Ravikumar et al. BMC Bioinformatics (2015) 16:185 DOI 10.1186/s12859-015-0609-x

RESEARCH ARTICLE Open Access

Text mining facilitates database curation -extraction of mutation-disease associationsfrom Bio-medical literature

Komandur Elayavilli Ravikumar, PhD*, Kavishwar B. Wagholikar, MBBS, PhD, Dingcheng Li, PhD, Jean-Pierre Kocher, PhDand Hongfang Liu PhD*

Abstract

Background: Advances in the next generation sequencing technology has accelerated the pace of individualizedmedicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need ininterpreting sequencing data is the assembly of information about genetic variants and their correspondingassociations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such informationin biological databases, much of this information remains ‘locked’ in the unstructured text of biomedical publications.There is a substantial lag between the publication and the subsequent abstraction of such information intodatabases. Multiple text mining systems have been developed, but most of them focus on the sentence levelassociation extraction with performance evaluation based on gold standard text annotations specifically preparedfor text mining systems.

Results: We developed and evaluated a text mining system, MutD, which extracts protein mutation-diseaseassociations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extractedfrom curated database records. MutD achieves an F-measure of 64.3 % for reconstructing protein mutation diseaseassociations in curated database records. Discourse level analysis component of MutD contributed to a gain of morethan 10 % in F-measure when compared against the sentence level association extraction. Our error analysis indicatesthat 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects inthe curated database, the revised F-measure of MutD in association detection reaches 81.5 %.

Conclusions: Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associationswhen benchmarking based on curated database records. The analysis also demonstrates that incorporating discourselevel analysis significantly improved the performance of extracting the protein-mutation-disease association. Futurework includes the extension of MutD for full text articles.

Keywords: Mutation mining, Text mining, Protein mutation disease association

BackgroundRecent advances in next generation sequencing technol-ogy has accelerated the pace of individualized medicine(IM), which aims to incorporate genetic/genomic infor-mation into medicine [1]. One immediate need in inter-preting sequencing data is the assembly of information

* Correspondence: KomandurElayavilli.Ravikumar@mayo.edu;Liu.Hongfang@mayo.eduDepartment of Health Sciences Research, Mayo Clinic College of Medicine,200 First St SW, Harvick 3rd, Rochester, MN 55905, USA

© 2015 Ravikumar et al. This is an Open AccesLicense (http://creativecommons.org/licenses/medium, provided the original work is proper(http://creativecommons.org/publicdomain/zero

about genetic variants especially mutations in coding re-gions (i.e., protein mutations) and their associations withdiseases. Such information has been catalogued in mul-tiple biomedical databases such as ClinVar [2], OMIM[3], or UniProtKB [4]. Peterson et al., 2013 [5] list nearly21 resources containing protein mutation disease associa-tions. Even with dedicated effort in capturing such infor-mation in biomedical databases, much of this informationstill remains ‘locked’ in the unstructured text of biomedicalpublications. Additionally, the interpretation of “novel”

s article distributed under the terms of the Creative Commons Attributionby/4.0), which permits unrestricted use, distribution, and reproduction in anyly credited. The Creative Commons Public Domain Dedication waiver/1.0/) applies to the data made available in this article, unless otherwise stated.

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variants has been a significant task since some of the vari-ants may not be “novel” which are already published in theliterature. For example, the latest evidence indicates thatPSEN1, p.E318G variant is associated with the develop-ment of early-onset of Alzheimer’s disease (AD) forAPOE-ε4 carriers [6–10]. OMIM has not been updatedwith this clinically relevant finding. Instead OMIMstates “the E318G change is a polymorphism with un-certain clinical significance” based on the 2005 publica-tion [11]. Similarly, many of these databases includingUniProtKB and NCBI ClinVar fail to report the signifi-cance of the PSEN1, p.E318G variant with early onsetAD. Thus, the development of text mining approachesto accelerate the process of assembling IM knowledgein published literature is necessary.Text mining techniques can be potentially applied to

mitigate such information lag [12]. Multiple text miningsystems have been developed for extracting mutationevents (e.g., Mutation-Finder [13] and EMU [14], tmVAR[15]); as well as relation extraction to associate mutationevents to other entities such as proteins, (e.g., MEMA[16], MuteXt [17], MuGeX [18] and Mutation-GraB [19]),genes (e.g., Polysearch [20]), diseases (e.g., SNPShot [21]),and drugs (e.g., SNPShot). Most of these systems use regu-lar expressions to detect mutation mentions form bio-medical text but associations between mutations andother entities are based on simple co-occurrence informa-tion. For example, SNPShot extracts various binary rela-tions between genes, variants, drugs, diseases and drugreactions using simple co-occurrence statistics andnormalize entities to databases such as EntrezGene [22],PharmGKB [23], or PubChem [24].MEMA [16] and MuteXt [17] applied word distance

metric to retain the right protein-mutation pairs ex-tracted based on sentence co-occurrence.; Mutation-GraB [19] applied weighted graph-based traversal to re-solve ambiguity and retain only the protein-mutationpairs that have the shortest path between them. On theother hand, Doughty et al. [14] have proposed the use ofsequence information to validate the associations. Moresophisticated approaches based on grammar parsing havebeen extensively employed for relation extraction in theNLP shared task. For example, Open Mutation Miner [25]attempts to extract the impact of mutation on proteinfunction and its properties such as kinetics and stabilityusing a rule-based approach based on shallow parsing.Some recent studies on automatic linking of protein mu-tations and diseases consider advanced linguistic featuressuch as dependency parse graphs [26,27].

