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Research ArticleCreation of Individual Scientific Concept-Centered SemanticMaps Based on Automated Text-Mining Analysis of PubMed

Ekaterina Ilgisonis1 Andrey Lisitsa1 Valerya Kudryavtseva2 and Elena Ponomarenko 1

1 Institute of Biomedical Chemistry RAS Moscow Russia2KuB Ltd Russia

Correspondence should be addressed to Elena Ponomarenko 2463731gmailcom

Received 4 March 2018 Accepted 5 July 2018 Published 26 July 2018

Academic Editor Ming Chen

Copyright copy 2018 Ekaterina Ilgisonis et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Concept-centered semantic maps were created based on a text-mining analysis of PubMed using the BiblioEngine v2018 softwareThe objects (ldquoconceptsrdquo) of a semantic map can be MeSH-terms or other terms (names of proteins diseases chemical compoundsetc) structured in the form of controlled vocabularies The edges between the two objects were automatically calculated based onthe index of semantic similarity which is proportional to the number of publications related to both objects simultaneously On theone hand an individual semantic map created based on the already published papers allows us to trace scientific inquiry On theother hand a prospective analysis based on the study of PubMed search history enables us to determine the possible directions forfuture research

1 Introduction

Today the number of papers and citations is considered asthe main indicator of scientific output [1] According to thePubMedMEDLINE database [2] the number of scientificarticles in the fields of medicine and biology amountedto more than a million over the last year To analyze theknowledge accumulated in the form of hundreds and eventhousands of papers a large number of text-mining solutionsoften called the ldquoPubMed derivativesrdquo have been proposed[3]

MeSH indexing terms (the main headings and subhead-ings) are served as a rich resource for extracting a broadrange of domain knowledge [4] In 2015 it was shown thatleveraging the implicit and explicit semantics provided bymanually assignedMeSH-terms is an effective representationfor capturing the underlying context of complex associations[5] Earlier researches in this field including the developmentof a MeSH-based index of research interests confirmedMeSHrsquos usefulness for indexing research interests [6] devel-opment of a biomedical expert finding system [7] or creationof an individualized service on the libraryrsquos portal system[8] As a visualization technique many tools help to organize

the PubMed publications in the form of a concept-centerednetwork or graph [3 9ndash11]

In this study we developed an idea of combined MeSH-based profiles of the published and recently viewed papers inthe formof aMeSH-centered semantic network Past researchin this field including an analysis of MeSH indexing patternsconsists of Unified Medical Language System [12] (UMLS)semantic groups related to the MeSH headings together withtheir associated MeSH subheadings [13] which suggests thatthe MeSH-based analytical tools tend to be more effectiveover time

An example of the use of automated text processing isan approach implemented in BiblioEngine v2018 softwareand based on the analysis of the summary of scientificpublications on medical and biological topics from thePubMedMEDLINE database Automated analysis and com-parison of MeSH-terms included in the papers enable us toform groups of relevant literature according to user-definedcriteria and highlight the key concepts within these groupsexpressed in the form of a relationship between MeSHThis approach was implemented in the automated analysisof scientific papers in the field of molecular mechanismsof the onset of Alzheimerrsquos disease investigation of drug

HindawiAdvances in BioinformaticsVolume 2018 Article ID 4625394 10 pageshttpsdoiorg10115520184625394

2 Advances in Bioinformatics

transport systems and discovery of natural compounds withtherapeutic properties [14]

In this work concept-centered semantic maps werecreated based on a text-mining analysis of PubMed usingthe BiblioEngine v2018 software The edges between the twoobjects (laquoconceptsraquo) were automatically calculated based onthe index of semantic similarity which is proportional to thenumber of publications related to both objects simultane-ously On the one hand an individual semantic map createdbased on the already published papers allows us to tracescientific thought On the other hand a prospective analysisbased on the study of search queries to PubMed enables us todetermine the possible directions for future research

The proposed approach was demonstrated using anexample of the creation of an individual cognitive map ofProfessor Alexander Archakov a biochemist and one of themost famous Russian scientists in the field of life scienceArchakov AI is a founder of a scientific school in the fieldof molecular organization studies His interests are focusedon the study of functioning the oxygenase cytochromeP450-containing system molecular mechanisms structureand function of membranes and biological oxidation Beingthe pioneer in the development of proteomics in RussiaArchakov AI headed the Chromosome 18 Team in the inter-national Human Proteome Project [15] The construction ofthe scientistrsquos semantic map is an interesting example sincehis scientific interests are changed every 10-15 years coveringbiochemistry bioinformatics nanotechnology proteomicspersonalized medicine and digital health

2 Materials and Methods

Todownload a list of relevant literature published in PubMedwe used the query [ldquoArchakov AIrdquo] A list of identifiers ofrelevant publications in PubMed (PMIDs) was created anda list of MeSH associated with each article was loaded (seeSupplementary Note 1 for algorithm details)

To quantify the semantic relationships between the nodesof the semantic map (MeSH-terms here) the BiblioEnginesoftwarewas used [11]The closest nodes on the semanticmapwere determined as the termsmost frequently occurred in thesame papers Selection of MeSH specific to the articles pub-lished by Alexander Archakov was performed by choosingMeSH the frequency of occurrence ofwhichwas significantlydifferent from that in the random sample Our randomsample consisted of a list of PMIDs randomly assembled fromall PMIDs presented in the PubMedMEDLINE library andthe size of the group that contained those randomly selectedPMIDs was equal to the number of PMIDs presented inthe targeted group in this case the number of PMIDs ofArchakovrsquos scientific articles

To assess how the scientific interests have changed overtime all the papers published were divided into groupsaccording to the release date (starting from the year 1970)Thus we have formed five groups including the articlespublished in 1970-1980 1980-1990 1990-2000 2000-2010and 2010-2017 respectively The time intervals were chosenin such a way that on the one hand the selections of paperswere comparable in volume and on the other they covered

a period that was significant for the research For each timeinterval a list of PMIDs and the corresponding MeSH wasformed

