- Open Access
TXTGate: profiling gene groups with text-based information
© Glenisson et al.; licensee BioMed Central Ltd. 2004
- Received: 24 November 2003
- Accepted: 27 April 2004
- Published: 28 May 2004
We implemented a framework called TXTGate that combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. By means of tailored vocabularies, term- as well as gene-centric views are offered on selected textual fields and MEDLINE abstracts used in LocusLink and the Saccharomyces Genome Database. Subclustering and links to external resources allow for in-depth analysis of the resulting term profiles.
- Gene Ontology
- Additional Data File
- Gene Symbol
- Unify Medical Language System
Recent advances in high-throughput methods such as microarrays enable systematic testing of the functions of multiple genes, their interrelatedness and the controlled circumstances in which ensuing observations hold. As a result, scientific discoveries and hypotheses are stacking up, all primarily reported in the form of free text. However, as large amounts of raw textual data are hard to extract information from, various specialized databases have been implemented to provide a complementary resource for designing, performing or analyzing large-scale experiments.
Until now, the fact that there is little difference between retrieving an abstract from MEDLINE and downloading an entry from a biological database has been largely overlooked . The fading of the boundaries between text from a scientific article and a curated annotation of a gene entry in a database is readily illustrated by the GeneRIF feature in LocusLink , where snippets of a relevant article pertaining to a gene's function are manually extracted and directly pasted as an attribute in the database. The broadening of biologists' scope of investigation, along with the growing amount of information, result in an increasing need to move from single gene or keyword-based queries to more refined schemes that allow comprehensive views of text-oriented databases.
As gene-expression studies typically output a list of dozens or hundreds of genes that are co-expressed, a researcher is faced with the assignment of biological meaning to such lists. Several text-mining approaches have been developed to this end. Masys et al.  link groups of genes with relevant MEDLINE abstracts through the PubMed engine. Each cluster is characterized by a pool of keywords derived from both the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS) ontology. Jenssen et al.  set up a pioneering online system to link co-expression information from a microarray experiment with the cocitation network they constructed. This literature network covers co-occurrence information of gene identifiers in more than 10 million MEDLINE abstracts. Their system characterizes co-expressed genes using the MeSH keywords attached to the abstracts about those genes. Shatkay et al.  link abstracts to genes in a probabilistic scheme that uses the EM algorithm to estimate the parameters of the word distributions underlying a 'theme'. Genes are identified as similar when their corresponding gene-by-documents representations are close. Chaussabel and Sher  and Glenisson et al.  provide a proof of principle on how clustering of genes encoded in a keyword-based representation can further discern relevant subpatterns. Finally, Raychaudhuri et al.  developed a method called neighborhood divergence, to quantify the functional coherence of a group of genes using a database that links genes to documents. The score is successfully applied to both gold-standard and expression data, but has the slight drawback that it does not give information on the actual function. Their method is indeed geared to the identification of biologically coherent groups, rather than their interpretation.
Our system is built taking into account three main considerations, in an attempt to improve the quality and interpretability of term profiles. First, the construction of a sound linkage between genes and MEDLINE abstracts is often problem-dependent and constitutes a research track on its own that requires advanced document-classification strategies as, for example, proposed by Leonard et al.  or Raychaudhuri et al. . Despite some shortcomings, therefore, curated gene-literature references are helpful resources to exploit. Second, the information contained within curated gene references is sometimes diverse and can range from sequence to disease. In addition, the research questions that scientists are addressing when they scrutinize gene groups from high-throughput assays are similarly diverse. Therefore, considering all the terms occurring in a large set of documents (that is, a general vocabulary) might be detrimental to the extraction of terms that are relevant to the question at hand. The construction of separate vocabularies according to gene name, disease and function seems a logical choice to provide increased insight. Third, as mentioned previously, annotations offered by curated gene databases are often in semi-structured form and encompass keywords, sentences or paragraphs. To facilitate integration of such annotations with existing knowledge, controlled vocabularies that describe conceptual properties are of great value when constructing interoperable and computer-parsable systems. A number of structured vocabularies have already arisen (Gene Ontology (GO) , MeSH , eVOC ) and, slowly but surely, certain standards are systematically being adopted to store and represent biological information .