RationaleMost of the previous studies discussed above [17,18,20,21]have estimated their text mining performance against text-based gold standard annotations and not against human

curated gold standard annotations in the databases. Whilethe text based gold standard annotations consider only theinformation given in the document for annotation, data-base annotations may infer information from multipledocuments relevant to the task for curation besides thecurator’s domain knowledge. Hence evaluating textmining systems against biomedical text gold standardsdoes not estimate the “practical utility” of these systemsfor database curation task. In this section, we briefly dis-cuss the rationale behind this work and the issues that weattempt to address in the current study.

Biomedical database curation workflow and the role of textminingDatabase curation broadly involves the following steps[28] generally followed by human curators with the re-quired domain knowledge.

Step 1 Problem and curation guideline definition -Define the problem domain selected for curationand its guidelines,

Step 2 Collection of literature pool – Collect the seedliterature pool through appropriate choice of searchterms using information retrieval engines suchas PubMed.

Step 3 Retrieval of relevant article – Retrieve therelevant abstracts/articles from the literature toolusing API such as Entrez e-utils in PubMed.

Step 4 Identification of relevant evidence sentences –Manually read the literature and identify theevidence sentences in the individual abstract/articlebased on their relevance to the target problemconsidered for curation.

Step 5 Identification of entities – Identify and markup the entity mentions in the text.

Step 6 Normalization of entity mentions to databaseconcepts - Normalize the entity mentions in the textto ontological concepts or database identifiers.

Step 7 Identification of relations between entities -Extract the relations between the entity mentionsboth within and across sentences.

Step 8 Store the abstracted concepts and relation inthe database – This is the final step that involvestransformation of information in natural languageto structured representation in flat file such as XMLor in a relational database.

The major bottleneck in the database curation work-flow is the manual effort required in each of these steps.Text mining systems have been used to assist the cura-tors to accelerate the curation workflow, mainly for theinitial steps from 1 to 4. The performance of the existingtools is unsatisfactory for the latter steps from 5 to 7.Steps 5–7 involve normalization and inferences, which

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require synthesis of information across sentences, doc-uments, and domain knowledge sources such as bio-medical ontologies.Figure 1 illustrates two major inference challenges: lin-

guistic inference and expert inference using two sen-tences from an abstract (PMID-8112750) describingthe association between a point mutation “Asp399Asn”of “glucocerebrosidase gene” and “Gaucher’s disease”.While the first sentence has the mentions of gene/pro-tein and disease, it is the second sentence that providesthe site of mutation information. Linguistic inference isrequired to connect the two pieces of information men-tioned in different sentences to infer the role of muta-tion in a disease. Current text mining tools fall short ofmaking such linguistic inferences. Prior to relation ex-traction, normalization of the entities to the ontological/database definitions requires inference predominantlybased on the background knowledge and the linguisticinference drawn from the description in the current text.The state of the art of entity normalization, particularlythe gene and disease has been steadily increasing to ac-ceptable levels thanks to the organization of series ofshared tasks such as BioCreative [29,30]. However,normalization of the mutation site mentions in the textto the sequence in biomedical databases has been a chal-lenge. It requires inference by the curation expert tojudge whether the mutation residue position mentionedin the text accounts for the signal peptide region. In theexample shown in Figure 1 the position of mutation Aspto Asn is mentioned as “399” in the text. However, thegold standard annotation in UniProtKB mentions the

Fig. 1 Role of human inference in database curation

site of mutation to be “438”. The curator has utilized his/her biological knowledge to infer that the sequence re-ported in the paper does not include signal peptide region(a total of 39 residues). The length of signal peptide re-gion, 39 is added to the position mentioned in the text,399, which gives the final position ,438, mentioned in thegold standard. Such expert inference capability is beyondany text mining system.Leveraging the existing knowledgebase annotations for

text mining often referred to as weakly supervised/dis-tant supervision learning has been on the rise in otherdomains [31,32]. Such distant supervision approaches[33,34] were explored in a limited context for learningpatterns relating protein and its sites mentioned in a sin-gle sentence using multiple database annotations such asUniProtKB and PDB.In this paper, we present a text mining system, MutD,

which extracts protein mutation-disease associationsfrom MEDLINE abstracts by incorporating discourselevel analysis. We evaluated the performance of MutDto extract mutation disease relations against human cu-rated annotations in UniProtKB as the gold standard. Inthe following sections, we describe tools and resources,our discourse level analysis approach, and experimentalmethods.