The semantic concept-centered map was created basedon the calculated matrix of semantic similarity between thenodes (MeSH-terms) and then visualized in the Cytoscapev361 program [16] The coefficient of semantic similarityT (119886 119887) between two objects (MeSH a and MeSH b) wascalculated using the Tanimoto normalization [17]

T (a b) =1003816100381610038161003816Pab1003816100381610038161003816

1003816100381610038161003816Pa1003816100381610038161003816 +1003816100381610038161003816Pb1003816100381610038161003816 minus1003816100381610038161003816Pab1003816100381610038161003816 (1)

where Pa is the number of PMIDs of the papers indexed withMeSH of a obtained as the number of papers returned byPubMed to a query (ldquoardquo [MeSH-Terms]rdquo) Pb is the numberof PMIDs of papers indexed with MeSH of b and Pab is thenumber of PMIDs of papers indexed with MeSH of a as wellas MeSH of b

The nodes formed a separate cluster if the measure ofsemantic similarity between the nodeswas in the range of 075to 1 For each time interval the MeSH-centered clusters werevisualized as a semantic network Additional information forannotation of the semantic network was obtained fromGoPubMed system [18] (httpgopubmedorg) and the Sco-pus bibliometric database (httpswwwelseviercomsolu-tionsscopus) We used GoPubMed for creating a list ofjournals in which relevant research articles retrieved fromPubMed were published and information on the number ofcitations was obtained from Scopus

For the creation of theMeSH-centered semantic networkbased on search history BioKnol [14] software was used Thefree available plugin that enables us to trace the whole searchhistory in the form of PMIDs or DOI of the content can beinstalled from httpwsbioknowledgecenterru While navi-gating PubMed a researcher generates user-specific readingprofiles that can be shared within the BioKnol social net-working environment Within the framework of the presentstudy we used the browsing history of scientific articles in thePubMed system for a one-year period (2012-2013) ReflectingArchakovrsquos present interests this period also allows us tocompare MeSH with his scientific papers published in 2014-2017

3 Results and Discussion

Over the period from 1968 to 2017 more than 600 publica-tions authored by Alexander Archakov were published ofwhich 424 papers are indexed in the PubMed system Thisnumber is sufficient enough for demonstrating the laquoproof-of-conceptraquo Indeed the number of objects on the semantic mapand its informativeness depend on the number of publishedarticles Our previous experience [19] has shown that onehundred publications are the quantity that allows us tocompare the results of text-mining processing with those ofbiocuration and expert analysis

The most cited articles (data about citing obtainedfrom Scopus) are published in such journals as ProteomicsBiosensors amp Bioelectronics Journal of Proteome Research

Advances in Bioinformatics 3

Figure 1The cloud ofMeSH-terms associated with the publicationsof Alexander Archakov in PubMed The frequency of MeSH occur-rence in the articles published is proportional to the font size

Biochemical and Biophysical Research Communications andBiochemistry and Molecular Biology International accord-ing to GoPubMed annotations

The subjects of all published articles were presentedin the form of a MeSH-terms cloud (see Figure 1) whilethe frequency of MeSH occurrence is proportional to thefont size The key terms are Oxidation Reduction andCytochrome P450 Enzyme System since the main publi-cations are focused on the study of the functional role ofenzymes of the cytochromes P450 superfamily The reactionof monooxygenase catalysis involving the participation ofthese proteins is a necessary link in providing vital activitytherefore a significant part of the work is devoted to theanalysis of the substrate specificity (ldquoSubstrate Specificityrdquo)of proteins of this group and investigation of the kineticsof enzymatic reactions (ldquoKineticsrdquo) Among the commonlyused methods of research are two-dimensional electrophore-sis (ldquoElectrophoresis Gel Two-Dimensionalrdquo) spectropho-tometric methods (ldquoSpectrophotometryrdquo) biosensor analysis(ldquoBiosensing Techniquesrdquo) and mass spectrometric meth-ods (including MALDI ldquoSpectrometry Mass Matrix-AssistedLaser Desorption-Ionizationrdquo) Bioinformatic approaches arerepresented by algorithms for the analysis of amino acidsequences of proteins (ldquoMolecular Sequence Datardquo) reflect-ing some articles led by Alexander Archakov and aimed atstudying the approaches enabled us to classify proteins of thecytochromes P450 superfamily [20 21]

For a retrospective analysis of changes in the prioritiesof the scientific school founded by Alexander Archakov weanalyzed the frequency of MeSH-terms occurrence for thescientific papers published between 1970 and 2010 (see Fig-ure 2(a) Supporting information Table 1) A1-A4 fragmentsreflect a network ofMeSH-terms associatedwith publicationsof each decade It is noteworthy that for a quite large timeintervalmdashforty yearsmdashthe key list of the most commonMeSH remained almost unchanged ldquoMicrosomes Liverrdquo

ldquoCytochrome P450 Enzyme Systemrdquo ldquoOxidation-ReductionrdquoldquoNADHNADPHOxidoreductasesrdquo and other terms (markedin the figure with (lowast)) characterizing the research in the fieldof microsomal oxidation and the molecular organization andfunctioning of cytochrome P450 containing system

During the first decade under consideration (1970-1980)the specificity of the work performed is related to the studyof biological membranes and electron transport which isspecified by the presence of terms such as ldquoMembranesrdquoand ldquoElectron transportrdquo on the A1 fragment After 1980ldquoLiposomesrdquo ldquoCholesterolrdquo ldquoRabbitsrdquo ldquoPhospholipidsrdquo andldquoMembrane lipidsrdquo are found among MeSH-terms that indi-cates the beginning of work on the mechanisms of damageand effective recovery of biological membranes In the futurepublications in this area will become the basis for thecreation of a hepatoprotector laquoPhosphoglivraquo One of themaincomponents of this medicine soybean phosphatidylcholine(lecithin) can restore the structure of the liver cells actingas a ldquomembrane gluerdquo and ldquogluingrdquo the defects of damagedbiological membranes regardless of their origin [22] TodaylaquoPhosphoglivraquo is a medication that can be bought from anypharmacy Therefore the description of these publications isonly present in a retrospective part of the semantic map

