HybridGO-Loc: Mining Hybrid Features on Gene Ontology for Predicting Subcellular Localization of ...

12 Pages·2014·0.95 MB·English

HybridGO-Loc: Mining Hybrid Features on Gene
Ontology for Predicting Subcellular Localization of Multi-
Location Proteins
Shibiao Wan1, Man-Wai Mak1*, Sun-Yuan Kung2
1DepartmentofElectronicandInformationEngineering,TheHongKongPolytechnicUniversity,HongKongSAR,China,2DepartmentofElectricalEngineering,Princeton
University,Princeton,NewJersey,UnitedStatesofAmerica
Abstract
Protein subcellular localization prediction, as an essential step to elucidate the functions in vivo of proteins and identify
drugs targets, has been extensively studied in previous decades. Instead of only determining subcellular localization of
single-label proteins, recent studies have focused on predicting both single- and multi-location proteins. Computational
methods based on Gene Ontology (GO) have been demonstrated to be superior to methods based on other features.
However,existingGO-basedmethodsfocusontheoccurrences ofGOtermsanddisregard theirrelationships.Thispaper
proposes a multi-label subcellular-localization predictor, namely HybridGO-Loc, that leverages not only the GO term
occurrencesbutalsotheinter-termrelationships.ThisisachievedbyhybridizingtheGOfrequenciesofoccurrencesandthe
semantic similarity between GO terms. Given a protein, a set of GO terms are retrieved by searching against the gene
ontology database, using the accession numbers of homologous proteins obtained via BLAST search as the keys. The
frequencyofGOoccurrencesandsemanticsimilarity(SS)betweenGOtermsareusedtoformulatefrequencyvectorsand
semantic similarity vectors, respectively, which are subsequently hybridized to construct fusion vectors. An adaptive-
decisionbasedmulti-labelsupportvectormachine(SVM)classifierisproposedtoclassifythefusionvectors.Experimental
resultsbasedonrecentbenchmarkdatasetsandanewdatasetcontainingnovelproteinsshowthattheproposedhybrid-
feature predictor significantly outperforms predictors based on individual GO features as well as other state-of-the-art
predictors. For readers’ convenience, the HybridGO-Loc server, which is for predicting virus or plant proteins, is available
onlineathttp://bioinfo.eie.polyu.edu.hk/HybridGoServer/.
Citation:WanS,MakM-W,KungS-Y(2014)HybridGO-Loc:MiningHybridFeaturesonGeneOntologyforPredictingSubcellularLocalizationofMulti-Location
Proteins.PLoSONE9(3):e89545.doi:10.1371/journal.pone.0089545
Editor:PeterCsermely,SemmelweisUniversity,Hungary
ReceivedNovember11,2013;AcceptedJanuary23,2014;PublishedMarch19,2014
Copyright:(cid:2)2014Wanetal.Thisisanopen-accessarticledistributedunderthetermsoftheCreativeCommonsAttributionLicense,whichpermitsunrestricted
use,distribution,andreproductioninanymedium,providedtheoriginalauthorandsourcearecredited.
Funding:ThisworkwasinpartsupportedbyHKPolyUGrantNos.G-YJ86andG-YL78.Thefundershadnoroleinstudydesign,datacollectionandanalysis,
decisiontopublish,orpreparationofthemanuscript.
CompetingInterests:Theauthorshavedeclaredthatnocompetinginterestsexist.
*E-mail:[email protected]
Introduction with large-scale proteomic data to determine the subcellular
localization of proteins.
Proteins located in appropriate physiological contexts within a
Conventionally, subcellular-localization predictors can be
cell are of paramount importance to exert their biological
roughly divided into sequence-based and annotation-based.
functions. Subcellular localization of proteins is essential to the
Sequence-based methods use (1) amino-acid compositions
functions of proteins and has been suggested as a means to
[10,11], (2) sequence homology [12,13], and (3) sorting signals
maximize functional diversity and economize on protein design
[14,15] as features. Annotation-based menthods use information
and synthesis [1]. Aberrant protein subcellular localization is
beyondtheproteinsequences,suchasGeneOntology(GO)terms
closely correlated to a broad range of human diseases, such as
[16–21],Swiss-Protkeywords[22],andPubMedabstracts[23,24].
Alzheimer’s disease [2], kidney stone [3], primary human liver
A number of studies have demonstrated that methods based on
tumors [4], breast cancer [5], pre-eclampsia [6] and Bartter
GOinformationaresuperiortomethodsbasedonsequence-based
syndrome [7].Knowingwhereaproteinresideswithinacellcan
features [25–28]. Note that the GO database contains not only
give insights on drug targets identification and drug design [8,9].
experimental data but also predicted data (http://www.
Wet-labexperimentssuchasfluorescentmicroscopyimaging,cell
geneontology.org/GO.evidence.shtml),whichmaybedetermined
fractionation and electron microscopy are the gold standard for
by sequence-based methods. From this point of view, the GO-
validating subcellular localization and are essential for the design
based prediction, which uses the GO annotation database to
ofhighqualitylocalizationdatabasessuchasTheHumanProtein
retrieve GO terms, is a filtering method for sequence-based
Atlas (http://www.proteinatlas.org/). However, wet-lab experi-
predictions.
ments are time-consuming and laborious. With the avalanche of
The GO comprises three orthogonal taxonomies whose terms
newly discovered protein sequences in the post-genomic era,
describe the cellular components, biological processes, and
computational methods are required to assist biologists to deal
molecular functions of gene products. The GO terms in each
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HybridGO-Loc
taxonomy are organized within a directed acyclic graph. These similarity between the terms for protein subcellular localization
termsareplacedwithinstructuralrelationships,ofwhichthemost prediction. Compared to existing multi-label subcellular-localiza-
important being the ‘is-a’ relationship (parent and child) and the tion predictors, our proposed predictor has the following
‘part-of’ relationship (part and whole) [29,30]. Recently, the GO advantages: (1) it formulates the feature vectors by hybridizing
consortium has been enriched with more structural relationships, GOfrequencyofoccurrencesandGOsemanticsimilarityfeatures
such as ‘positively-regulates’, ‘negatively-regulates’ and ‘has-part’ whichcontainricherinformationthanonlyGOtermfrequencies;
[31,32]. These relationships reflect that the GO hierarchical tree (2)itadoptsanewstrategytoincorporatericherandmoreuseful
for each taxonomy contains redundant information, for which homologousinformationfrommoredistanthomologsratherthan
semantic similarity overGO termscan befound. using the top homologs only; (3) it adopts an adaptive decision
Instead of only determining subcellular localization of single- strategy for multi-label SVM classifiers so that it can effectively
label proteins, recent studies have been focusing on predicting deal with datasets containing both single-label and multi-label
both single- and multi-location proteins. Since there exist multi- proteins. Results on two recent benchmark datasets and a new
location proteins that can simultaneously reside at, or move dataset containing novel proteins demonstrate that these three
between, two or more subcellular locations, it is important to properties enable the proposed predictor to accurately predict
include these proteins in the predictors. Actually, multi-location multi-location proteins and outperform several state-of-the-art
proteinsplayimportantrolesinsomemetabolicprocessesthattake predictors.