MethodsResources and ToolsThe system developed for this study (MutD) was imple-mented under Apache Unstructured Information Man-agement Architecture (UIMA) [35]. We used the Unified

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Medical Language System (UMLS) (version 2012 AB)[36], BioThesaurus (V2.1) [37], Comparative Toxicoge-nomics Database (CTD)’s [38], and merged disease vo-cabulary (MEDIC) [39] ontology as terminology sourcesfor recognizing diseases and proteins mentioned in thetext. BioTagger-GM [40] is used for dictionary lookup.We used Stanford parser [41] to generate the depend-ency parse representation from individual sentences.For entity recognition and normalization, in additionto customized BioTagger-GM [40] we also used exist-ing systems, MutationFinder [13], PubTator [42] andGeNo [43]. We used UniProtKB to extract curatedternary relations to form a gold standard data set todevelop, refine and evaluate the system. The followingprovides a brief background of each of resources andsystems.

UIMAApache UIMA [35], is a data-driven architecture wherecomponents communicate with each other throughCommon Analysis System (CAS) containing annota-tions. Each annotation is an instance of a given type de-fined by a specified hierarchical type system.

UniprotKBUniProtKB [4] is a protein database partially curated byexperts, consisting of two sections: SwissProt where en-tries have been manually annotated and TrEMBL whereentries are automatically annotated. The manual anno-tation process of an entry in UniProtKB involves de-tailed analysis of the protein sequence with thescientific literature being the primary evidence. For thisstudy, we specifically use the curated information aboutmutation and its role in diseases. Only the SwissProt en-tries are used to automatically generate the gold stand-ard data set.

BioThesaurusBioThesaurus [37] is a web-based system designed tomap a comprehensive collection of protein and genenames to protein records in UniProtKB [4]. Currentlycovering four million proteins, BioThesaurus consists ofover 6 million names extracted from multiple molecularbiological databases according to the database cross-references in iProClass [44]. The data is downloadablefrom the iProLINK [45].

MEDICMEDIC is a comprehensive collection of disease namesfrom two major resources namely MeSH [15,36] andOMIM [3]. The OMIM entries are mapped to hierarch-ical entries in MeSH ontology. This results in not only

the integration of both these resources but provides ahierarchical structure to OMIM ontology as well. MEDICconsists of 9700 unique diseases with close to 67, 000terms that includes synonyms.

BioTagger-GMBioTagger-GM [40] is a gene recognition system, whichtakes a hybrid approach to recognize gene names. In thefirst step, the system combines dictionary lookup with ma-chine learning to detect gene names. Subsequently heuris-tics were used to filter false positives. The system finallyperforms a voting on the output of all methods, and buildsa consensus among them. The system achieved F-measureof over 85 % for the gene mention task, and ~78 % for thegene normalization task. Here gene mention refers to thedetection of the spans of gene names in the text, whilegene normalization refers to associating a specific genemention to a unique Entrez gene symbol. BioTagger-GM is the state of the art system for gene mention andnormalization.

MutationFinder (MF)MutationFinder [13] is an open-source, high-performanceinformation extraction system that extracts mentions ofpoint mutations from free text. MutationFinder applies aset of approximately 700 regular expressions to identifymutation mentions in the input text. On blind test data,the precision of MF is 98.4 % and recall 81.9 %, whenextracting point mutation mentions. We used Muta-tionFinder version 1.1 for this study, which is imple-mented in UIMA.

GeNoGeNo [43] is a gene normalization tool that uses a set ofsymbolic and statistical methods by fully relying onpublicly available software and data resources, includingextensive background knowledge based on semanticprofiling. GeNO achieved a F-measure of 86.4 % for thegene normalization task on the BioCreative-II [29,30]test set. GeNO is available as a remotely employableUIMA Analysis Engine (AE). This AE bundles the genemapper with key components like named entity recog-nition component JNET [46].

PubTatorPubTator [42] is a web-based service from NCBI thatdelivers entity annotations such as Gene, Mutation,Disease, Species and Chemical for PubMed abstracts.The entities are further normalized to ontological defini-tions where genes are normalized to Entrez Gene Id, dis-ease and chemical names are normalized to MeSH IDand species to NCBI taxonomy. PubTator is an ensembleof state of the art algorithms for entity detection namelyGenTUKit [47] for detecting gene mentions, GenNorm

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[48] for Gene normalization and SR4GN [49] for speciesdetection and normalization and DNorm [50] for diseaseentity mentions and normalization.

System implementationFigure 2 illustrates the overall system architectureimplemented for this study. The system includes com-ponents for retrieval of abstracts, sentence detection,tokenization, lexical normalization, dictionary lookupfor entity normalization, and relation extraction. Thefollowing sections briefly describe the details of each ofthe components.

RetrievalThe pipeline starts with the retrieval of PubMed abstractsfor a given query (PMID in the current work) using theEntrez e-utils web service [51].

PreprocessingThe system includes a series of pre-processing modulessuch as sentence splitting and tokenization using OpenNLP[52]. It also includes a lexical normalization component,which converts words to a canonical form using the Lex-ical variant generator (LVG) [53] provided by the NationalLibrary of Medicine (NLM).