The last decade of the previous century (since 1991)signaled the beginning of active bioinformatics research onthe structure and function of proteins of the cytochromesP450 superfamily This period is specified by the presence ofdecade-specificMeSH-terms such as ldquoAmino Acid SequencerdquoldquoMolecular Sequence Datardquo ldquoMolecular Modelsrdquo ldquoBindingsitesrdquo ldquoComputer Simulationrdquo and ldquoProtein Conformationrdquo(see the A3 fragment of Figure 2) The beginning of the 21stcentury (2000-2010) is associated with the development ofpostgenomic experimental methods as can be seen fromthe MeSH most commonly found in this period ldquoPro-teomicsrdquo ldquoMass-Spectrometryrdquo and ldquoElectrophoresis Gel Two-dimensionalrdquo Also for the first time there are publicationsassociated with the terms ldquoMicroscopy Atomic Forcerdquo andldquoElectrochemistryrdquo (A4 fragment Figure 2) which indicate anew period of research

After 2010 more than 40 articles led by AlexanderArchakovwere published themainMeSH-terms ofwhich arepresented in Figure 2(b) As part of the analyzed fragmentof the cognitive map we can identify five clusters thatcharacterize the main areas of research carried out by theteam of the Institute of Biomedical Chemistry headed byProfessor Alexander Archakov since 1989

Thus the first cluster (B1) reflects studies in the fieldof bioinformatics the development of information systemsfor storage of proteomic data (ldquoDatabases Proteinrdquo) algo-rithms for sequence analysis (ldquoSequence Analysis Proteinrdquo)and molecular modeling (ldquoModels molecularrdquo) and researchin the field of text-mining analysis (ldquoPubMedrdquo ldquoInforma-tion Storage and Retrievalrdquo) The second fragment (B2)reveals the scientific interests of biochemical laboratoriesthe continuation of research in the field of cytochromeP450-containing and enzymatic systems A group of MeSH-terms describing bioelectrochemistry studies stands out (seeMeSH ldquoElectrochemistryrdquo ldquoElectrodesrdquo and ldquoElectrochemicalTechniquesrdquo) Members of the team headed by Professor


(a)

(b)

Figure 2 A fragment of the cognitive map created based on the analysis of scientific papers of Professor Alexander Archakov The nodesare MeSH-terms associated with the papersThe edges between the two objects were automatically calculated based on the index of semanticsimilarity which is proportional to the number of publications related to both objects simultaneously (a) The period from 1970 to 2010 Thescientific papers of each decade are presented on A1-A4 fragments The MeSH-terms common to the A1-A4 fragments are marked with (lowast)(b) The period from 2010 to 2017

Victoria Shumyantseva working in this field are the leadersrecognized by the world community [23ndash25]

The department of proteomic research was established in2001 and has increased significantly over the past ten years

when Russia took part in the international Human ProteomeProject [26] MeSH characterizing the main scientific papersin the field of proteomics are presented in the B3 fragmentof Figure 2 ldquoBlood Proteinsrdquo (the main biological material


for this research is the depleted plasma of human blood [27]and ldquoChromosome Pair 18rdquo (chromosome 18 was selectedfor research within the Russian part of the internationalHuman Proteome Project [28]) The terms ldquoTranscriptomeand Metabolomerdquo emphasize the need to use a systemapproach at the interface of several technologies in modernscientific research

The work performed under the leadership of AlexanderArchakov resulted in an increased knowledge in the fieldof molecular functioning of living systems that is necessaryfor improving the methods of diagnosis and treatment ofdiseases and potentially cost-effective for the use in medicalpractice Thus the other two fragments B4 and B5 areconnected with personalized medicine The B4 fragmentspecifies nanotechnology studies namely the developmentof new drugs (see MeSH ldquoDrug Delivery Systemsrdquo ldquoDrugcombinationrdquo) and the use of nanotechnology for creating abasis in the field of analytical technologies with the potentialfor disease diagnostics (ldquoNanomedicinerdquo ldquoMicroscopy andAtomic Forcerdquo) Studies in personalizedmedicine are reflectedby MeSH forming the B5 fragment of the cognitive mapThey are devoted to the use of sets of genomic data andpostgenomic technologies for monitoring human healthincluding blood composition development of the approachesto individualized drug therapy and prediction of the risks ofdiseases

Citation is one of the knowledge-intensive indicators thatindirectly enables us to assess the relevance of publishedwork It is believed that citation in rapidly developing scien-tific fields is higher than in others When analyzing the paperactivity and bibliometric indicators the citation statistics ofthe works published stands out according to Google Scholar(httpsscholargoogleru) as of January 2017 the articles ofAlexander Archakov were cited about 13 thousand timeswhile the h-index of the scientist was 50

The maximum number of citations (2552) was receivedby the book laquoLipid peroxidation in biological membranesraquocoauthored by Yuriy A Vladimirov and published in 1972 thevolume ldquoMicrosomal Oxidationrdquo (1975) is cited in more thanthree hundred scientific works and the book ldquoCytochromeP-450 and active oxygenrdquo published in 1990 ranks third withrespect to citations among books in the field of biochemistrypublished by Alexander Archakov

The most cited papers in journals are related to pro-teomics and participation in the international projects [29ndash32] mostly these are the articles published between 2003 and2011 An analysis of MeSH-terms associated with the 20 mostcited articles of Alexander Archakov showed the importanceof these proteomic studies for the scientific community(see Figure 3) characterized by MeSH-terms ldquoProteinsrdquoldquoProteomerdquo ldquoMass-spectrometryrdquo etc as well as fundamentalbiochemical papers presented in the figure with the key termsldquoEnzyme Activatorsrdquo ldquoEnzymesrdquo ldquoElectron transporterrdquo etcIt is noteworthy that the results of the analysis reflect thebasic information about the nature of the scientific work ofthe scientist previously obtained from the entire array ofpublished papers It seems that MeSH networks representinformation comparable with a citation graph [33] Themost cited publications (at least in the example described)

