About: Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions

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About: Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions Goto Sponge NotDistinct Permalink

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	Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions
Creator	Chou, Kuo-Chen Chen, Chen Cai, Yu-Dong Hu, Le-Le Huang, Tao
topic	covid:e344d21fb68d14bed65d7fbdc97164e61717a0f2#this
source	PMC
abstract	Given a compound, how can we effectively predict its biological function? It is a fundamentally important problem because the information thus obtained may benefit the understanding of many basic biological processes and provide useful clues for drug design. In this study, based on the information of chemical-chemical interactions, a novel method was developed that can be used to identify which of the following eleven metabolic pathway classes a query compound may be involved with: (1) Carbohydrate Metabolism, (2) Energy Metabolism, (3) Lipid Metabolism, (4) Nucleotide Metabolism, (5) Amino Acid Metabolism, (6) Metabolism of Other Amino Acids, (7) Glycan Biosynthesis and Metabolism, (8) Metabolism of Cofactors and Vitamins, (9) Metabolism of Terpenoids and Polyketides, (10) Biosynthesis of Other Secondary Metabolites, (11) Xenobiotics Biodegradation and Metabolism. It was observed that the overall success rate obtained by the method via the 5-fold cross-validation test on a benchmark dataset consisting of 3,137 compounds was 77.97%, which is much higher than 10.45%, the corresponding success rate obtained by the random guesses. Besides, to deal with the situation that some compounds may be involved with more than one metabolic pathway class, the method presented here is featured by the capacity able to provide a series of potential metabolic pathway classes ranked according to the descending order of their likelihood for each of the query compounds concerned. Furthermore, our method was also applied to predict 5,549 compounds whose metabolic pathway classes are unknown. Interestingly, the results thus obtained are quite consistent with the deductions from the reports by other investigators. It is anticipated that, with the continuous increase of the chemical-chemical interaction data, the current method will be further enhanced in its power and accuracy, so as to become a useful complementary vehicle in annotating uncharacterized compounds for their biological functions.
has issue date	2011-12-29(xsd:dateTime)
bibo:doi	10.1371/journal.pone.0029491
bibo:pmid	22220213
has license	cc-by
sha1sum (hex)	e344d21fb68d14bed65d7fbdc97164e61717a0f2
schema:url	https://doi.org/10.1371/journal.pone.0029491
resource representing a document's title	Predicting Biological Functions of Compounds Based on Chemical-Chemical Interactions
has PubMed Central identifier	PMC3248422
has PubMed identifier	22220213
schema:publication	PLoS One
resource representing a document's body	covid:e344d21fb68d14bed65d7fbdc97164e61717a0f2#body_text
is http://vocab.deri.ie/void#inDataset of	https://covidontheweb.inria.fr:4443/about/id/http/ns.inria.fr/covid19/e344d21fb68d14bed65d7fbdc97164e61717a0f2
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