place in more than one cellular compartment, e.g., fatty acid b-
oxidation in the peroxisome and mitochondria, and antioxidant Methods
defense in thecytosol,mitochondriaand peroxisome [33].
Legitimacy of Using GO Information
Recently,severalmulti-labelpredictorsbasedonGOhavebeen
proposed, including Plant-mPLoc [34], Virus-mPLoc [35], iLoc- Despite their good performance, GO-based methods have
Plant [36], iLoc-Virus [37], KNN-SVM [38], mGOASVM [39] receivedsomecriticismsfromtheresearchcommunity.Themain
and others [40,41]. These predictors have demonstrated superi- argument of these criticisms is that the cellular component GO
ority over sequence-based methods. These predictors use the terms already have the cellular component categories, i.e., if the
occurrences of the GO terms but do not take the semantic GOtermsareknown,thesubcelluarlocationswillalsobeknown.
relationships between GOterms intoaccount. Thepredictionproblemcanthereforebeeasilysolvedbycreating
SincetherelationshipbetweenGOtermsreflectstheassociation alookuptableusingthecellularcomponentGOtermsasthekeys
betweendifferentgeneproducts,proteinsequencesannotatedwith andthecellularcomponentcategoriesasthehashedvalues.Sucha
GO terms can be compared on the basis of semantic similarity naive solution, however, will lead to very poor prediction
measures. The semantic similarity over GO has been extensively performance, as demonstrated and explained in our previous
studied and have been applied to many biological problems, studies[28,39].Anumberofstudies[70–72]byothergroupsalso
including protein function prediction [42,43], subnuclear locali- strongly support the legitimacy of using GO information for
zation prediction [44], protein-protein interaction inference [45– subcellular localization. For example, as suggested by [72], the
47] and microarray clustering [48]. The performance of these good performance of GO-based methods is due to the high
predictorsdependsonwhetherthesimilaritymeasureisrelevantto representation power of the GO space as compared to the
the biological problems. Over the years, a number of semantic Euclideanfeaturespacesusedbytheconventionalsequence-based
methods.
similaritymeasureshavebeenproposed,someofwhichhavebeen
used innatural languageprocessing.
Semantic similarity measures can be applied at the GO-term Retrieval of GO Terms
levelorthegene-productlevel.AttheGO-termlevel,methodsare The proposed predictor can use either the accession numbers
roughly categorized as node-based and edge-based. The node- (AC) or amino acid (AA) sequences of query proteins as input.
based measures basically rely on the concept of information Specifically, for proteins with known ACs, their respective GO
content of terms, which was proposed by Resnik [49] for natural terms are retrieved from the Gene Ontology annotation (GOA)
language processing. Later, Lord et al. [50] applied this idea to database (http://www.ebi.ac.uk/GOA) using the ACs as the
measurethesemanticsimilarityamongGOterms.Linetal.[51] searchingkeys.ForproteinswithoutACs,theirAAsequencesare
proposed a method based on information theory and structural presented toBLAST[73] tofindtheir homologs,whose ACsare
information. Subsequently, more node-based measures [52–54] thenusedas keys tosearch against theGOA database.
were proposed. Edge-based measures are based on using the While the GOA database allows us to associate the AC of a
lengthorthedepthofdifferentpathsbetweentermsand/ortheir protein with a set of GO terms, for some novel proteins, neither
common ancestors [55–58]. At the gene-product level, two most their ACs nor theACs of their top homologs have any entries in
commonmethodsarepairwiseapproaches[59–63]andgroupwise theGOAdatabase;inotherwords,noGOtermscanberetrieved
approaches [64–67]. Pairwise approaches measure similarity byusingtheirACsortheACsoftheirtophomologs.Insuchcase,
betweentwogeneproductsbycombiningthesemanticsimilarities the ACs of the homologous proteins, as returned from BLAST
between their terms. Groupwise approaches, on the other hand, search, will be successively used to search against the GOA
directlygrouptheGOtermsofageneproductasaset,agraphor database until a match is found. With the rapid progress of the
a vector, and then calculate the similarity by set similarity GOAdatabase,itisreasonabletoassumethatthehomologsofthe
techniques, graph matching techniques or vector similarity query proteins have at least one GO term [17]. Thus, it is not
techniques. More recently, Pesquita et al. [68] reviewed the necessarytouseback-upmethodstohandlethesituationwhereno
semanticsimilaritymeasuresappliedtobiomedicalontologies,and GO termscanbe found. Theprocedures areoutlined in Fig1.
Guzzi et al. [69] provides a comprehensive review on the
relationship between semantic similarity measures and biological GO Frequency Features
features. LetWdenoteasetofdistinctGOtermscorrespondingtoadata
This paper proposes a multi-label predictor based on hybrid- set. W is constructed in two steps: (1) identifying all of the GO
izing frequency of occurrences of GO terms and semantic terms in the dataset and (2) removing the repetitive GO terms.