Figure 3 MeSH-terms associated with the 20 most cited publica-tions of Alexander Archakov The frequency of MeSH occurrenceis proportional to the font size The distance between the nodes israndom

thematically coincide with the most frequently encounteredMeSH-terms characterizing the entire sample of the articlespublished by the author ie the subject to which the mostattention was devoted

The scientistrsquos cognitive map constructed based on thepublished studies was supplemented with information aboutthe key MeSH associated with the articles viewed by thescientist For the analyzed period (2012-2013) the BioKnol[14] automatic system recorded a log with 114 different scien-tific papers published in international journals and indexedby PubMedMEDLINE For identifying common conceptsthe frequencies of MeSH-terms were compared with thekey words of the articles published under the guidance ofAlexander Archakov during the period 2014-2017

Therefore two sources of MeSH-terms were consideredOn one hand there were frequencies of MeSH-terms whichoccur in the articles published that are written by the author(or coauthors) On the other hand there were frequenciesof MeSH-terms which characterize scientific papers read bythe same author The difference between MeSH profiles ofwrittenpublished and read papers is useful to capture thenew trends in the personal research interests For instancefor professor Archakovrsquos sample for the period from 2014 to2017 terms ldquoProteomicsrdquo ldquoEnzyme Activationrdquo and ldquoMem-brane Proteinsrdquo significantly prevailed in connection withthe published articles as compared to the articles read Thisobservation reflects the field of the scientific interests and theprocess of knowledge actualization in this area (see Figure 4the background cloud of MeSH)

MeSH-terms appeared more frequently in publishedpapers include terms ldquoIndividualized medicinerdquo ldquoRisk Assess-mentrdquo ldquoBiological markersrdquo ldquoProtein interaction mappingrdquoetc It is interesting that an analysis ofMeSH-terms of the arti-cles published in 2014-2017 (ie for the subsequent period)shows the appearance of the same terms in the scientistrsquos pub-lications describing the possibilities of postgenomic methods


Proteins

Mass-Spectrometry

Protein Binding

ROC Curve

Phenotype

Individualized Medicine

Genome Human

Proteome

Computer Simulation

Biological Markers

Kinetics

Risk Assessment

Transcriptome

Metabolomics

Gene Expression

Figure 4 A fragment of the cognitivemap constructed based on an analysis of the frequencies ofMeSH-terms for the scientific papers viewedby the scientist in 2012-2013 In the foreground there is a fragment of the MeSH network associated both with the articles viewed and withthe authorrsquos articles published in the latest years (2014-2017)

for personalized medicine The key studies published in thisfield in the last two years [34 35] highlight the possibilitiesof metabolic profiling for diagnosis The studies [36 37]reveal the possibilities of using proteomicmethods andmass-spectrometry for medicine and a search for biological mark-ers The studies [38 39] discuss the expression of potentiallymedically important genes at the proteomic level based on theanalysis of genomic and transcriptomic data

Figure 4 demonstrates a fragment of the cognitive mapshowing MeSH-terms the frequencies of which have signifi-cantly increased in the scientistrsquos articles published in 2014-2017 Thus the development of this direction namely anautomatic processing of the history of the researcherrsquos log toscientific papers may be potentially interesting for predic-tion of possible directions for future research the authorrsquosscientific publications and consequently the effectivenessof his scientific activity However besides BioKnol thereare alternative ways to store the search history The key isto compare the frequencies of MeSH-terms associated withalready published studies andMeSH-terms assigned with thecontent of the scientist log-history formed during routinework

From our point of view the retrospective part of anindividual semantic map is less subject to temporal changesThis mainly depends on the age of a researcher the youngerthe scientist is the more the changes on the retrospectivemap can be expected including radical changes in its structurelike the emergence of a new cluster of terms if a researcher

has changed his place of work or a field of study Theprospective part of amap ismore susceptible to changes sinceit depends on the cognitive interest of a scientist The higherthe cognitive activity is the more active a person is and thegreater the number of scientific directions (and consequentlyMeSH-terms) can be reflected in a personal semantic mapThe predictive power of a MeSH-centered semantic mapdepends on the level of the nodes hierarchyMeSH-termsThemore detailed (or less) the level of the MeSH-terms hierarchyon a prospective semantic map is the more precise one canassume the specificity of scientific papers

As an example of the BiblioEngine software capabilitieswe shall consider the creation of a semantic map in thefield of scientific developments of John Craig Venter (JCVenter) who had a significant impact on the development ofpostgenomic technologies andmolecular biology John CraigVenter is a biologist businessman and a cofounder of theInstitute for Genomic Research and J Craig Venter Institutewhich conducts research in synthetic biology

Figure 5 shows a fragment of his semanticmapThe largersubgraph contains the terms ldquoGenomicsrdquo ldquoHuman GenomeProjectrdquo ldquoSequence Analysis DNArdquo and others which highlycorrespond to one of the activities of CraigVenter ie partici-pation in the project of sequencing the human genome Someof the concepts included in the same subgraph (for exampleldquoGenome Bacterialrdquo ldquoRecombination Geneticsrdquo) refer to theactivities of Craig Venter which he carries out in the field ofsynthetic biology


RepetitiveSequencesNucleicAcid

Kinetics

ReceptorsMuscarinic

Isoproterenol

MolecularWeight

ReceptorsAdrenergicalpha

Rats

CellMembrane

ReceptorsAdrenergicbeta

GenesBacterial

DNARepair

TranscriptionGenetic

DNABacterial

OpenReadingFrames

EvolutionMolecular

GeneDuplication

Phylogeny

GenomeBacterial

CloningMolecular

Genomics

Genome

PolymorphismSingleNucleotide

SequenceAnalysisDNA

SpeciesSpecificity

ComputationalBiology

GenomeHuman

DNA

DNAComplementary

BaseSequence

DatabasesFactual

SequenceHomologyNucleicAcid

ChromosomeMapping

HumanGenomeProject

Figure 5 A fragment of the MeSH-centered semantic map created based on the analysis of John Craig Venterrsquos publications The nodes areMeSH-terms associated with the articles

