GSNFS: Gene subnetwork biomarker identification of lung cancer expression data

Abstract

BackgroundGene expression has been used to identify disease gene biomarkers, but there are ongoing challenges. Single gene or gene-set biomarkers are inadequate to provide sufficient understanding of complex disease mechanisms and the relationship among those genes. Network-based methods have thus been considered for inferring the interaction within a group of genes to further study the disease mechanism. Recently, the Gene-Network-based Feature Set (GNFS), which is capable of handling case-control and multiclass expression for gene biomarker identification, has been proposed, partly taking into account of network topology. However, its performance relies on a greedy search for building subnetworks and thus requires further improvement. In this work, we establish a new approach named Gene Sub-Network-based Feature Selection (GSNFS) by implementing the GNFS framework with two proposed searching and scoring algorithms, namely gene-set-based (GS) search and parent-node-based (PN) search, to identify subnetworks. An additional dataset is used to validate the results.MethodsThe two proposed searching algorithms of the GSNFS method for subnetwork expansion are concerned with the degree of connectivity and the scoring scheme for building subnetworks and their topology. For each iteration of expansion, the neighbour genes of a current subnetwork, whose expression data improved the overall subnetwork score, is recruited. While the GS search calculated the subnetwork score using an activity score of a current subnetwork and the gene expression values of its neighbours, the PN search uses the expression value of the corresponding parent of each neighbour gene. Four lung cancer expression datasets were used for subnetwork identification. In addition, using pathway data and protein-protein interaction as network data in order to consider the interaction among significant genes were discussed. Classification was performed to compare the performance of the identified gene subnetworks with three subnetwork identification algorithms.ResultsThe two searching algorithms resulted in better classification and gene/gene-set agreement compared to the original greedy search of the GNFS method. The identified lung cancer subnetwork using the proposed searching algorithm resulted in an improvement of the cross-dataset validation and an increase in the consistency of findings between two independent datasets. The homogeneity measurement of the datasets was conducted to assess dataset compatibility in cross-dataset validation. The lung cancer dataset with higher homogeneity showed a better result when using the GS search while the dataset with low homogeneity showed a better result when using the PN search. The 10-fold cross-dataset validation on the independent lung cancer datasets showed higher classification performance of the proposed algorithms when compared with the greedy search in the original GNFS method.ConclusionsThe proposed searching algorithms provide a higher number of genes in the subnetwork expansion step than the greedy al...

Gene Expression Dataset Classification Using Artificial Neural Network and Clustering-Based Feature Selection

“…Ramyachitra,et al,(2015)classifiedresultantdatasetsusingexistingtechniquesthenImprovised InternalValueClassificationwithPSO(IVPSO)forfeatureselection.Theoutcomeoftheresult ofIVPSOoutperformedotheralgorithmsundersomeperformanceevaluationfunctionstestedon eighteensampledatasets. Yuan,et al,(2016) Narumol,et al,(2016)builtupanapproachbyactualizingtheGeneNetworkbasedFeature Set(GNFS)systemwiththepresentedgene-set-centered(GS)searchinadditiontoparent-nodecentered(PN)searchalgorithms,todistinguishsub-networks,labeledGeneSub-Network-centered FeatureSelection(GSNFS).Toapprovetheoutcomes,theresearchersutilizedanextradataset.The twopresentedsearchingalgorithmsaimedatthesub-networkdevelopmentwereworriedaboutthe connectivityleveltogetherwiththescoringplanaimedatconstructingthesub-networksalongwith theirtopology.Aimedateverycycleoftheextension,theneighborgenesofapresentsub-network, whoseexpressiondataenhancedthegeneralsub-networkscore,wasenrolled.WhilsttheGSsearch figuredthesub-networkscoreutilizinganactionscoreofapresentsubnetworkinadditiontheGE valuesofitsneighbors,thePNseekutilizedthematchingparent'sexpressionvalueofeveryneighbor gene.Aimedatsub-networkidentification,fourcancerexpressiondatasetwereutilized.Additionally, utilizingpathwaydatabesidesproteinwithproteincommunicationasnetworkdatawiththeintentionof regardingtheinteractionamongstimportantgeneswerediscuss.Theclassificationwasdonetocontrast thedistinguishedgenesubnetworksperformancewiththe3sub-networkrecognitionalgorithms. Hala,et al,(2016),analyzedmicroarraydatasetswiththeaidofABCalgorithmandproposed aclassificationmodelwiththeaidofSVMtermed(ABC-SVM).Experimentationofresultofthe proposedmethod onsixbinaryandmulticlass microarraydatasets showedapromising wayfor selectinggenefeaturesandcancerclassificationwithgoodclassificationaccuracyaccompaniedby lowestaveragegeneselectedaftercomparisonwithothernormalswarmintelligencetechniques.…”

Identification of Lung Cancer Related Genes Using Enhanced Floyd Warshall Algorithm in a Protein to Protein Interaction Network

“…Doungpan, W. Engchuan, J.H. Chan, and A. Meechai, [15] illustrated the two search algorithms for Gene Sub-Network-Based Feature Selection (GSNFS) for subnetwork expansion with the degree of connectivity and scoring scheme for building an effective subnet works and topology. For each iteration, the neighbourhood genes of current subnet work were recruited.…”

pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks

“…For the identification of active subnetworks, various algorithms have been proposed, such as greedy algorithms (Breitling et al, 2004; Sohler et al, 2004; Chuang et al, 2007; Nacu et al, 2007; Ulitsky and Shamir, 2007; Karni et al, 2009; Ulitsky and Shamir, 2009; Fortney et al, 2010; Doungpan et al, 2016), simulated annealing (Ideker et al, 2002; Guo et al, 2007), genetic algorithms (Klammer et al, 2010; Ma et al, 2011; Wu et al, 2011; Amgalan and Lee, 2014; Ozisik et al, 2017), and mathematical programming-based methods (Dittrich et al, 2008; Zhao et al, 2008; Qiu et al, 2009; Backes et al, 2012; Beisser et al, 2012). In pathfindR, we provide implementations for a greedy algorithm, a simulated annealing algorithm, and a genetic algorithm.…”