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HybridGO-Loc
Figure1.ProceduresofretrievingGOterms. :thei-thqueryprotein;k :themaximumnumberofhomologsretrievedbyBLASTwiththe
i max
defaultparametersetting;Qi,ki:thesetofGOtermsretrievedbyBLASTusingtheki-thhomologforthei-thqueryprotein i;ki:theki-thhomolog
usedtoretrievetheGOterms.
doi:10.1371/journal.pone.0089545.g001
SupposeW distinctGOtermsarefound,i.e.,jWj~W;theseGO contentsofcategoricaldata.Inthecontextofgeneontology,the
terms forma GO Euclidean space with W dimensions. For each semanticsimilaritybetweentwoGOtermsisbasedontheirmost
sequence in the dataset, a GO vector is constructed by matching specificcommonancestorintheGOhierarchy.Therelationships
its GO terms against W, using the number of occurrences of between GO terms in the GO hierarchy, such as ‘is-a’ ancestor-
individualGOtermsinWasthecoordinates.Specifically,theGO child, or ‘part-of’ ancestor-child can be obtained from the SQL
vector pi of thei-thprotein i isdefined as: database through the link: http://archive.geneontology.org/
latest-termdb/go_daily-termdb-tables.tar.gz. Note here only the
(cid:2)f , GOhit ‘is-a’ relationship is considered for semantic similarity analysis
pFi ~½bi,1,(cid:2)(cid:2)(cid:2),bi,j,(cid:2)(cid:2)(cid:2),bi,W(cid:3)T,bi,j~ 0i,j , otherwise ð1Þ [51].Specifically,thesemanticsimilaritybetweentwoGOterms
x and y is defined as [49]:
wheref isthenumberofoccurrencesofthej-thGOterm(term-
i,j
frequency) in the i-th protein sequence. The rationale is that the
sim(x,y)~max ½{log(p(c))(cid:3), ð3Þ
term-frequencies contain important information for classification. c[A(x,y)
Note that b ’s are analogous to the term-frequencies commonly
i,j whereA(x,y)isthesetofancestorGOtermsofbothxandy,and
used indocument retrieval.
p(c)istheprobabilityofthenumberofgeneproductsannotatedto
Similarly, for the t-th query protein , the GO frequency
t the GO term c divided by the total number of gene products
vector isdefined as:
annotated in the GO taxonomy.
WhileResnik’smeasureiseffectiveinquantifyingtheshared
qF~½b ,(cid:2)(cid:2)(cid:2),b ,(cid:2)(cid:2)(cid:2),b (cid:3)T,b ~(cid:2)ft,j , GOhit ð2Þ information between two GO terms, it ignores the distance
t t,1 t,j t,W t,j 0 , otherwise between the terms and their common ancestors in the GO
hierarchy. To further incorporate structural information from
the GO hierarchy into the similarity measure, we have
Inthefollowingsections,weusethesuperscriptFtodenotethe explored three extension of Resnik’s measure, namely Lin’s
GO frequency features inEq. 2. measure [51], Jiang’s measure [74], and relevance similarity
(RS) [52].
Semantic-Similarity Features GiventwoGOtermsxandy,thesimilaritybyLin’smeasureis:
Semantic similarity (SS) is a measure for quantifying the
similarity between categorical data (e.g., words in documents),
(cid:3) 2:½{log(p(c))(cid:3) (cid:4)
where the notion of similarity is based on the likeliness of sim (x,y):sim (x,y)~ max ð4Þ
meaningsinthedata.ItisoriginallydevelopedbyResnik[49]for Lin 1 c[A(x,y) {log(p(x)){log(p(y))
natural language processing. The idea is to evaluate semantic
similarity in an ‘is-a’ taxonomy using the shared information
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The similarity byJiang’s measure is: GOterms,whileGOSSfeatures(Eq.4toEq.6)usethesemantic
similarity between GO terms. These two features are developed
from two different perspectives. It is therefore reasonable to
sim (x,y):sim (x,y)
Jiang 2
believe that these two kinds of information complement each
(cid:3) 1 (cid:4)ð5Þ other. Based on this assumption, we combine these two GO
~max
c[A(x,y) 1{log(p(x)){log(p(y))z2:½{log(p(c)) features andforma hybridized vector as:
2qF 3
The similarity byRS iscalculated as: t
qHl~6 7, ð10Þ
t 4 5
qSl
simRS(x,y):sim3(x,y) t
~ max (cid:3) 2:½{log(p(c))(cid:3) :(1{p(c))(cid:4)ð6Þ where l[f1,2,3g. In other words, qHt l represents the hybridizing-
c[A(x,y) {log(p(x)){log(p(y)) feature vector by combining the GO frequency features and the
SSfeaturesderivedfromthel-thSSmeasure.Wereferthemtoas
Hybrid1,Hybrid2 andHybrid3,respectively.
Among the three measures, sim (x,y) and sim (x,y) are
Lin Jiang
relative measures that are proportional to the difference in
Multi-label Multi-class SVM Classification
information content between the terms and their common
The hybridized-feature vectors obtained from the previous
ancestors, which is independent of the absolute information
subsection are used for training multi-label one-vs-rest support
content of the ancestors. On the other hand, sim (x,y)
RS vector machines (SVMs). Specifically, for an M-class problem
incorporates the probability of annotating the common ancestors
(here M is the number of subcellular locations), M independent
as a weighing factor to Lin’s measure. To simplify notations, we
binary SVMs are trained, one for each class. Denote the hybrid
refer sim (x,y), sim (x,y) and sim (x,y) as sim (x,y),
Lin Jiang RS 1 GOvectorsofthet-thqueryproteinusingthel-thSSmeasureas
sim (x,y)and sim (x,y),respectively.
2 3 qHl.Giventhet-thqueryprotein ,thescoreofthem-thSVM
Based on the semantic similarity between two GO terms, we t t
using thel-thSSmeasure is
adopted a continuous measure proposed in [48] to calculate the
similaritybetweentwoproteins.Specifically,giventwoproteins
i
and j,weretrievedtheircorrespondingGOtermsPi andPj as sm,l( t)~Xam,rym,rK(pHrl,qHt l)zbm ð11Þ
describedinthesubsection‘‘Retrieval ofGOTerms’’.(Notethat
r[Sm
strictlyspeaking,P shouldbeP ,wherek isthek-thhomolog
i i,ki i i
used to retrieve the GO terms for the i-th protein. To simplify whereqHt l isthehybridGOvectorderivedfrom t (SeeEq.10),
notations, we write it as Pi.) Then, we computed the semantic Sm,l isthesetofsupportvectorindexescorrespondingtothem-th
similarity between twosets ofGO termsfPi,Pjg asfollows: SVM,am,raretheLagrangemultipliers,ym,r[f{1,z1gindicates
whetherther-thtrainingproteinbelongstothem-thclassornot,
S(P,P)~Xmax sim(x,y), ð7Þ and K(:,:)is a kernel function. Here, the linear kernel was used.