Another subgraph consisted of the terms ldquoReceptorsrdquoldquoIsoproterenolrdquo and others (see Figure 5)This group of termscharacterizes the field of scientific interests of Craig Venterrelated to the study of alpha- and beta-adrenergic receptorsimmobilized drugs and their influence on the cardiacmuscleIn PubMed 25 publications were available on the queryldquoVenter JC catecholaminesrdquo All these publications date backto the period 1975-1990 They describe the properties ofimmobilized catecholamines Another search query ldquoVenterJC isoproterenolrdquo also revealed a similar number of publica-tions that coincide with the publications found for ldquoVenterJC catecholaminesrdquo query Thus the BiblioEngine enabled ina few hours obtaining a semantic map reflecting the mainconcepts of the sphere of Craig Venterrsquos scientific interests aswell as a list of his most important publications in each area

The approach suggested can also be illustrated by con-structing the semantic networks for proteins mentioned inthe articles published in Nature We have chosen 260 mostcommonly discussed proteins in two time periods 2008-2011 and 2016-2018 For each group of proteins we analyzedthe frequency of protein occurrence in all published inthe PubMed database articles in the selected period Wevisualized the most strong associations in the networks inFigure 6which illustrates associations between themost pop-ular proteins in 2008-2011 years In the center of the network

there are cancer-associated proteins such as ldquoTumor necrosisfactorrdquo and ldquoEpidermal Growth factorrdquo From Figure 6(b)it is clear that after 10 years the focus of scientific interesthas shifted to ubiquitin Ubiquitinylation of proteins of somesignaling pathways regulates their activity and as a resultmediates signal transmission to the nucleus Finally as it hasbeen recently discovered the functions of ubiquitin are alsoapplied to the regulation of the nucleus apparatus its rolein regulating the transcription of genes by modifying theRNA polymerase complex [40] A comparison of the twoprotein lists showed that they have two common proteinsPCNA HUMAN and CADH1 HUMAN

The simple idea of using BiblioEngine and BioKnol allowsusers to form concept-centered semantic networks (maps)organizing real-time PubMed-available knowledge in theform of semantic networks Networks represent relationshipsbetween various objects genes (proteins) MeSH chemicalcompounds names terms from the dictionary compiled byexperts names for providing information of an individual orgroup scientific output etc

Semantic networks were visualized using Cytoscapeaccording to the matrix of similarity and the distancebetween the nodes (concepts) was correlated with the nor-malized number of popular scientific articles Relevant frag-ments of a network as well as a list of PMIDs for each


260 proteins from gt6000 ldquoNaturerdquo publications

(a) ldquoNaturerdquo journal 2008-2011


(b) ldquoNaturerdquo journal 2016-2018

Figure 6 Protein-centered semanticmap created based on the analysis of the papers from ldquoNaturerdquo journal (a) 2008-2011 years (b) 2016-2018years

relationship detected could be provided to an expert for thefuture analysis

4 Conclusions

This work shows the possibility of using the BiblioEnginesoftware package combined with the BioKnol social networkfor an automated text-mining analysis of scientific literatureand creation of a personal cognitive map By the exampleof scientific publications of Alexander Archakov a semanticmap of key MeSH-based concepts was constructed allowingus to trace the main scientific directions of his work Thepractical use of the data obtained is directly related to thepossibility of predicting the scientific output of an individualor a group of researchers One of the proposed methodicalsolutions is to use the results of a text-analysis of the articlesviewed since this indicator is one of the most importantfactors in the scientific search of a scientist This studydemonstrates an obvious correlation between viewed alreadypublished scientific articles and those that will be publishedin the future Approved methodological approaches can beapplied to other authors and represent practical significancein terms of developing modern approaches to evaluation ofscientific output

Data AvailabilityThe data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of InterestThe authors declare that they have no conflicts of interest

AcknowledgmentsThe work was done within the framework of the StateAcademies of Sciences Fundamental Scientific Research Pro-gram for 2013-2020

Supplementary Materials

Supplementary 1 Supplementary Table 1 evolution of thescientific interests of Alexander Archakov over the past 50yearsSupplementary 2 Supplementary Note 1 BiblioEngineToolkit Manual

References

[1] M Kosmulski ldquoAre you in top 1rdquo Scientometrics vol 114 pp557ndash565 2018


[2] A D Baxevanis G A Petsko L D Stein and G D StormoSearching NCBI databases using Entrez Chapter 1 Unit 13Current Protocols in Bioinformatics Hoboken NJ USA 2002

[3] H Yang and H Lee ldquoResearch Trend Visualization by MeSHTerms from PubMedrdquo International Journal of EnvironmentalResearch and Public Health vol 15 no 6 p 1113 2018

[4] J A Mi M Kreuzthaler S Schulz and J A Minarro-GimenezldquoKnowledge Extraction from MEDLINE by Combining Clus-teringrdquo in Proceedings of the with Natural Language ProcessingAMIA Annu Symp proceedings pp 915ndash924 2015

[5] D Cameron R Kavuluru T C Rindflesch A P Sheth KThirunarayan and O Bodenreider ldquoContext-driven automaticsubgraph creation for literature-based discoveryrdquo Journal ofBiomedical Informatics vol 54 pp 141ndash157 2015

[6] P W Friedman B L Winnick C P Friedman and P CMickelson ldquoDevelopment of a MeSH-based index of facultyresearch interestsrdquo Proceedings of the Annual Symposium pp265ndash269 2000

[7] H Singh R Singh A Malhotra and M Kaur ldquoDevelopinga biomedical expert finding system using medical subjectheadingsrdquoHealth Informatics Journal vol 19 no 4 pp 243ndash2492013

[8] D Zhang N Roderer L Song and G Huang ldquoBuildinguser research interest profiles through a MeSH indexerrdquo inProceedings of the AMIA Annu Symp Proc 2007

[9] A Kastrin T C Rindflesch and D Hristovski ldquoLarge-scalestructure of a network of co-occurring MeSH terms Statisticalanalysis of macroscopic propertiesrdquo PLoS ONE vol 9 no 7Article ID e102188 2014

