FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2758461185> ?p ?o ?g. }

• W2758461185 endingPage "154" @default.
• W2758461185 startingPage "146" @default.
• W2758461185 abstract "Single Nucleotide Polymorphism (SNPs) are, nowadays, becoming the marker of choice for biological analyses involving a wide range of applications with great medical, biological, economic and environmental interest. Classification tasks i.e. the assignment of individuals to groups of origin based on their (multi-locus) genotypes, are performed in many fields such as forensic investigations, discrimination between wild and/or farmed populations and others. Τhese tasks, should be performed with a small number of loci, for computational as well as biological reasons. Thus, feature selection should precede classification tasks, especially for Single Nucleotide Polymorphism (SNP) datasets, where the number of features can amount to hundreds of thousands or millions.In this paper, we present a novel data mining approach, called FIFS - Frequent Item Feature Selection, based on the use of frequent items for selection of the most informative markers from population genomic data. It is a modular method, consisting of two main components. The first one identifies the most frequent and unique genotypes for each sampled population. The second one selects the most appropriate among them, in order to create the informative SNP subsets to be returned.The proposed method (FIFS) was tested on a real dataset, which comprised of a comprehensive coverage of pig breed types present in Britain. This dataset consisted of 446 individuals divided in 14 sub-populations, genotyped at 59,436 SNPs. Our method outperforms the state-of-the-art and baseline methods in every case. More specifically, our method surpassed the assignment accuracy threshold of 95% needing only half the number of SNPs selected by other methods (FIFS: 28 SNPs, Delta: 70 SNPs Pairwise FST: 70 SNPs, In: 100 SNPs.) CONCLUSION: Our approach successfully deals with the problem of informative marker selection in high dimensional genomic datasets. It offers better results compared to existing approaches and can aid biologists in selecting the most informative markers with maximum discrimination power for optimization of cost-effective panels with applications related to e.g. species identification, wildlife management, and forensics." @default.
• W2758461185 created "2017-10-06" @default.
• W2758461185 creator A5006991763 @default.
• W2758461185 creator A5016044543 @default.
• W2758461185 creator A5022128097 @default.
• W2758461185 creator A5066340205 @default.
• W2758461185 date "2017-11-01" @default.
• W2758461185 modified "2023-09-26" @default.
• W2758461185 title "FIFS: A data mining method for informative marker selection in high dimensional population genomic data" @default.
• W2758461185 cites W1541474912 @default.
• W2758461185 cites W1850594542 @default.
• W2758461185 cites W1886329524 @default.
• W2758461185 cites W1944632735 @default.
• W2758461185 cites W1980566852 @default.
• W2758461185 cites W2003031227 @default.
• W2758461185 cites W2012455821 @default.
• W2758461185 cites W2013483531 @default.
• W2758461185 cites W2024027525 @default.
• W2758461185 cites W2035596487 @default.
• W2758461185 cites W2047417387 @default.
• W2758461185 cites W2050756209 @default.
• W2758461185 cites W2058401000 @default.
• W2758461185 cites W2075923653 @default.
• W2758461185 cites W2081930221 @default.
• W2758461185 cites W2116782829 @default.
• W2758461185 cites W2118769566 @default.
• W2758461185 cites W2121996702 @default.
• W2758461185 cites W2127965912 @default.
• W2758461185 cites W2128195336 @default.
• W2758461185 cites W2129853163 @default.
• W2758461185 cites W2138658821 @default.
• W2758461185 cites W2149799970 @default.
• W2758461185 cites W2155309921 @default.
• W2758461185 cites W2160717298 @default.
• W2758461185 cites W2161633633 @default.
• W2758461185 cites W2417875227 @default.
• W2758461185 cites W2998574808 @default.
• W2758461185 doi "https://doi.org/10.1016/j.compbiomed.2017.09.020" @default.
• W2758461185 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28992453" @default.
• W2758461185 hasPublicationYear "2017" @default.
• W2758461185 type Work @default.
• W2758461185 sameAs 2758461185 @default.
• W2758461185 citedByCount "10" @default.
• W2758461185 countsByYear W27584611852018 @default.
• W2758461185 countsByYear W27584611852019 @default.
• W2758461185 countsByYear W27584611852020 @default.
• W2758461185 countsByYear W27584611852021 @default.
• W2758461185 countsByYear W27584611852022 @default.
• W2758461185 countsByYear W27584611852023 @default.
• W2758461185 crossrefType "journal-article" @default.
• W2758461185 hasAuthorship W2758461185A5006991763 @default.
• W2758461185 hasAuthorship W2758461185A5016044543 @default.
• W2758461185 hasAuthorship W2758461185A5022128097 @default.
• W2758461185 hasAuthorship W2758461185A5066340205 @default.
• W2758461185 hasConcept C104317684 @default.
• W2758461185 hasConcept C124101348 @default.
• W2758461185 hasConcept C135763542 @default.
• W2758461185 hasConcept C139275648 @default.
• W2758461185 hasConcept C148483581 @default.
• W2758461185 hasConcept C153209595 @default.
• W2758461185 hasConcept C154945302 @default.
• W2758461185 hasConcept C2908647359 @default.
• W2758461185 hasConcept C41008148 @default.
• W2758461185 hasConcept C54355233 @default.
• W2758461185 hasConcept C71924100 @default.
• W2758461185 hasConcept C81917197 @default.
• W2758461185 hasConcept C84597430 @default.
• W2758461185 hasConcept C86803240 @default.
• W2758461185 hasConcept C99454951 @default.
• W2758461185 hasConceptScore W2758461185C104317684 @default.
• W2758461185 hasConceptScore W2758461185C124101348 @default.
• W2758461185 hasConceptScore W2758461185C135763542 @default.
• W2758461185 hasConceptScore W2758461185C139275648 @default.
• W2758461185 hasConceptScore W2758461185C148483581 @default.
• W2758461185 hasConceptScore W2758461185C153209595 @default.
• W2758461185 hasConceptScore W2758461185C154945302 @default.
• W2758461185 hasConceptScore W2758461185C2908647359 @default.
• W2758461185 hasConceptScore W2758461185C41008148 @default.
• W2758461185 hasConceptScore W2758461185C54355233 @default.
• W2758461185 hasConceptScore W2758461185C71924100 @default.
• W2758461185 hasConceptScore W2758461185C81917197 @default.
• W2758461185 hasConceptScore W2758461185C84597430 @default.
• W2758461185 hasConceptScore W2758461185C86803240 @default.
• W2758461185 hasConceptScore W2758461185C99454951 @default.
• W2758461185 hasLocation W27584611851 @default.
• W2758461185 hasLocation W27584611852 @default.
• W2758461185 hasOpenAccess W2758461185 @default.
• W2758461185 hasPrimaryLocation W27584611851 @default.
• W2758461185 hasRelatedWork W1983039539 @default.
• W2758461185 hasRelatedWork W1984103522 @default.
• W2758461185 hasRelatedWork W2000823134 @default.
• W2758461185 hasRelatedWork W2045539875 @default.
• W2758461185 hasRelatedWork W2108799808 @default.
• W2758461185 hasRelatedWork W2122380662 @default.
• W2758461185 hasRelatedWork W2148996710 @default.
• W2758461185 hasRelatedWork W2367756009 @default.
• W2758461185 hasRelatedWork W2406362019 @default.
• W2758461185 hasRelatedWork W2483370043 @default.

• next