l i j y[Pj l Unlike the single-label problem where each protein has one
x[Pi
predicted label only, a multi-label protein could have more than
where l[f1,2,3g, and sim(x,y) is defined in Eq. 4 to Eq. 6. one predicted labels. In this work, we compared two different
l
S(P,P) is computed in the same way by swapping P and P. decisionschemesforthismulti-labelproblem.Inthefirstscheme,
l j i i j
Finally,theoverallsimilaritybetweenthetwoproteinsisgivenby: the predicted subcellular location(s) of the i-th query protein are
givenby
S(P,P)zS(P,P)
SS (P,P)~ l i j l j i , ð8Þ
l i j Sl(Pi,Pi)zSl(Pj,Pj) M(cid:4)l( t)~
8
wJiahnegrealn[df1R,2S,3tgo.aIsnStSh1e,sSeqSu2eal,nwdeSrSe3fe,rrethspeeSctSivmelye.asuresbyLin, >>>><SMm~1fm:sm,l( t)w0g, whenA m[f1,...,Mgs:t: sm,l( t)w0ð(;1122Þ)
semTahnutsi,cfsoimr ialarteitsytin(SgS)pvroectetoinrqStl cwainthbeGoObtateinrmedsbeyt cQotm, pauGtinOg >>>>:argmaxMm~1sm,l( t), otherwise:
t
the semantic similarity between Qt and each of the training
proteinf gN ,whereNisthenumberoftrainingproteins.Thus,
i i~1 The second scheme is an improved version of the first one in
canberepresented by an N-dimensionalvector:
t that the decision threshold is dependent on the test protein.
Specifically, the predicted subcellular location(s) of the i-th query
qStl~½SSl(Qt,P1),(cid:2)(cid:2)(cid:2),SSl(Qt,Pi),(cid:2)(cid:2)(cid:2),SSl(Qt,PN)(cid:3)T, ð9Þ protein aregivenby:
If A s ( )w0,
m,l t
wherel[f1,2,3g.Inotherwords,qSl representstheSSvectorby
t
using thel-thSSmeasure. M
[
M( )~ ðm:s ( )§minf1:0,f(s ( ))gÞ ð13Þ
l t m,l t max,l t
Hybridization of Two GO Features m~1
Ascanbeseenfromthesubsections‘‘GOFrequencyFeatures’’
otherwise,
and ‘‘Semantic-Similarity Features’’, we know that the GO
frequency features (Eq. 2) use the frequency of occurrences of
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for the i-th protein (i~1,...,N), respectively. Here, N~207
i
for thevirus dataset and N~978for theplant dataset. Thenthe
M( t)~argMmaxsm,l( t): ð14Þ five measurementsare defined as follows:
m~1
1 XN (cid:3)jM( )\L( )j(cid:4)
In Eq. 13, f(s ( )) is a function of s (Q), where Accuracy~ i i ð16Þ
max,l t max,l t N jM( )|L( )j
s (Q)~maxM s (Q). In this work, we used a linear i~1 i i
max,l t m~1 m,l t
functionas follows:
f(smax,l( t))~hsmax,l( t), ð15Þ Precision~1 XN (cid:3)jM( i)\L( i)j(cid:4) ð17Þ
N jM( )j
i~1 i
where h[½0:0,1:0(cid:3) is a hyper-parameter that can be optimized
through cross-validation.
In fact, besides SVMs, many other machine learning models, 1 XN (cid:3)jM( )\L( )j(cid:4)
such as hidden Markov models (HMMs) and neural networks Recall~ i i ð18Þ
N jL( )j
(NNs) [75,76], have been used in protein subcellular-localization i~1 i
predictors. However, HMMs and NNs are not suitable for GO-
basedpredictorsbecauseofthehighdimensionalityofGOvectors.
The main reason is that under such condition, HMMs and NNs 1 XN (cid:3)2jM( )\L( )j(cid:4)
canbeeasilyovertrainedandthusleadtopoorperformance.On F1~N jM( )ijzjL( i)j ð19Þ
the other hand, linear SVMs can well handle high-dimensional i~1 i i
databecauseevenifthenumberoftrainingsamplesissmallerthan
thefeaturedimension,linearSVMsarestillabletofindanoptimal
solution. 1 XN (cid:3)jM( )|L( )j{jM( )\L( )j(cid:4)
HL~ i i i i ð20Þ
N M
i~1
Materials and Performance Metrics
wherej:jmeanscountingthenumberofelementsinthesettherein
Datasets
and\represents theintersectionof sets.
In this paper, a virus dataset [35,37] and a plant dataset [36] Accuracy, Precision, Recall and F1 indicate the classification
wereusedtoevaluatetheperformanceoftheproposedpredictor. performance. The higher the measures, the better the prediction
ThevirusandtheplantdatasetswerecreatedfromSwiss-Prot57.9 performance. Among them, Accuracy is the most commonly used
and 55.3, respectively. The virus dataset contains 207 viral criteria.F1-scoreistheharmonicmeanofPrecisionandRecall,which
proteins distributed in 6 locations. Of the 207 viral proteins, 165 allowsustocomparetheperformanceofclassificationsystemsby
belongtoonesubcellularlocations,39totwolocations,3tothree takingthetrade-offbetweenPrecisionandRecallintoaccount.The
locations and none to four or more locations. This means that HammingLoss(HL)[77,78]isdifferentfromothermetrics.Ascan
about20%oftheproteinsinthedatasetarelocatedinmorethan be seen from Eq. 20, when all of the proteins are correctly
one subcellular location. The plant dataset contains 978 plant predicted, i.e., jM( )|L( )j~jM( )\L( )j (i~1,...,N),
i i i i
proteinsdistributedin12locations.Ofthe978plantproteins,904 then HL~0; whereas, other metrics will be equal to 1. On the
belongtoonesubcellularlocations,71totwolocations,3tothree other hand, when the predictions of all proteins are completely
locations and none to four or more locations. The sequence wrong,i.e.,jM( )|L( )j~MandjM( )\L( )j~0,then
i i i i
identity of bothdatasets was cut offat 25%. HL~1;whereas,othermetricswillbeequalto0.Therefore,the
ThebreakdownofthesetwodatasetsarelistedinFigs.2(a)and lower theHL,thebetter theprediction performance.