[10] A Kastrin T C Rindflesch andDHristovski ldquoLink predictionon a network of Co-occurringMeSH terms Towards literature-based discoveryrdquo Methods of Information in Medicine vol 55no 4 pp 340ndash346 2016

[11] E A Ponomarenko A V Lisitsa E V Ilrsquogisonis and A IArchakov ldquoConstruction of protein semantic networks usingPubMedMEDLINErdquo Journal of Molecular Biology vol 44 no1 pp 140ndash149 2010

[12] O Bodenreider ldquoThe Unified Medical Language System(UMLS) integrating biomedical terminologyrdquo Nucleic AcidsResearch vol 32 pp D267ndashD270 2004

[13] J A Mi and S Schulz ldquoAnalysis of MeSH indexing patterns andfrequency of predicatesrdquo in StudHealth Technol Inform vol 247pp 666ndash670 2018 httpebooksiospressnlpublication48875

[14] P Evdokimov A Kudryavtsev E Ilgisonis E Ponomarenkoand A Lisitsa ldquoUse of scientific social networking to improvethe research strategies of PubMed readersrdquo BMC ResearchNotes vol 9 no 1 article no 1920 2016

[15] AArchakovAAseevV Bykov et al ldquoGene-centric viewon thehuman proteome project the example of the Russian roadmapfor chromosome 18rdquo Proteomics vol 11 no 10 pp 1853ndash18562011

[16] P Shannon A Markiel O Ozier et al ldquoCytoscape a softwareEnvironment for integratedmodels of biomolecular interactionnetworksrdquo Genome Research vol 13 no 11 pp 2498ndash25042003

[17] D J Rogers and T T Tanimoto ldquoA computer program forclassifying plantsrdquo Science vol 132 no 3434 pp 1115ndash1118 1960

[18] A Doms and M Schroeder ldquoGoPubMed exploring PubMedwith the gene ontologyrdquo Nucleic Acids Research vol 33 no 2pp W783ndashW786 2005

[19] E A Ponomarenko A V Lisitsa S A Moshkovskii and A IArchakov ldquoIdentification of differentially expressed proteinsusing automatic meta-analysis of proteomics-related articlesrdquoBiomed Khim vol 55 no 1 pp 5ndash14 2009 httpswwwncbinlmnihgovpubmedterm=Identification+of+differentially+expressed+proteins+using+automatic+meta-analysis+of+prot-eomics-related+articles2C

[20] A V Lisitsa S A Gusev I I Karuzina A I Archakov andL Koymans ldquoCytochrome P450 databaserdquo SAR and QSAR inEnvironmental Research vol 12 no 4 pp 359ndash366 2001

[21] A Lisitsa A Archakov P Lewi and P Janssen ldquoBioinformaticInsight into the Unity and Diversity of Cytochromes P450rdquoMethods and Findings in Experimental and Clinical Pharmacol-ogy vol 25 no 9 pp 733ndash745 2003

[22] A Archakov A Seltsovskii V Tsyganov et al ldquoPhosphoglivmechanism of therapeutic action and clinical efficacyrdquo VoprMed Khim vol 48 no 2 pp 139ndash153 2002

[23] Y Chalenko V Shumyantseva S Ermolaeva and A ArchakovldquoElectrochemistry of Escherichia coli JM109 Direct electrontransfer and antibiotic resistancerdquo Biosensors and Bioelectronicsvol 32 no 1 pp 219ndash223 2012

[24] V V Shumyantseva T V Bulko and A I Archakov ldquoElectro-chemical reduction of cytochrome P450 as an approach to theconstruction of biosensors and bioreactorsrdquo Journal of InorganicBiochemistry vol 99 no 5 pp 1051ndash1063 2005

[25] V V Shumyantseva Y D Ivanov N Bistolas F W SchellerA I Archakov and U Wollenberger ldquoDirect electron transferof cytochrome P450 2B4 at electrodes modified with nonionicdetergent and colloidal clay nanoparticlesrdquo Analytical Chem-istry vol 76 no 20 pp 6046ndash6052 2004

[26] Y-K Paik C M Overall E W Deutsch J E Van Eyk and GS Omenn ldquoProgress and Future Direction of Chromosome-Centric Human Proteome Projectrdquo Journal of ProteomeResearch vol 16 no 12 pp 4253ndash4258 2017

[27] A T Kopylov E V Ilgisonis A A Moysa et al ldquoTargetedQuantitative Screening of Chromosome 18 Encoded Proteomein Plasma Samples of Astronaut Candidatesrdquo Journal of Pro-teome Research vol 15 no 11 pp 4039ndash4046 2016

[28] E Ponomarenko E Poverennaya M Pyatnitskiy et al ldquoCom-parative ranking of human chromosomes based on post-genomic datardquo OMICS A Journal of Integrative Biology vol 16no 11 pp 604ndash611 2012

[29] G S Omenn D J States M Adamski et al ldquoOverview ofthe HUPO Plasma Proteome Project Results from the pilotphase with 35 collaborating laboratories andmultiple analyticalgroups generating a core dataset of 3020 proteins and apublicly-available databaserdquo Proteomics vol 5 no 13 pp 3226ndash3245 2005

[30] P Legrain R Aebersold and A Archakov ldquoThe human pro-teome project current state and future directionrdquoMolecular ampCellular Proteomics vol 10 no 7 article 009993 2011

[31] A I Archakov V M Govorun A V Dubanov et al ldquoProtein-protein interactions as a target for drugs in proteomicsrdquoProteomics vol 3 no 4 pp 380ndash391 2003

[32] S A Moshkovskii M V Serebryakova K B Kuteykin-Teplyakov et al ldquoOvarian cancer marker of 117 kDa detectedby proteomics is a serum amyloid A1rdquo Proteomics vol 5 no 14pp 3790ndash3797 2005

[33] S M Douglas G T Montelione and M Gerstein ldquoPubNeta flexible system for visualizing literature derived networksrdquoGenome Biology vol 6 no 9 Article ID R80 2005