Armed with these insights, we developed TXTGate , a platform that offers multiple 'views' on vast amounts of gene-based free-text information available in selected curated database entries and scientific publications. TXTGate enables detailed functional analysis of interesting gene groups by displaying key terms extracted from the associated literature and by offering options to link out to other resources or to subcluster the genes on the basis of text. This way, we address on the one hand the need for easy means to validate gene clusters arising from, for instance, microarray experiments, and on the other hand the problem of using scientific literature in the form of free text as a source of functional information about genes. The strength of TXTGate is its use of tailored vocabularies to visualize only the information most relevant to the query at hand. TXTGate is implemented as a web application and is available for academic use .
This work extends the general ideas of textual profiling and clustering presented in Blaschke et al.  and Chaussabel and Sher , where the utility of literature indices for profiling gene groups in yeast and humans is proven. TXTGate implements the vector-space model for gene profiling  and provides indices for MEDLINE abstracts and selected functional annotations from two public databases. Various engineered domain-specific vocabularies (term- as well as gene-centric) act as perspectives to the literature and the tool provides direct links to external resources. In what follows, we compare TXTGate to other reported biological text-mining software.
MedMiner [17, 18] retrieves relevant abstracts by formulating expanded queries to PubMed. It uses entries from the GeneCards database  to fish for additional relevant keywords to expand a query. The resulting filtered abstracts are summarized in keywords and sentences, and feedback loops are provided. Nevertheless, the system is directed at querying terms and specific gene-drug or gene-gene relationships, rather than at scrutinizing gene clusters. MedMOLE [20, 21] is also a system to query MEDLINE more intelligently and detects Human Genome Organization (HUGO) names in abstracts via a natural language processing (NLP)-based gene-name extractor. The retrieved abstracts can be clustered, and top keywords are presented. However, the application scales less well, is not effective at profiling groups of genes, and the summaries provide much less detail than MedMiner and TXTGate. GEISHA [16, 22] is a tool for profiling gene clusters with an emphasis on summarization within a shallow parsing framework. This system was implemented for Escherichia coli but is no longer updated. PubGene [4, 23] is a database containing gene co-occurrence and cocitation networks of human genes derived from the full MEDLINE database. For a given set of genes it reports the literature network they reside in, together with their high-scoring MeSH terms. As not all relevant information can be captured by gene symbols or MeSH terms, the functionalities offered by TXTGate provide complementary views to interpret groups of genes. Although our colinkage feature (being a weaker form of co-occurrence that spans only the set of 73,152 MEDLINE abstracts used in LocusLink) is less elaborate than the possibilities offered by PubGene, we will show its utility and added value through its integration in the broader TXTGate framework. MedGene [24, 25] and G2D [26, 27] are specialized databases that, in contrast to TXTGate, are geared at ranking genes by disease. They accept user-defined queries scrutinizing gene-disease, disease-disease or gene-gene relationships extracted from the literature. Finally, MeKE [28, 29] is an application listing gene functions extracted by an ontology-based NLP system. Its current setup is directed more towards a functional knowledge base, rather than comprehensibly profiling information coming from groups of genes, as offered by our software.
Combining multiple, linked documents into a single gene profile
When a given gene has several curated MEDLINE references associated to it, we combine these abstracts into an indexed gene entry by taking the mean profile. This operation is part of the offline process.
Combining multiple gene profiles into a group profile
To summarize a cluster of genes and explore the most interesting terms they share, we compute the mean and variance of the terms over the group. Although simple, these statistics already reveal information on interesting terms characterizing the gene group. This is performed online.
Subclustering gene profiles
We offer the possibility online of subclustering a group of a maximum of 200 genes by means of hierarchical clustering. Ward's method was chosen because of its deterministic nature and the computational advantage of using the same solution when consecutively considering different numbers of clusters k. By varying the threshold at which to cut the tree, we can obtain an arbitrary number of clusters.
Text profiling, clustering and the supporting web interface are implemented as a Java web application that communicates with a mySQL database via Java Remote Method Invocation . The literature indices are generated using custom-developed indexing software written in C++. Code is available on request.