2(b).Fig.2(a)showsthatthemajority(68%)ofviralproteinsinthe
Two additional measurements [37,39] are often used in multi-
virusdatasetarelocatedinhostcytoplasmandhostnucleuswhile
label subcellular localization prediction. They are overall locative
proteins located in the rest of the subcellular locations totally
accuracy (OLA) and overall actual accuracy (OAA). The former is
account only around one third. This means that this multi-label
givenby:
dataset is imbalanced across the six subcellular locations. Similar
conclusionscanbedrawnfromFig.2(b),wheremostoftheplant
pdrroiotneinwshielexipstroitneinchslionroopthlaesrt,8csyutbocpellalusmla,rlnouccalteiounssatnodtalmlyiatoccchoounn-t OLA~PN 1jL( )jXN jM( i)\L( i)j, ð21Þ
i~1 i i~1
forlessthan30%.Thisimbalancedpropertymakestheprediction
of these two multi-label datasets difficult. These two benchmark andtheoverall actual accuracy (OLA) is:
datasetsaredownloadablefromthehyperlinksintheHybridGO-
Locserver.
1 XN
OAA~ ∆½M( ),L( )(cid:3) ð22Þ
N i i
Performance Metrics i~1
Compared to traditional single-label classification, multi-label
where
classification requires more complicated performance metrics to
betterreflectthemulti-labelcapabilitiesofclassifiers.Convention-
alsingle-labelmeasuresneedtobemodifiedtoadapttomulti-label (cid:2)1 ,if M( )~L( )
D½M( ),L( )(cid:3)~ i i ð23Þ
classification.ThesemeasuresincludeAccuracy,Precision,Recall,F1- i i 0 ,otherwise:
score (F1) and Hamming Loss (HL) [77,78]. Specifically, denote
L( )andM( )asthetruelabelsetandthepredictedlabelset
i i
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HybridGO-Loc
Figure2.Breakdownofthe(a)virusand(b)plantdatasets.Thenumberofproteinsshownineachsubcellularlocationrepresentsthenumber
of‘locativeproteins’[37,39].For(a),thereare207actualproteinsand252locativeproteins;For(b),thereare978actualproteinsand1055locative
proteins.
doi:10.1371/journal.pone.0089545.g002
According to Eq. 21, a locative protein is considered to be thebiasandvarianceofanestimator[81],toavoidconfusion,we
correctlypredictedifanyofthepredictedlabelsmatchesanylabels onlyuse theterm LOOCVinthis paper.
in the true label set. On the other hand, Eq. 22 suggests that an
actualproteinisconsideredtobecorrectlypredictedonlyifallof Results
the predicted labels match those in thetrue label set exactly. For
Comparing Different Features
example,foraproteincoexistin,saythreesubcellularlocations,if
only two of the three are correctly predicted, or the predicted Fig. 3(a) shows the performance of individual and hybridized
resultcontainsalocationnotbelongingtothethree,theprediction GO features on the virus dataset based on leave-one-out cross
isconsideredtobeincorrect.Inotherwords,whenandonlywhen validation (LOOCV). In the figure, SS1, SS2 and SS3 represent
all of the subcellular locations of a query protein are exactly Lin’s, Jiang’s and RS similarity measures, respectively. Hybrid1,
predictedwithoutanyoverpredictionorunderprediction,canthe Hybrid2 and Hybrid3 represent the hybridized features obtained
from these measures. As can be seen, in terms of all the six
prediction be considered as correct. Therefore, OAA is a more
performance metrics, the performance of the hybrid features is
stringentmeasureascomparedtoOLA.OAAisalsomoreobjective
remarkably better than the performance of individual features,
thanOLA.Thisisbecauselocativeaccuracyisliabletogivebiased
regardlessofwhichoftheGOfrequencyfeaturesorthethreeGO
performance measures when the predictor tends to over-predict,
SS features were used. Specifically, the OAAs (the most stringent
i.e., giving large jM( )j for many . In the extreme case, if
i i andobjectivemetric)ofallofthethreehybridfeaturesareatleast
every protein is predicted to have all of the M subcellular
3% (absolute) higher than that of the individual features, which
locations, according to Eq. 20, the OLA is 100%. But obviously,
suggests that hybridizing the two features can significantly boost
thepredictionsarewrongandmeaningless.Onthecontrary,OAA
the prediction performance. Moreover, among the hybridized
is 0% in this extreme case, which definitely reflects the real
features, the performance of Hybrid2, namely combining GO
performance.
frequency features and GO SS features by Jiang’s measure,
Among all the metrics mentioned above, OAA is the most
outperformsHybrid1andHybrid3.Anotherinterestingthingisthat
stringentandobjective.Thisisbecauseifonlysome(butnotall)of
althoughalloftheindividualGOSSfeaturesperformmuchworse
the subcellular locations of a query protein are correctly predict,
than the GO frequency features, the performance of the three
the numerators of the other 4 measures (Eqs. 16 to 21) are non-
hybridized features is still better that of any of the individual
zero,whereasthenumeratorofOAAinEq.22is0(thuscontribute
features. This suggests that the GO frequency features and SS
nothing tothefrequency count).
features are complementary toeachother.