[34] P G Lokhov O P Trifonova D L Maslov et al ldquoDiagnosingimpaired glucose tolerance using direct infusion mass spec-trometry of blood plasmardquo PLoS ONE vol 9 no 9 Article IDe105343 2014

[35] O P Trifonova P G Lokhov and A I Archakov ldquoMetabolicprofiling of human bloodrdquo Biochemistry Supplement Series BBiomedical Chemistry vol 7 no 3 pp 179ndash186 2013

[36] A Archakov A Lisitsa E Ponomarenko andV Zgoda ldquoRecentadvances in proteomic profiling of human blood Clinicalscoperdquo Expert Review of Proteomics vol 12 no 2 pp 111ndash1132015

[37] A Lisitsa S Moshkovskii A Chernobrovkin E Ponomarenkoand A Archakov ldquoProfiling proteoforms promising follow-up of proteomics for biomarker discoveryrdquo Expert Review ofProteomics vol 11 no 1 pp 121ndash129 2014

[38] M A Karpova D S KarpovM V Ivanov et al ldquoExome-drivencharacterization of the cancer cell lines at the proteome levelThe NCI-60 case studyrdquo Journal of Proteome Research vol 13no 12 pp 5551ndash5560 2014

[39] A V Shargunov G S Krasnov E A Ponomarenko et alldquoTissue-specific alternative splicing analysis reveals the diver-sity of chromosome 18 transcriptomerdquo Journal of ProteomeResearch vol 13 no 1 pp 173ndash182 2014

[40] A Daulny F GengMMuratani J M Geisinger S E Salghettiand W P Tansey ldquoModulation of RNA polymerase II subunitcomposition by ubiquitylationrdquo Proceedings of the NationalAcadamy of Sciences of the United States of America vol 105no 50 pp 19649ndash19654 2008

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transport systems and discovery of natural compounds withtherapeutic properties [14]

In this work concept-centered semantic maps werecreated based on a text-mining analysis of PubMed usingthe BiblioEngine v2018 software The edges between the twoobjects (laquoconceptsraquo) were automatically calculated based onthe index of semantic similarity which is proportional to thenumber of publications related to both objects simultane-ously On the one hand an individual semantic map createdbased on the already published papers allows us to tracescientific thought On the other hand a prospective analysisbased on the study of search queries to PubMed enables us todetermine the possible directions for future research

The proposed approach was demonstrated using anexample of the creation of an individual cognitive map ofProfessor Alexander Archakov a biochemist and one of themost famous Russian scientists in the field of life scienceArchakov AI is a founder of a scientific school in the fieldof molecular organization studies His interests are focusedon the study of functioning the oxygenase cytochromeP450-containing system molecular mechanisms structureand function of membranes and biological oxidation Beingthe pioneer in the development of proteomics in RussiaArchakov AI headed the Chromosome 18 Team in the inter-national Human Proteome Project [15] The construction ofthe scientistrsquos semantic map is an interesting example sincehis scientific interests are changed every 10-15 years coveringbiochemistry bioinformatics nanotechnology proteomicspersonalized medicine and digital health

2 Materials and Methods

Todownload a list of relevant literature published in PubMedwe used the query [ldquoArchakov AIrdquo] A list of identifiers ofrelevant publications in PubMed (PMIDs) was created anda list of MeSH associated with each article was loaded (seeSupplementary Note 1 for algorithm details)

To quantify the semantic relationships between the nodesof the semantic map (MeSH-terms here) the BiblioEnginesoftwarewas used [11]The closest nodes on the semanticmapwere determined as the termsmost frequently occurred in thesame papers Selection of MeSH specific to the articles pub-lished by Alexander Archakov was performed by choosingMeSH the frequency of occurrence ofwhichwas significantlydifferent from that in the random sample Our randomsample consisted of a list of PMIDs randomly assembled fromall PMIDs presented in the PubMedMEDLINE library andthe size of the group that contained those randomly selectedPMIDs was equal to the number of PMIDs presented inthe targeted group in this case the number of PMIDs ofArchakovrsquos scientific articles

To assess how the scientific interests have changed overtime all the papers published were divided into groupsaccording to the release date (starting from the year 1970)Thus we have formed five groups including the articlespublished in 1970-1980 1980-1990 1990-2000 2000-2010and 2010-2017 respectively The time intervals were chosenin such a way that on the one hand the selections of paperswere comparable in volume and on the other they covered

a period that was significant for the research For each timeinterval a list of PMIDs and the corresponding MeSH wasformed

The semantic concept-centered map was created basedon the calculated matrix of semantic similarity between thenodes (MeSH-terms) and then visualized in the Cytoscapev361 program [16] The coefficient of semantic similarityT (119886 119887) between two objects (MeSH a and MeSH b) wascalculated using the Tanimoto normalization [17]

T (a b) =1003816100381610038161003816Pab1003816100381610038161003816

1003816100381610038161003816Pa1003816100381610038161003816 +1003816100381610038161003816Pb1003816100381610038161003816 minus1003816100381610038161003816Pab1003816100381610038161003816 (1)

where Pa is the number of PMIDs of the papers indexed withMeSH of a obtained as the number of papers returned byPubMed to a query (ldquoardquo [MeSH-Terms]rdquo) Pb is the numberof PMIDs of papers indexed with MeSH of b and Pab is thenumber of PMIDs of papers indexed with MeSH of a as wellas MeSH of b

The nodes formed a separate cluster if the measure ofsemantic similarity between the nodeswas in the range of 075to 1 For each time interval the MeSH-centered clusters werevisualized as a semantic network Additional information forannotation of the semantic network was obtained fromGoPubMed system [18] (httpgopubmedorg) and the Sco-pus bibliometric database (httpswwwelseviercomsolu-tionsscopus) We used GoPubMed for creating a list ofjournals in which relevant research articles retrieved fromPubMed were published and information on the number ofcitations was obtained from Scopus

