/aromatase/home.html. We hope that the developed method will be useful for aromatase protein related research."^^xsd:string;
	bibo:authorList <http://crdd.osdd.net/open/id/eprint/3387#authors>;
	bibo:issue "3";
	bibo:status <http://purl.org/ontology/bibo/status/peerReviewed>,
		<http://purl.org/ontology/bibo/status/published>;
	bibo:volume "18";
	dct:creator <http://crdd.osdd.net/open/id/person/ext-2fd016991a0935f4369d624dfb9defd2>,
		<http://crdd.osdd.net/open/id/person/ext-97ab54a3c8a07032b7a63b0a4fe1fd8d>;
	dct:date "2023-03-29";
	dct:isPartOf <http://crdd.osdd.net/open/id/repository>,
		<http://crdd.osdd.net/open/id/publication/ext-ceb82ca513fc49ffe2f82b12464bffce>;
	dct:publisher <http://crdd.osdd.net/open/id/org/ext-84f4587a644da6d2d4b26d0b5e3740a7>;
	dct:subject <http://crdd.osdd.net/open/id/subject/QR>;
	dct:title "Computational method for aromatase-related proteins using machine learning approach"^^xsd:string;
	rdf:type bibo:AcademicArticle,
		bibo:Article,
		ep:ArticleEPrint,
		ep:EPrint;
	rdfs:seeAlso <http://crdd.osdd.net/open/3387/> .

<http://crdd.osdd.net/open/id/subject/QR>
	rdf:type skos:Concept;
	skos:prefLabel "QR Microbiology"@en .

<http://crdd.osdd.net/open/id/org/ext-84f4587a644da6d2d4b26d0b5e3740a7>
	foaf:name "Public Library of Science (PLoS)"^^xsd:string;
	rdf:type foaf:Organization .

<http://crdd.osdd.net/open/id/person/ext-2fd016991a0935f4369d624dfb9defd2>
	foaf:familyName "Kaur"^^xsd:string;
	foaf:givenName "Jasmeet"^^xsd:string;
	foaf:name "Jasmeet Kaur"^^xsd:string;
	rdf:type foaf:Person .

<http://crdd.osdd.net/open/id/person/ext-97ab54a3c8a07032b7a63b0a4fe1fd8d>
	foaf:familyName "Selvaraj"^^xsd:string;
	foaf:givenName "Muthu Krishnan"^^xsd:string;
	foaf:name "Muthu Krishnan Selvaraj"^^xsd:string;
	rdf:type foaf:Person .

<http://crdd.osdd.net/open/id/publication/ext-ceb82ca513fc49ffe2f82b12464bffce>
	foaf:name "PLoS One"^^xsd:string;
	rdf:type bibo:Collection .