Term identificationThe term identification component has modules torecognize protein, point mutations, and disease termsfrom PubMed abstracts. We retrieved the entity annota-tions: protein/gene, mutation and disease for all thePubMed abstracts considered for this study using theweb service of PubTator [42] through the web service

Fig. 2 System architecture

provided by the tool. The protein/gene, mutation, anddisease from PubTator normalized to Entrez Gene ID,dbSNP/MEDIC, and MeSH respectively. For this studywe considered only the point mutations (substitutions)annotated by tmVAR of PubTator. Besides PubTator, wealso used entity recognition for protein from the Entrez/UniprotKB, applied within a BioTagger-GM [40] frame-work. For mutations, we also use MutationFinder [13] todetect protein point mutations from the text. We raneach of these tools individually and finally retain all thenormalized annotations from the combination of a selec-tion of state of the art tools such as PubTator, GeNO,BioTagger-GM, and Mutation-Finder. Wherever there isdisagreement between the entity boundaries we consid-ered the longest span of the entity.

Detection of abbreviationsAbbreviations are quite often used in biomedical litera-ture. Entity detection tools may identify abbreviations andthe associated definitions (protein/disease). We use theSchwartz algorithm [54] to detect abbreviations. If thelong form is identified as an entity, we also assign thesame semantic type and ontology id to the short formwithin the scope of the abstract.

Graph traversal in the dependency parse graphrepresentationPost entity extraction we simplify the sentences by re-placing the actual entities in the sentence with thenormalized entities. For example consider the followingsentence from an abstract: “A presenilin 1 mutation(Ser169Pro) associated with early-onset AD and myo-clonic seizures”. We replace the actual entity mentions

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with either Ontology IDs or symbols, which can beretraced in the text. Protein mention such as “presenilin1” in the above sentence is replaced by its Uniprot ID(PSN1_HUMAN). Similarly, disease mentions such as“early onset AD” and “myoclonic seizures” are respect-ively replaced by MeSH Ids “D000544” and “D004831”.We replace the mutation mentions with the text “MUT”followed by an incremental index number as they occurin the text. If two mutations are identical, they are re-placed by the same index. For example in the examplesentence, the mention of Ser169Pro is replaced withMUT0. The original sentence after replacements is sim-plified to: “A PSN1_HUMAN mutation (MUT0) associ-ated with D000544 and D004831.” We then processthe simplified sentence through Stanford dependencyparser [41] and obtain the dependency graph, whichcaptures the dependencies between the words in themodified sentence. We use the collapsed dependenciesgraph representation, which propagates the conjunctiondependencies and collapses the dependencies involvingprepositions. The approach is similar to the one pro-posed by Coulet et al. 2010 [26]. During the graph tra-versal, we assume that if the three target entities (i.e.protein, mutation and disease) share a common node,they are related to each other. Figure 3 illustrate all theindividual steps in relationship extraction throughgraph traversal using an example sentence involving allthe three entities.

Extra-Sentential processingQuite often we do not find all the three entities (i.e.protein, mutation, and disease) occur in a single sen-tence. They are often located in different sentences. Tohandle such cases we developed methods to associateentities across multiple sentences. Our approach in-cludes two steps:

Fig. 3 Steps in dependency parse graph traversal

Linking dependency graphs from multiple sentencesAs the first step towards extracting information fromdifferent sentences, we link dependency graphs across sen-tences based on entity identifiers. If dependency graphsfrom two or more individual sentences share the sameentity, we link the graphs. Consider the following two sen-tences from an abstract (PMID: 21062920) where the entitymentions are replaced with database identifiers/symbols.“Mechanistic insights into C566471 caused by DSC2_HU-MAN mutations.” and “The two missense mutations(DSC2_HUMAN MUT5 and MUT6) have been function-ally characterized …” The dependency parse graph traver-sal for the first sentence (shown in Figure 4) yields only therelationship between the disease arrhythmogenic right ven-tricular cardiomyopathy (MesH: C566471) and the proteindesmocollin-2 (UniProt ID: DSC2_HUMAN). No pointmutation is mentioned within the same sentence. Howeverin one of the subsequent sentence there is a mention ofthe same protein and its two point mutations. The rela-tion between the two mutations R203C (replaced withMUT5) and T275M (replaced with MUT6) and the pro-tein (DSC2_HUMAN) is extracted by the dependencyparse graph traversal as shown in Fig. 4. Since the proteinDSC2_HUMAN is common between the two graphs welinked these two graphs, which yield a connection betweenthe three entities yielding two ternary relations namely <DSC2_HUMAN, R203C, C566471 > and <DSC2_HU-MAN,T275M, C566471 > .

Linking dependency graphs across sentences usinganaphora and trigger words In scientific discourse, it isquite common to use anaphora to refer to entities men-tioned in other portions of the text. In this work, we con-sider linking dependency parse graphs from multiplesentences through anaphora resolution. We adapted theanaphora resolution system that is described in Ravikumaret al., 2013 [55]. Besides anaphora, we observed a lot of

Fig. 4 Linking dependency graphs on entity identity

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other terms, which we call, 'trigger words' that refer to en-tities that are mentioned in earlier sentences or one of thesubsequent sentences. We treat such trigger words (e.g.mutation, mutant, variations, variants, mutational, patient,phenotype) as anaphoric though they may not be consid-ered anaphora category in strict grammar sense. Figure 5illustrate how anaphoric references are used to link twodependency graphs from different sentences.