The indices are built using the vector-space model , where a textual entity is represented by a vector (or text profile) of which each component corresponds to a single (multi-word) term from the entire set of terms (the vocabulary) being used. For each component a value denotes the importance of a given term, represented by a weight. Indexing a document is performed by the calculation of these weights:
Each w i,j in the vector of document i is a weight for term j from the vocabulary of size N. This representation is often referred to as 'bag-of-words'. All textual information is stemmed using the Porter stemmer  (stemming is the automated conflation of related words, usually by reducing the words to a common root form) and indexed with a normalized inverse document frequency (IDF) weighting scheme, a reasonable choice for modeling pieces of text comprising up to 200 terms, as observed in database annotations and MEDLINE abstracts. With D the number of documents in the collection and D t the number of documents containing term t, IDF is defined as
Overview of the indexed resources of textual information in TXTGate
Domain vocabularies used
Linked MEDLINE abstracts
GO, MeSH, eVOC, OMIM, HUGO gene symbols
Linked MEDLINE abstracts
GO-pruned, SGD gene symbols
Construction of domain vocabularies
We constructed five different term-centric domain vocabularies that provide different views on the gene-specific information we indexed. All vocabulary sources underwent parsing and pruning operations to obtain stemmed words and phrases, eliminating stop words (such as 'then', 'as', 'of', 'gene') from a handcrafted list. We again applied the Porter stemmer ) to avoid information loss due to morphological and inflexional endings. Although stemming is not always desirable, for relatively small documents it has proved advantageous. Where applicable we derived phrases directly from the vocabulary source.
A first vocabulary was derived from the GO  and comprises 17,965 terms. GO is a dynamic controlled hierarchy of multi-word terms with a wide coverage of life-science literature, and genetics in particular. We considered it an ideal source from which to extract a highly relevant and relatively noise-free domain vocabulary. We retained all composite GO terms shorter than five tokens as phrases. Longer terms containing brackets or commas were split to increase their detection. For the yeast indices, we pruned the vocabulary, retaining only those terms occurring at least twice and in less than 20% of all MEDLINE abstracts referred to in SGD , obtaining a new vocabulary of 3,867 terms.
Two other domain vocabularies are rather similar in scope but differ in size. One is based on the MeSH , the National Library of Medicine's controlled vocabulary thesaurus, and counts 27,930 terms. The other is based on OMIM's Morbid Map . This is a cytogenetic map location of all disease genes present in OMIM and their associated diseases. We extracted all disease terms to construct a 2,969-term vocabulary. A fifth domain vocabulary was drawn from eVOC , a thesaurus consisting of four orthogonal controlled vocabularies encompassing the domain of human gene-expression data. It includes terms related to anatomical system, cell type, pathology, and developmental stage.
In addition to these term-centric domain vocabularies we constructed two gene-centric vocabularies with the screening of co-occurring and colinked genes in mind. 'Co-occurrence' denotes the simultaneous presence of gene names within a single abstract, as described by Jenssen et al. . We define 'colinkage' here as a weaker form of co-occurrence screening for the simultaneous presence of gene names in the pool of abstracts that is linked to a given group of genes.
Overview of the domain vocabularies in TXTGate
Number of terms
HUGO gene symbols (human)
SGD gene symbols (yeast)
The online clustering is done with our own implementation in Java of Ward's method for hierarchical clustering . Ward's method outperforms single, average or complete linkage. The similarity measure used is the cosine distance between two vector representations and . The similarity between a newly formed cluster (r, s) (by linking two existing vectors/clusters) with (n r + n s ) elements and an existing cluster (t) with n t elements is given by
d[(t), (r, s)] = α r d[(t), (r)] + α s d[(t), (s)] + β d[(r), (s)]
Given the preferred number of clusters k, the linkage tree is cut at the appropriate level to yield k clusters.
As a measure of textual coherence, C G , we calculate the median distance in term space from the profile of the group G of size n G to the individual profiles, g i , of all genes in that group:
We assess its significance by computing a background distribution from random gene clusters of different sizes.
Significance of coherence score C G
TXTGate allows online subclustering and profiling of gene groups via terms extracted from MEDLINE. Below we describe two examples.
TXTGate profiling of cluster E from Eisen et al. 
Cluster terms in Blaschke et al. 
Terms from TXTGate
TXTGate profiling of clusters a, b, c, and d from Chaussabel and Sher  (GO vocabulary)
Cluster terms in 
Terms from TXTGate
Comparison of the terms in cluster e found by Chaussabel and Sher  with those found by TXTGate (OMIM vocabulary)
Cluster terms in Chaussabel and Sher .