In statistical prediction, there are three methods that are often
Similarconclusionscanbedrawnfromtheplantdatasetshown
used for testing the generalization capabilities of predictors:
inFig.3(b).However,comparisonbetweenFig.3(a)andFig.3(b)
independent tests, sub-sampling tests (or K-fold cross-validation)
reveals that for the plant dataset, the performance of hybridized
and leave-one-out cross validation (LOOCV). For independent
features outperforms all of the individual features in terms of all
tests,theselectionofindependentdatasetoftenbearssomesortof metrics except OLA and Recall, while for the virus dataset, the
arbitrariness [79]; for the K-fold cross validation, different formerissuperiortothelatterintermsofallmetrics.However,the
partitioning of a dataset will lead to different results, thus still losses in these two metrics do not outweigh the significant
beingliabletostatisticalarbitrariness;forLOOCV,itwillyielda improvement on other metrics, especially on OAA, which has
unique outcome and is considered to be the most rigorous and around3%(absolute)improvementintermsofhybridizedfeatures
bias-freemethod[80].Hence,LOOCVwasusedtoexaminethe as opposed to using individual features. Among the hybridizing
performanceofallpredictorsinthiswork.Moredetailedanalysis features,Hybrid2alsooutperformsHybrid1andHybrid3intermsof
of the statistical methods can be found in the supplementary OLA,Accuracy,RecallandF1-score,whereasHybrid1performsbetter
materials. Note that the jackknife cross validation in iLoc-Plant than others in terms of OAA and Precision. These results
anditsvariantsisthesameasLOOCV,asmentionedin[36,79]. demonstrate that the GO SS features obtained by Lin’s measure
Becausethetermjackknifealsoreferstothemethodsthatestimate andJiang’smeasurearebettercandidatesthantheRSmeasurefor
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HybridGO-Loc
combining with the GO frequency features; however, there is no Table 2 except that the OLA of the proposed predictor is slightly
evidencesuggestingwhichmeasureisbetter.Itisalsointerestingto worsethanthatofmGOASVM,andtheRecallisequivalenttothat
seethattheperformanceofthethreeindividualGOSSfeaturesis ofmGOASVM.Nevertheless,thesmalllossesdonotoutweighthe
better than that of GO frequency features, in contrary to the impressiveimprovementintheothermetrics,especiallyintheOAA
results showninFig 3(a). (0.936 vs0.874).
Comparing with State-of-the-Art Predictors Prediction of Novel Proteins
Table1andTable2comparetheperformanceoftheproposed To further demonstrate the effectiveness of HybridGO-Loc, a
predictor against several state-of-the-art multi-label predictors on newerplantdatasetconstructedformGOASVM[39]wasusedto
thevirusandplantdatasetbasedonleave-one-outcrossvalidation. compare with state-of-the-art multi-label predictors using inde-
Note that we used the best performing hybridizing features with pendent tests. Specifically, this new plant dataset contains 175
theadaptivedecisionstrategy.Specifically,forboththevirusand plantproteins,ofwhich147belongtoonesubcellularlocation,27
plant datasets, the best performance was achieved when Hybrid2 belong to two locations, 1 belong to three locations and none to
and the adaptive decision strategy with h~0:3 were used. h was fourormorelocations.TheseplantproteinswereaddedtoSwiss-
determined by cross-validation as stated previously. Unless stated Prot between 08-Mar-2011 and 18-Apr-2012. Because the plant
otherwise, we used Hybrid2 to represent HybridGO-Loc in dataset used for training the predictors was created on 29-Apr-
subsequent experiments. Our proposed predictor use the GO 2008,thereisanalmost3-yeartimegapbetweenthetrainingdata
frequency features and GO semantic similarity features, whereas
andtest data inour experiments.
other predictors use only the GO frequency of occurrences as
Table 3 compare the performance of HybridGO-Loc against
features. From the classification perspective, Virus-mPLoc [35]
several state-of-the-art multi-label plant predictors on the new
uses an ensemble OET-KNN (optimized evidence-theoretic K-
plant dataset. All the predictors use the 978 proteins of the plant
nearest neighbors) classifier; iLoc-Virus [37] uses a multi-label
dataset (See Fig. 2(b)) for training the classifier and make
KNN classifier; KNN-SVM [38] uses an ensemble of classifiers
independent test on the new 175 proteins. As can be seen,
combining KNN and SVM; mGOASVM [39] uses a multi-label
HybridGO-Loc performs significantly better than all the other
SVMclassifier;andtheproposedpredictoruseamulti-labelSVM
predictors in terms of all of the performance metrics. Similar
classifier incorporated withtheadaptive decision scheme.
conclusionscanalsobedrawnfromtheperformanceinindividual
As shown in Table 1, the proposed predictor perform
subcellular locations.
significantly better than the other predictors. The OAA and OLA
Fig.4showsthedistributionoftheE-valuesofthetestproteins,
of the proposed predictor are more than 15% (absolute) higher
which were obtained by using the training proteins as the
than that of iLoc-Virus and Virus-mPLoc. It also performs
repository and the test proteins as the query proteins in the
significantly better than KNN-SVM in terms of OLA. When
BLAST search. If we use a common criteria that homologous
comparing with mGOASVM, the proposed predictor performs
proteins should have E-value less than 10{4, then 74 out of 175
remarkably better in of all of the performance metrics, especially
for the OAA (0.937 vs 0.889). These results demonstrate that testproteinsarehomologsofthetrainingproteins,whichaccount
hybridizing the GO frequency features and GO SS features can for42%ofthetestset.Notethatthishomologousrelationshipdoes
significantly boost prediction performance, which also suggests notmeanthatusingBLAST’shomologytransferscanpredictallof
thatthesetwokindsofinformationareprovedtobecomplemen- the74testproteinscorrectly.Infact,BLAST’shomologytransfers
tary to each other in terms of predicting subcellular localization. (based on the CC field of the homologous proteins) can only
Similar conclusions can be drawn for the plant dataset from achieve a prediction accuracy of 26.9% (47/175). As the
Figure 3. Performance of the hybrid features and individual features on the (a) virus and (b) plant datasets. Freq: GO frequency
features;SS1,SS2andSS3:GOsemanticsimilarityfeaturesbyusingLin’smeasure[51],Jiang’smeasure[74]andRSmeasure[52],respectively;Hybrid1,
Hybrid2 and Hybrid3: GO hybrid features by combining GO frequency features with GO semantic similarity features based on SS1, SS2 and SS3,
respectively.
doi:10.1371/journal.pone.0089545.g003
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HybridGO-Loc
Table1.Comparing theproposed predictorwith state-of-the-artmulti-labelpredictors basedonleave-one-outcrossvalidation
(LOOCV)usingthe virus dataset.