Post-processingA few post-processing steps are implemented afterextracting these relations. We replace the modified mu-tations with the original entities during this step. Thenwe separate the three individual components of pointmutations from PubTator namely the wild type residue,the mutant residue and the position of the residue in theamino acid sequence. Next, we map the mutant and the

Fig. 5 Linking dependency graphs based on discourse analysis

wild type residue from its single letter or full name no-tation to its three-letter notation. We also map the dis-ease MeSH Ids to OMIM Ids through MEDIC ontology[39] available in CTD [38]. Mapping disease terms toOMIM is essential as the disease annotation in UniProtKBis normalized to OMIM entries. For example, the ternaryrelation <DSC2_HUMAN, R203C, C566471 > extractedfrom the text, will be finally transformed to <DSC2_HU-MAN, Arg, 203, 275, Cys, 610476 >where DSC2_HU-MAN is UniProtKB ID, Arg is the wild type residue, 203represents the residue position, 275 the normalized resi-due position after accounting for the signal peptide re-gion, Cys is the mutant residue and 610476 is theOMIM Id for the disease arrhythmogenic right ventricu-lar cardiomyopathy. If there is more than one OMIM Idassociated with a MeSH Id, we retain all the OMIM Idsfor that output.

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ExperimentsFigure 6 illustrates the experimental workflow that weadopted in the study. We first extracted gold standardinstances from curated database records in UniProtKB.Specifically, we used SwissProt, the manually curated por-tion in UniProtKB, to extract protein mutation disease as-sociations that are used as a gold standard.Figure 7 shows an example illustrating the steps in-

volved in extracting gold standard instances. Specifically,we extract UniProtKB ID (APC_HUMAN), wild type(Ala), its position in the sequence (1296), mutant residue(Val), OMIM ID (155255, i.e., Medulloblastoma) fromthe curated portion of UniProtKB (i.e., SwissProt) andthe cited PubMed Id (PMID - 10666372). If the sequenceannotation has signal peptide region, we subtract thelength of the signal peptide from the mutation site pos-ition, which results in an expanded annotation containingthe adjusted sequence position. If the sequence position isnot mentioned, we retain the original position of the mu-tation for the adjusted position. As shown in Fig. 7, thefinal gold standard annotation is recorded as <APC_HU-MAN, Ala, 1296, 1296, Val; 155255, 10666372>. Quiteoften we observe that the mutation position mentioned inUniProtKB ID differed with the position mentioned in thetext. This difference is mainly due to the length of the sig-nal peptide region. In such cases, We adjust the mutationposition by subtracting the length of the signal peptide re-gion (39 in case of GLCM_HUMAN) from the one men-tioned in the UniProtKB.

Fig. 6 Experimental design

Since our focus was on association detection, weretained only those abstracts with at least one diseasemention, one gene/protein mention and one mutationmention (tmVAR and MutationFinder). The data setwas then split into two sets namely development (D)and test (T) sets. We used the development set to de-velop the MutD system and the test set for blind evalu-ation. We compared five variations of the system wherethe first two systems (S1 and S2) served as the baselineand the remaining three (S3, S4, and S5) utilized de-pendency graph traversal.

� S1 uses abstract-level co-occurrence.� S2 uses sentence-level co-occurrence.� S3 performs dependency graph traversal of a

single sentence.� S4 links dependency graphs across multiple

sentences, using entity identifiers.� S5 links dependency graphs, using entity identifiers,

anaphoric terms, and trigger words.

Evaluation metricsWe report the results in terms of precision, recalland F-measure where precision is a measure of sys-tem accuracy, recall its coverage, and F-measure, aharmonic mean of precision and recall. Let TP, FP,and FN be the number of correct, incorrect, and missedassociations extracted by the system in comparison withthe gold standard, respectively. The precision, recall,

Fig. 7 Extraction of gold standard from UniProtKB

Table 1 Gold standard relation statistics (Development and testdata set)

S. No Relation Data set Total numbers

1 Protein-Mutation-Disease (PMD) D 631

T 264

2 Protein – Mutation (PM) D 879

T 388

3 Protein-Disease (PD) D 671

T 295

D – Development set; T – Test set

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and F-measure are computed as follows: Precision ¼TP

TPþFP ; ¼ TPTPþFN ; and F−Measure ¼ 2�Precision�Recall

PrecisionþRecallð Þ .

Results and discussionGold standard data set extracted from UniProtKBThere are 2,613 abstracts in total cited as literature evi-dence of curated protein mutation disease associations.Using state-of-the-art entity mention tools, we found 720abstracts from 497 UniProtKB records with at least onedisease mention, one mutation mention, and one gene/protein mention. We retrieved the title and abstract of thePubMed articles cited as evidence in the records usingBatch Entrez (e-utils). The 720 abstracts were divided intodevelopment (576) and testing (144) with a ratio of 80:20.The development and test data sets contain 631 and 264gold standard associations respectively.Table 1 summarizes the ternary and binary relations con-

tained in the development (D) and test (T) data sets. Notethat not all binary relations are part of ternary relations.For example, there are 40 more protein-disease relations inthe development data set but the underlying association isnot about protein mutation disease information.