Terms from TXTGate
Another major feature of TXTGate is its ability to present textual information (most importantly MEDLINE abstracts) from different perspectives. This is implemented by offering indices built on GO-, OMIM-, MeSH-, eVOC-, and gene nomenclature-based domain vocabularies respectively. Each configuration is meant to expose a different view of the literature. TXTGate mirrors the dual approach adopted by the external databases it links to, which separate keyword and gene-symbol queries. This, in part, motivated our strategy to construct both term- and gene-centric vocabularies.
Various perspectives on textual information in TXTGate
Co-linkage analysis of genes with gene-centric vocabularies
Hereditary nonpolyposis colon cancer
Adenomatous polyposis coli protein
Cellular tumor antigen P53 (tumor suppressor P53)
DNA mismatch repair protein MLH1 (mutL protein homolog 1)
E. coli mismatch repair gene mutS
Cyclin-dependent kinase inhibitor 1A
DNA mismatch repair protein MSH2 (mutS protein homolog 2)
BAX protein, cytoplasmic isoform delta
Wingless-type MMTV integration site family members
DNA mismatch repair protein PMS2
Proto-oncogene tyrosine protein kinase SRC
Tumor suppressor protein DCC precursor (colorectal cancer suppressor)
Colorectal mutant cancer protein MCC
Proto-oncogene serine/threonine protein kinase B-RAF
Fibroblast growth factor receptor 3 precursor
Chloride anion exchanger DRA
AXIS inhibition protein 2
DNA mismatch repair protein PMS1
Abelson murine leukemia viral oncogene homolog 1
Mitotic checkpoint serine/threonine protein kinase BUB1
Protein tyrosine phosphatase family
B cell lymphoma/leukemia 10
Protein tyrosine phosphatase family with C-terminal PEST-motif
PDGF-receptor beta-like tumor suppressor
Textual profile of a gene group from a mouse model for human benign tumors of the salivary glands
Terms sorted by mean
Terms sorted by variance
As a measure of textual coherence C G , we calculated the median distance in vocabulary space from the profile of the group G to the individual profiles g i of all genes in that group:
We have described a framework for advanced textual profiling of groups of genes. TXTGate is implemented as a web application designed to efficiently process queries of up to 200 genes, although this is not a strict limit. We believe that the application scales well enough to be of use in, for example, microarray cluster validation.
Supported by the work of Stephens et al.  and more recently that of Chiang and Yu , we aimed to complement the limitations of a single, more general, text index by offering different views. Nevertheless, some vocabularies could still be optimized to improve the information content of the profiles. For example, some general or non-informative terms are still scoring high because of our stemming and phrase-detection methods (for example, 'ii', 'protein', 'alpha').
Finally, although the citations in LocusLink and SGD constitute good sources for retrieving relevant gene-related MEDLINE abstracts, weighting the information according to the context and eliminating poorly informative or contaminating annotations (such as sequence-related articles) still need to be taken into account in future incarnations of the software. Document-classification strategies as in Leonard et al.  or Raychaudhuri et al.  can be adopted to this end.
As with GO annotations, transfer of literature references according to homology can be used to characterize poorly annotated genes [44, 45]. At this stage, the application allows for the study of homologs within all organisms contained in LocusLink, provided the user inputs the corresponding LocusLink identifiers. This type of operation will be increasingly supported with future additions of literature indices from other organisms and databases.
In conclusion, TXTGate's approach to summarizing database annotations and literature via specific vocabularies, along with its options to perform further analysis via clustering or query building, make it a flexible gateway to explore text-based information comprehensively.
The following additional data are available with the online version of this article: the MEDLINE-based text profiles of yeast expression clusters from Eisen et al.  (Additional data file 1); the MEDLINE-based profiles for the data in Chaussabel and Sher  (Additional data file 2); details on the colon and colorectal cancer test case (Additional data file 3); the expert summary and textual profiles of the 350 upregulated mouse genes for different domain vocabularies (Additional data file 4).
This research was supported by grants from the Research Council K.U. Leuven (GOA-Mefisto-666, GOA-Ambiorics, IDO), the Fonds voor Wetenschappelijk Onderzoek - Vlaanderen (G.0115.01, G.0240.99, G.0407.02, G.0413.03, G.0388.03, G.0229.03, G.0241.04), the Instituut voor de aanmoediging van Innovatie door Wetenschap en Technologie Vlaanderen (STWW-Genprom, GBOU-McKnow, GBOU-SQUAD, GBOU-ANA), the Belgian Federal Science Policy Office (IUAP V-22), and the European Union (FP5 CAGE, ERNSI, FP6 NoE Biopattern, NoE E-tumours). We acknowledge Peter Antal for starting up this research direction.