For the creation of theMeSH-centered semantic networkbased on search history BioKnol [14] software was used Thefree available plugin that enables us to trace the whole searchhistory in the form of PMIDs or DOI of the content can beinstalled from httpwsbioknowledgecenterru While navi-gating PubMed a researcher generates user-specific readingprofiles that can be shared within the BioKnol social net-working environment Within the framework of the presentstudy we used the browsing history of scientific articles in thePubMed system for a one-year period (2012-2013) ReflectingArchakovrsquos present interests this period also allows us tocompare MeSH with his scientific papers published in 2014-2017

3 Results and Discussion

Over the period from 1968 to 2017 more than 600 publica-tions authored by Alexander Archakov were published ofwhich 424 papers are indexed in the PubMed system Thisnumber is sufficient enough for demonstrating the laquoproof-of-conceptraquo Indeed the number of objects on the semantic mapand its informativeness depend on the number of publishedarticles Our previous experience [19] has shown that onehundred publications are the quantity that allows us tocompare the results of text-mining processing with those ofbiocuration and expert analysis

The most cited articles (data about citing obtainedfrom Scopus) are published in such journals as ProteomicsBiosensors amp Bioelectronics Journal of Proteome Research












(a)

(b)

















Proteins

Mass-Spectrometry

Protein Binding

ROC Curve

Phenotype


Genome Human

Proteome

Computer Simulation

Biological Markers

Kinetics

Risk Assessment

Transcriptome

Metabolomics

Gene Expression










Kinetics

ReceptorsMuscarinic

Isoproterenol

MolecularWeight


Rats

CellMembrane


GenesBacterial

DNARepair


DNABacterial

OpenReadingFrames

EvolutionMolecular

GeneDuplication

Phylogeny

GenomeBacterial

CloningMolecular

Genomics

Genome


SequenceAnalysisDNA

SpeciesSpecificity


GenomeHuman

DNA

DNAComplementary

BaseSequence

DatabasesFactual


ChromosomeMapping

HumanGenomeProject














4 Conclusions







References













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018













(a)

(b)

















Proteins

Mass-Spectrometry

Protein Binding

ROC Curve

Phenotype


Genome Human

Proteome

Computer Simulation

Biological Markers

Kinetics

Risk Assessment

Transcriptome

Metabolomics

Gene Expression










Kinetics

ReceptorsMuscarinic

Isoproterenol

MolecularWeight


Rats

CellMembrane


GenesBacterial

DNARepair


DNABacterial

OpenReadingFrames

EvolutionMolecular

GeneDuplication

Phylogeny

GenomeBacterial

CloningMolecular

Genomics

Genome


SequenceAnalysisDNA

SpeciesSpecificity


GenomeHuman

DNA

DNAComplementary

BaseSequence

DatabasesFactual


ChromosomeMapping

HumanGenomeProject














4 Conclusions







References













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018



(a)

(b)

















Proteins

Mass-Spectrometry

Protein Binding

ROC Curve

Phenotype


Genome Human

Proteome

Computer Simulation

Biological Markers

Kinetics

Risk Assessment

Transcriptome

Metabolomics

Gene Expression










Kinetics

ReceptorsMuscarinic

Isoproterenol

MolecularWeight


Rats

CellMembrane


GenesBacterial

DNARepair


DNABacterial

OpenReadingFrames

EvolutionMolecular

GeneDuplication

Phylogeny

GenomeBacterial

CloningMolecular

Genomics

Genome


SequenceAnalysisDNA

SpeciesSpecificity


GenomeHuman

DNA

DNAComplementary

BaseSequence

DatabasesFactual


ChromosomeMapping

HumanGenomeProject














4 Conclusions







References













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018














Proteins

Mass-Spectrometry

Protein Binding

ROC Curve

Phenotype


Genome Human

Proteome

Computer Simulation

Biological Markers

Kinetics

Risk Assessment

Transcriptome

Metabolomics

Gene Expression










Kinetics

ReceptorsMuscarinic

Isoproterenol

MolecularWeight


Rats

CellMembrane


GenesBacterial

DNARepair


DNABacterial

OpenReadingFrames

EvolutionMolecular

GeneDuplication

Phylogeny

GenomeBacterial

CloningMolecular

Genomics

Genome


SequenceAnalysisDNA

SpeciesSpecificity


GenomeHuman

DNA

DNAComplementary

BaseSequence

DatabasesFactual


ChromosomeMapping

HumanGenomeProject














4 Conclusions







References













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018



Proteins

Mass-Spectrometry

Protein Binding

ROC Curve

Phenotype


Genome Human

Proteome

Computer Simulation

Biological Markers

Kinetics

Risk Assessment

Transcriptome

Metabolomics

Gene Expression










Kinetics

ReceptorsMuscarinic

Isoproterenol

MolecularWeight


Rats

CellMembrane


GenesBacterial

DNARepair


DNABacterial

OpenReadingFrames

EvolutionMolecular

GeneDuplication

Phylogeny

GenomeBacterial

CloningMolecular

Genomics

Genome


SequenceAnalysisDNA

SpeciesSpecificity


GenomeHuman

DNA

DNAComplementary

BaseSequence

DatabasesFactual


ChromosomeMapping

HumanGenomeProject














4 Conclusions







References













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018




Kinetics

ReceptorsMuscarinic

Isoproterenol

MolecularWeight


Rats

CellMembrane


GenesBacterial

DNARepair


DNABacterial

OpenReadingFrames

EvolutionMolecular

GeneDuplication

Phylogeny

GenomeBacterial

CloningMolecular

Genomics

Genome


SequenceAnalysisDNA

SpeciesSpecificity


GenomeHuman

DNA

DNAComplementary

BaseSequence

DatabasesFactual


ChromosomeMapping

HumanGenomeProject














4 Conclusions







References













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018









4 Conclusions







References













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018













































Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018












Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018




Volume 2018

Zoology











Volume 2018

















Advances in




Enzyme Research





Volume 2018


Documents

Creation of Individual Scientific Concept-Centered ...static.tongtianta.site › paper_pdf › 96ae2446-a48e-11e... · ResearchArticle Creation of Individual Scientific Concept-Centered