System performanceIn the task of relation extraction, we have two subtasks.1) Named entity recognition and 2) Protein-Mutation-Disease relation extraction. We did not formally evaluatethe named entity recognition and normalization in thecurrent study due to our choice of considering UniProtKB

as the gold standard. Evaluating named entity recognitionand normalization against UniProtKB as Gold standardhas the following limitations. PubMed (PMID) cited as areference in UniProtKB against a particular protein oftencontain entities mentioned in an abstract. Besides weconsidered only those UniProtKB entries, which containProtein-Mutation-Disease relation curated in UniProtKB.PubMed abstracts also contain other entity names, whichmay not be the focus of the current paper. Even thoughthe entity mention detection is correct as per the text,since the PMID is not found in UniProtKB against thoseprotein entries, such entities will be considered as inaccur-ate. Our estimation of Precision, Recall and F-measurewill also not be accurate. For example, the abstract withPMID-21828135 is cited as a reference in 3 UniProtIDs(KDM6A_HUMAN, DNM3A_HUMAN, and EZH2_HU-MAN). However, the abstract contains a sentence “TET2mutations were present in 49 %, ASXL1 in 43 %, CBL in

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14 %, IDH1/2 in 4 %, KRAS in 7 %, NRAS in 4 %, andJAK2 V617F in 1 % of patients.” where additional proteinnames (in bold) were identified and normalized to respect-ive UniProtIDs. While they are correct considering the in-formation in the abstract, they will be wrong since theyare not annotated in UniProtKB. Considering these fac-tors, we did not perform evaluation for named entity per-formance and instead chose only the state of the art toolsthat are known to perform well in the detection andnormalization of protein, mutation and disease entities inthe text which have performed really well on shared tasks.Table 2 lists the evaluation results on the development

and test data sets of all the five system variants respect-ively. The columns under heading PMD in Table 2 referto the performance of the ternary relation extractionamong protein, mutation and disease. The performancemetrics of binary relations such as protein-mutation(under heading PM), mutation-disease (under headingMD), and protein-disease (under heading PD) relationsare also shown in Table 2. Figure 8 plots the performanceof the ternary relation and Figure 9 intends to bring in-sights on the performance of individual systems and alsohighlight the gaps in their performance.The abstract level co-occurrence (S1) has the highest

recall, while the sentence level dependency graph traver-sal (S3) achieved the highest precision for extracting theternary association between protein, mutation and dis-ease. The maximum F-measure (achieved by S5) on thedevelopment and test sets is 66.7 % and 64.3 % respect-ively, close to 4 % percentage gain in the F-measure thanabstract level co-occurrence (S1). Another interesting ob-servation is that we found only 36 % of the ternary rela-tions within a single sentence (S2).We observed a trend similar to that of ternary relation

extraction in the case of binary relation extraction. While

Table 2 Evaluation results on development (D) and test (T) data set

Sys Set PMD PM

P R F P R

S1 D 60.0 69.3 64.3 69.2 68.4

T 52.6 72.0 60.8 65.5 71.4

S2 D 76.2 39.0 51.6 82.6 48.1

T 77.3 41.3 53.8 76.3 43.0

S3 D 77.1 36.3 49.7 84.4 45.6

T 78.7 36.4 49.7 79.1 38.9

S4 D 76.4 52.3 60.3 84.4 45.6

T 75.8 52.3 61.9 79.1 38.9

S5 D 75.8 59.6 66.7 81.7 57.0

T 71.6 58.3 64.3 76.8 58.0

Sys – Systems; PMD – Protein-Mutation-Disease relationships; PM – Protein-Mutatiotionships; S1 (System1) – Abstract level co-occurrence; S2 (System2) – Sentence levversal; S4 (System4) – Linking two dependency graphs based on entity identity; S5words.; P – Precision (in %); R – Recall (in %); F – F-measure (in %)

the abstract level co-occurrence (S1) achieved the highestrecall S3 achieved the highest precision, and S5 achievedthe best F-measure for almost all the binary relation ex-traction tasks.

Error analysisIn order to have better understanding of the system per-formance, we performed manual error analysis on the out-put of the best system (S5) using the test data set.Figures 10 and 11 illustrate the percentage distribution ofboth the precision and recall errors respectively.There were 64 precision errors categorized in the

following:

Absence of annotation in UniProtKBWe found nearly 23 (31 %) of the associations extractedby the system not captured by UniProtKB. For example,consider the following sentence from an example ab-stract (PMID - 9973276): “We conclude that the APCI1307K variant leads to increased adenoma formationand directly contributes to 3 %-4 % of all AshkenaziJewish colorectal cancer.” The system extracted the tern-ary relation <APC_HUMAN, Ile, 1307, Lys, 114500 >from the above sentence. However, in UniProtKB for“APC_HUMAN” entry the annotation contains onlyprotein-mutation binary relation (APC_HUMAN andLys1307) leaving out the disease colorectal cancer.