Label SubcellularLocation LOOCVLocativeAccuracy(LA)
Virus-mPLoc[35] KNN-SVM[38] iLoc-Virus[37] mGOASVM[39] HybridGO-Loc
1 Viralcapsid 8/8=1.000 8/8=1.000 8/8=1.000 8/8=1.000 8/8=1.000
2 Hostcellmembrane 19/33=0.576 27/33=0.818 25/33=0.758 32/33=0.970 32/33=0.970
3 HostER 13/20=0.650 15/20=0.750 15/20=0.750 17/20=0.850 18/20=0.900
4 Hostcytoplasm 52/87=0.598 86/87=0.988 64/87=0.736 85/87=0.977 85/87=0.966
5 Hostnucleus 51/84=0.607 54/84=0.651 70/84=0.833 82/84=0.976 82/84=0.988
6 Secreted 9/20=0.450 13/20=0.650 15/20=0.750 20/20=1.000 20/20=1.000
OverallLocativeAccuracy(OLA) 152/252=0.603 203/252=0.807 197/252=0.782 244/252=0.968 245/252=0.972
OverallActualAccuracy(OAA) – – 155/207=0.748 184/207=0.889 194/207=0.937
Accuracy – – – 0.935 0.961
Precision – – – 0.939 0.965
Recall – – – 0.973 0.976
F1 – – – 0.950 0.968
HL – – – 0.026 0.016
‘‘–’’meansthecorrespondingreferencesdonotprovidetheresultsontherespectivemetrics.HostER:Hostendoplasmicreticulum.
doi:10.1371/journal.pone.0089545.t001
predictionaccuracyofHybridGO-Loconthistestset(seeTable3) Discussion
is significantly higher than this percentage, the extra information
available from the GOA database plays a very important role in Semantic Similarity Measures
theprediction. In this paper, we have compared three of the most common
semanticsimilaritymeasuresforsubcellularlocalization,including
Table2.Comparing theproposed predictorwith state-of-the-artmulti-labelpredictors basedonleave-one-outcrossvalidation
(LOOCV)usingthe plantdataset.
Label SubcellularLocation LOOCVLocativeAccuracy(LA)
Plant-mPLoc[34] iLoc-Plant[36] mGOASVM[39] HybridGO-Loc
1 Cellmembrane 24/56=0.429 39/56=0.696 53/56=0.946 51/56=0.911
2 Cellwall 8/32=0.250 19/32=0.594 27/32=0.844 28/32=0.875
3 Chloroplast 248/286=0.867 252/286=0.881 272/286=0.951 278/286=0.972
4 Cytoplasm 72/182=0.396 114/182=0.626 174/182=0.956 168/182=0.923
5 Endoplasmicreticulum 17/42=0.405 21/42=0.500 38/42=0.905 38/42=0.905
6 Extracellular 3/22=0.136 2/22=0.091 22/22=1.000 21/22=0.955
7 Golgiapparatus 6/21=0.286 16/21=0.762 19/21=0.905 19/21=0.905
8 Mitochondrion 114/150=0.760 112/150=0.747 150/150=1.000 149/150=0.993
9 Nucleus 136/152=0.895 140/152=0.921 151/152=0.993 150/152=0.987
10 Peroxisome 14/21=0.667 6/21=0.286 21/21=1.000 21/21=1.000
11 Plastid 4/39=0.103 7/39=0.179 39/39=1.000 38/39=0.974
12 Vacuole 26/52=0.500 28/52=0.538 49/52=0.942 48/52=0.923
OverallLocativeAccuracy(OLA) 672/1055=0.637 756/1055=0.717 1015/1055=0.962 1009/1055=0.956
OverallActualAccuracy(OAA) – 666/978=0.681 855/978=0.874 915/978=0.936
Accuracy – – 0.926 0.959
Precision – – 0.933 0.972
Recall – – 0.968 0.968
F1 – – 0.942 0.966
HL – – 0.013 0.007
‘‘–’’meansthecorrespondingreferencesdonotprovidetheresultsontherespectivemetrics.
doi:10.1371/journal.pone.0089545.t002
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HybridGO-Loc
Table3. ComparingHybridGO-Loc with state-of-the-art multi-labelplantpredictors basedon independenttestsusingthe new
plantdataset.
Label SubcellularLocation IndependentTestLocativeAccuracy
Plant-mPLoc[34] iLoc-Plant[36] mGOASVM[39] HybridGO-Loc
1 Cellmembrane 8/16=0.500 1/16=0.063 7/16=0.438 16/16=1.000
2 Cellwall 0/1=0 0/1=0 0/1=0% 1/1=1.000
3 Chloroplast 27/54=0.500 45/54=0.833 39/54=0.722 30/54=0.556
4 Cytoplasm 5/38=0.132 15/38=0.395 19/38=0.500 31/38=0.816
5 Endoplasmicreticulum 1/9=0.111 1/9=0.111 3/9=0.333 4/9=0.444
6 Extracellular 0/3=0 0/3=0 1/3=0.333 0/3=0
7 Golgiapparatus 3/7=0.429 1/7=0.143 3/7=0.429 7/7=1.000
8 Mitochondrion 6/16=0.375 3/16=0.188 11/16=0.688 16/16=1.000
9 Nucleus 31/46=0.674 43/46=0.935 33/46=0.717 44/46=0.957
10 Peroxisome 4/6=0.667 0/6=0 3/6=0.500 4/6=0.667
11 Plastid 0/1=0 0/1=0 0/1=0 0/1=0
12 Vacuole 2/7=0.286 4/7=0.571 4/7=0.571 7/7=1.000
OverallLocativeAccuracy(OLA) 87/204=0.427 113/204=0.554 123/204=0.603 160/204=0.784
OverallActualAccuracy(OAA) 60/175=0.343 91/175=0.520 97/175=0.554 127/175=0.726
Accuracy 0.417 0.574 0.594 0.784
Precision 0.444 0.626 0.630 0.826
Recall 0.474 0.577 0.609 0.798
F1 0.444 0.592 0.611 0.803
HL 0.116 0.076 0.075 0.037
doi:10.1371/journal.pone.0089545.t003
N
Lin’smeasure[51],Jiang’smeasure[74],andrelevancesimilarity B1) Inter-term relationship. SS vectors are based on inter-
measure [52]. We excluded Resnik’s measure because it ignores term relationships. They are defined on a space in which
thedistancebetweenthetermsandtheircommonancestorsinthe each basis corresponds to one training protein and the
GO hierarchy. In addition to these measures, many online tools coordinate along that basis is defined by the semantic
arealsoavailableforcomputingthesemanticsimilarityattheGO- similarity between a testing protein and the
term level and gene-product level [44,82–84]. However, these corresponding trainingprotein.