Entity detection errorsErrors in entity detection are another significant source(~22 %) for precision errors. Such errors happen whenthere is an overlap in the text span between two entityclasses. Consider the following sentence from an abstract(PMID - 11565064) “A homozygous R279W mutationwas recently found in the diastrophic dysplasia sulfate

s

MD PD

F P R F P R F

68.8 61.7 67.7 64.6 67.2 80.6 73.3

68.3 57.0 70.9 63.2 61.1 80.9 69.6

48.1 74.8 44.3 55.6 84.6 59.8 70.0

55.0 67.8 43.6 53.1 74.7 61.4 67.4

59.2 78.2 42.5 55.0 89.3 57.4 69.9

52.2 77.2 41.8 54.3 76.7 59.7 67.2

59.2 78.2 42.5 55.0 89.3 57.4 69.9

52.2 77.2 41.8 54.3 76.7 59.7 67.2

67.2 75.9 60.0 67.0 88.6 63.8 74.2

67.2 74.8 59.3 66.1 76.2 67.7 71.7

n relationships; MD – Mutation-Disease relationships; PD – Protein-disease rela-el co-occurrence; S3 (System3) – Sentence level dependency graph based tra-(System5) – Linking two or more graphs based on anaphora resolution/trigger

Fig. 8 Performance trend of systems on development and test data set

Ravikumar et al. BMC Bioinformatics (2015) 16:185 Page 11 of 15

transporter gene, DTDST, in a patient with MED whohad a club foot and double-layered patella.” MutD cor-rectly extracted the association among gene DTDST,mutation R279W, and disease MED. However, the namedentity recognition algorithm failed to recognize “dia-strophic dysplasia sulfate transporter gene” as a singleentity (gene/protein). Instead, MutD identified “dia-strophic dysplasia” as a disease and normalized it toOMIM Id: 222600. This led to the extraction of a falseassociation between gene DTDST, mutation R279W,and disease diastrophic dysplasia.

Fig. 9 Comparison of performance of systems on test data

Entity normalization errorsErrors in entity normalization contribute significantly(23.44 %) to precision errors. For example consider thefollowing sentence from an abstract (PMID: 18678517):“Thus, the current study identified the DSG2-V55Mpolymorphism as a novel risk variant for DCM associ-ated with shortened desmosomes of the cardiac inter-calated disc”. Three systems (S3, S4, and S5) gives thefollowing output <DSG2_HUMAN, Val, 55, Met, 115200|613424|613642>. The gold standard annotationfor this abstract reads as follows: <DSG2_HUMAN,

Fig. 10 Percentage distribution of precision errors on test data set

Ravikumar et al. BMC Bioinformatics (2015) 16:185 Page 12 of 15

Val, 55, 56, Met, 612877>. While all the fields matchedcorrectly, none of the three OMIM Ids identified by thesystems matched the one in the gold standard annotation.

Errors in linking dependency graphs through anaphora/trigger wordsLinking dependency graphs through trigger words causesome of the errors. Consider the following two sentencesfrom an abstract (PMID-21828135): “Mutational spectrumanalysis of chronic myelomonocytic leukemia includesgenes associated with epigenetic regulation: UTX, EZH2,and DNMT3A.” and “TET2 mutations were present in49 %, ASXL1 in 43 %, CBL in 14 %, IDH1/2 in 4 %,KRAS in 7 %, NRAS in 4 %, and JAK2 V617F in 1 % ofpatients”. The trigger word “Mutational” (base form: Mu-tation) in the first sentence is linked to the point mutationV617F in the second sentence leading to incorrect associa-tions among protein JAK2, mutation V617F and disease

60.18

10.62

12.39

13.27

3.54

Percentage E

Fig. 11 Percentage distribution of recall errors on test data set

chronic myelomonocytic leukemia. Apart from these, de-pendency parsing also contributed to 3 % of the errors.Totally, we identified 113 recall errors, detailed in the

following:

Absence of entities in the textAbsence of entities in the text has been the major causefor the recall errors. Nearly 60 % of the errors are due tothe absence of entity mentions in the abstract. For Uni-ProtKB entry “APC_HUMAN” the variant “CYS-1395”is annotated to be involved in “HEPATOBLASTOMA”where the PMID “8764128” is cited as the literatureevidence. However in the PubMed abstract there is nomention of any protein point mutation or variant. Thealgorithm correctly extracts the protein-disease relation.In UniProt entry GLCM_HUMAN, four variants (SER-409, HIS-448, PRO-483 and CYS-502) are annotated toplay a role in Gaucher’s disease with PMID “7627184”

rrors

Absence of entities in text

Failure to detect entities in text

Errors in Entity

Errors in Entity Normalization

Parsing errors

Ravikumar et al. BMC Bioinformatics (2015) 16:185 Page 13 of 15

as the literature evidence. After subtracting the length ofsignal peptide region (39) from the residue positions, themodified variants are converted into SER-370, HIS-409,PRO-444 and CYS-463 respectively. While all the fourmodified variants and the disease are mentioned in theabstract, the protein is not even mentioned anywhere inthe abstract, which results in 4 recall errors. Restrictingour processing to only biomedical abstracts is a primaryreason for this error.

Failure to detect entitiesFailure to detect entities is another source for recallerrors. Consider the following sentence from an abstract(PMID-1972019): “One mutation consists of a single-base substitution in three different codons: codon 444,Leu (CTG) to Pro (CCG); codon 456, Ala (GCT) to Pro(CCT); and codon 460, Val (GTG) to Val (GTC).” BothPubTator and Mutation-Finder (even with supplementedpatterns) failed to recognize all the three mutation men-tioned in the sentence resulting in recall errors.

Errors caused by precision errorsThere are 41 precision errors also leading to recall errors.For example consider the following sentence: “Thus, thecurrent study identified the DSG2-V55M polymorphismas a novel risk variant for DCM associated with shorteneddesmosomes of the cardiac intercalated disc.” In this sen-tence, the disease “DCM” is not normalized to the OMIMID annotated in the gold standard (UniprotKB). Such er-rors in entity normalization lead to both precision errorand recall error.The above detailed error analysis indicates 23 of the

64 precision errors are actual associations failed to becaptured by database curators and 68 of the 113 recallerrors are caused by the absence of associated disease en-tities in the abstract. If we neglect those errors, the ad-justed precision, recall, and F-measure of S5 in associationdetection reach 82.3 %, 80.8 %, and 81.5 % respectively.

Limitations and future directionsIn this study, our predominant focus was to address thelinguistic inference challenge from the text for databasecuration. We have not paid much attention to the initialsteps (Step 1 and 2) and the expert inference challenge.Our approach to discourse level analysis is more effect-ive when the scope of the problem under investigation ishighly targeted like the one in this study. However, webelieve that the issue of an expert inference cannot becompletely addressed by text mining.In addition, nearly 30-40 % of the gold standard tern-

ary relations were missed by the system due to the ab-sence of relevant entities in the abstract. One solution isto extend our system to process full text articles wherewe may find mention of more mutation and protein

information in the text. As a logical next step, we planto extend the system to process full text articles. How-ever, removing the irrelevant associations will be one ofthe critical challenges that need to be addressed whilewe adapt our text mining approach to extract associa-tions across sentences from full text articles. Also inthis study, we predominantly focused only on proteinpoint mutation disease associations. There are othersignificant variants at the gene and protein levels suchas deletions and additions, which also play significantroles in diseases.Our immediate next plan is to release both the web

and RESTAPI versions of the MutD system. With the in-creased use of the next generation sequencing in clinicalpractice, linking clinical variants to literature mentionswill help clinician interpret the impact of variants andguide them to take appropriate therapeutic intervention.We also plan to extend the system to extract drug-mutation-disease ternary relations from literature, whichcan be integrated with other knowledge sources such asClinVar [2] may empower physicians to embrace individ-ualized medicine.

ConclusionsOur study attempts to measure the capability of a textmining system in automatically extracting database levelannotations from biomedical texts. Our evaluation ofthe system against gold standard annotations extractedfrom curated database provides insight of the utility oftext mining for database curation. From text mining per-spective, this is the first attempt to effectively combineinformation from multiple sentences to extract ternaryrelations between protein, mutation and disease. Ourapproach to link dependency graphs across sentencesusing entity identity, anaphora and trigger words re-sulted in substantial performance improvement. Achiev-ing a performance of 64.3 % of overall F-measure, whichwhen further revised to 81.5 % after detailed error ana-lysis demonstrates that our approach to some extent ad-dressed the linguistic inference challenge faced by theuse of text mining for database curation.

Availability of supporting dataThe protein-mutation-disease ternary association andbinary association output for the MutD system for boththe development and test data in BRAT annotation styleis accessible at: http://ohnlp.org/index.php/MutD

AbbreviationsOMIM: Online Mendelian Inheritance in Man; EMU: Extractor of Mutations;UIMA: Unstructured Information Management Architecture; UMLS: Unifiedmedical language system; CTD: Comparative toxicogenomics database;MF: Mutationfinder; LVG: Lexical variant generator; NLM: National libraryof medicine; PMD: Protein mutation and disease; PD: Protein-disease;PM: Protein-mutation; MD: Mutation-disease.

Ravikumar et al. BMC Bioinformatics (2015) 16:185 Page 14 of 15

Competing interestsThe authors declare that they have no competing interests.

Authors' contributionsKER, JP, and HL conceived and designed the study. KER and DLimplemented the system. KER, KWB, and HL designed the experiments,analyzed the results, and drafted the manuscript. All authors have read andapproved the final manuscript.

AcknowledgementsThe authors acknowledge that the study was supported by two grants:National Science Foundation ABI:0845523 and National Library of MedicineR01LM009959 grants. KWB acknowledges NLM grant 1K99LM011575. We alsothank the intramural support from Mayo Center of Individualized Medicine(CIM). The authors acknowledge Ms. Mona Branstad, Mayo clinic for proofreading this manuscript. The authors sincerely thank James Masanz for helpwith the ohnlp website.

Received: 8 July 2014 Accepted: 30 April 2015

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