N
measuresarediscretemeasureswhereasthemeasuresthatweused B2) Inter-group relationship. The pairwise relationships
arecontinuous.Researchhasshownthatcontinuousmeasuresare betweena test protein and the training proteins are
better than discrete measuresinmanyapplications [48]. hierarchically structured. This is because each basis of
the SS space depends on a group of GO terms of the
GO-Frequency Features versus SS Features corresponding training protein, and the terms are
NotethatwedonotreplacetheGOfrequencyvectors.Instead, arranged in a hierarchical structure (parent- child
weaugmenttheGOfrequency featurewithamoresophisticated relationship). Because the GO terms in different groups
feature,i.e.theGOSSvectors,whicharetobecombinedwiththe are not mutually exclusive, the bases in the SS space are
GO frequency vectors. A GO frequency vector is found by not independent of eachother.
countingthenumberofoccurrencesofeveryGOterminasetof
distinctGOtermsobtainedfromthetrainingdataset,whereasan Bias Analysis
SS vector is constructed by computing the semantic similarity Except for the new plant dataset, we adopted LOOCV to
between a test protein with each of the training proteins at the examine the performance of all predictors in this work, which is
gene-product level. That is, each element in an SS vector considered to be the most rigorous and bias-free [80]. Neverthe-
represents the semantic similarity of two GO-term groups. This less,determiningthesetofdistinctGOtermsWfromadatasetis
can be easily seen from their definitions in Eq. 2 and Eq. 4–9, by no means without bias, which may favor the LOOCV
respectively. performance. This is because the set of distinct GO terms W
TheGOfrequencyvectorsandtheGOSSvectorsaredifferent derived from a given dataset may not be representative for other
in twofundamental ways. datasets; in other words, the generalization capabilities of the
N predictorsmaybeweakenedwhennewGOtermsoutsideWare
A). GO frequency vectors are more primitive in the sense that foundin thetest proteins.
their elements are based on individual GO terms without However,wehavethefollowingstrategiestominimizethebias.
considering the inter-term relationship, i.e., the elements in a First, the two benchmark datasets used in this paper were
GO frequency vectors are independent ofeach other. constructed based on the whole Swiss-Prot database (although in
N different years), which, to some extent, incorporated all the
B). GO SS vectors are more sophisticated in the following two
senses.
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Figure4.Distributionoftheclosenessbetweenthenewtestingproteinsandthetrainingproteins.TheclosenessisdefinedastheBLAST
E-valuesofthetrainingproteinsusingthetestproteinsasthequeryproteinsintheBLASTsearches.NumberofProteins:Thenumberoftesting
proteinswhoseE-valuesfallintotheintervalspecifiedunderthebar.SmallE-valuessuggestthatthecorrespondingnewproteinsareclosehomologs
ofthetrainingproteins.
doi:10.1371/journal.pone.0089545.g004
possible information of plant proteins or virus proteins in the this bias will not exist during LOOCV (see the accompanying
database. In other words, W was constructed based on all of the supplementarymaterialsfortheproof).Furthermore,theresultsof
GOtermscorrespondingtothewholeSwiss-Protdatabase,which theindependenttests(SeeTable3)forwhichnosuchbiasoccurs
enables W to be representative for all of the distinct GO terms. also strongly suggest that HybridGO-Loc outperforms other
Second,thesetwobenchmarkdatasetswerecollectedaccordingto predictors by alarge margin.
strict criteria. Details of the procedures can be found in the
supplementary materials. and the sequence similarity of both
Conclusions
datasetswascutoffat25%,whichenablesustouseasmallsetof
representative proteins to represent all of the proteins of the Thispaperproposesanewmulti-labelpredictorbyhybridizing
correspondingspecies(i.e.,virusorplant)inthewholedatabase.In GOfrequencyfeaturesandsemanticsimilarityfeaturestopredict
other words, W will vary from species to species, yet still be the subcellular locations of multi-label proteins. Three different
statistically representative for all of the useful GO terms for the semantic similarity measures have been investigated to be
corresponding species.Third,usingWforstatisticalperformance combined with GO frequency features to formulate GO hybrid
evaluationisequivalentoratleastapproximatetousingallofthe feature vectors. The feature vectors are subsequently recognized
distinctGOtermsintheGOAdatabase.ThisisbecauseotherGO by multi-label multi-class support vectors machine (SVM) classi-
terms that do not correspond to the training proteins will not fiersequippedwithanadaptivedecisionstrategythatcanproduce
participateintrainingthelinearSVMs,norwilltheyplayessential multiple class labels for a query protein. Compared to existing
roles in contributing to the final predictions. In other words, the multi-label subcellular-localization predictors, our proposed pre-
generalizationcapabilitiesofHybridGO-Locwillnotbeweakened dictor has the following advantages: (1) it formulates the feature
even if some new GO terms are found in the test proteins. A vectors by hybridizing GO frequency of occurrences and GO
mathematical proof of this statement can be found in the semantic similarity features which contains richer information
supplementary materials available in theHybridGO-Locserver. than only GO term frequencies; (2) it adopts a new strategy to
Onemayarguethattheperformancebiasmightarisewhenthe incorporatericherandmoreusefulhomologousinformationfrom
whole W was used to construct the hybrid GO vectors for both more distant homologs rather than using the top homologs only;
training and testing during cross validation. This is because, in (3) it adopts an adaptive decision strategy for multi-label SVM
eachfoldoftheLOOCV,thetrainingproteinsandthesingled-out classifiers so that it can effectively deal with datasets containing
test protein will use the same W to construct the GO vectors, both single-label and multi-label proteins. Experimental results
meaning that the SVM training algorithm can see some demonstrate the superiority of the proposed hybrid features over
information of the test protein indirectly through the GO vector each individual features. It was also found that the proposed
space defined by W. It is possible that for a particular fold of predictorperformsremarkablybetterthanexistingstate-of-the-art
LOOCV,theGOtermsofatestproteindonotexistinanyofthe predictors. For readers’ convenience, HybridGO-Loc is available
training proteins. However, we have mathematically proved that online athttp://bioinfo.eie.polyu.edu.hk/HybridGoServer/.
PLOSONE | www.plosone.org 10 March2014 | Volume 9 | Issue 3 | e89545