- Gerstein M, Junker J: Blurring the boundaries between scientific papers and biological databases. Nature Online. [http://0-www.nature.com.brum.beds.ac.uk/nature/debates/e-access/articles/gernstein.html]
- Pruitt K, Maglott D: RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res. 2001, 29: 137-140. 10.1093/nar/29.1.137.PubMedPubMed CentralView ArticleGoogle Scholar
- Masys DR, Welsh JB, Fink JL, Gribskov M, Klacansky I, Corbeil J: Use of keyword hierarchies to interpret gene expression. Bioinformatics. 2001, 17: 319-326. 10.1093/bioinformatics/17.4.319.PubMedView ArticleGoogle Scholar
- Jenssen T, Laegreid A, Komorowski J, Hovig E: A literature network of human genes for high-throughput analysis of gene expression. Nat Genet. 2001, 28: 21-28. 10.1038/88213.PubMedGoogle Scholar
- Shatkay H, Edwards S, Boguski M: Information retrieval meets gene analysis. IEEE Intell Syst (Special Issue on Intelligent Systems in Biology). 2002, 17: 45-53. 10.1109/5254.999219.Google Scholar
- Chaussabel D, Sher A: Mining microarray expression data by literature profiling. Genome Biol. 2002, 3: research0055.1-0055.16. 10.1186/gb-2002-3-10-research0055.View ArticleGoogle Scholar
- Glenisson P, Antal P, Mathys J, Moreau Y, Moor BD: Evaluation of the vector space representation in text-based gene clustering. Pac Symp Biocomput. 2003, 391-402.Google Scholar
- Raychaudhuri S, Schutze H, Altman RB: Using text analysis to identify functionally coherent gene groups. Genome Res. 2002, 12: 1582-1590. 10.1101/gr.116402.PubMedPubMed CentralView ArticleGoogle Scholar
- Leonard JE, Colombe JB, Levy JL: Finding relevant references to genes and proteins in Medline using a Bayesian approach. Bioinformatics. 2002, 18: 1515-1522. 10.1093/bioinformatics/18.11.1515.PubMedView ArticleGoogle Scholar
- Raychaudhuri S, Chang JT, Sutphin PD, Altman RB: Associating genes with Gene Ontology codes using a maximum entropy analysis of biomedical literature. Genome Res. 2002, 12: 203-214. 10.1101/gr.199701.PubMedPubMed CentralView ArticleGoogle Scholar
- Gene Ontology Consortium. [http://www.geneontology.org]
- Medical Subject Headings. [http://www.nlm.nih.gov/mesh/meshhome.html]
- Kelso J, Visagie J, Theiler G, Christoels A, Bardien S, Smedley D, Otgaar D, Greyling G, Jongeneel C, McCarthy M, et al: eVOC: a controlled vocabulary for unifying gene expression data. Genome Res. 2003, 13: 1222-1230. 10.1101/gr.985203.PubMedPubMed CentralView ArticleGoogle Scholar
- Gene Ontology Annotation. [http://www.ebi.ac.uk/GOA]
- TXTGate Portal. [http://www.esat.kuleuven.ac.be/txtgate]
- Blaschke C, Oliveros J, Valencia A: Mining functional information associated with expression arrays. Funct Integr Genomics. 2001, 1: 256-268. 10.1007/s101420000036.PubMedView ArticleGoogle Scholar
- Tanabe L, Scherf U, Smith L, Lee J, Hunter L, Weinstein J: MedMiner: an internet text-mining tool for biomedical information, with application to gene expression profiling. BioTechniques. 1999, 27: 1210-1217.PubMedGoogle Scholar
- MedMiner. [http://discover.nci.nih.gov/textmining]
- Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D: GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics. 1998, 14: 656-664. 10.1093/bioinformatics/14.8.656.PubMedView ArticleGoogle Scholar
- Calogero R, Iazzetti G, Motta S, Pedrazzi G, Rago S, Rossi E, Turra R: MedMOLE: mining literature to extract biological knowledge by microarray data. In Proc Virtual Conf Genomics Bioinformatics. 2002, 2: 9-14.Google Scholar
- MedMOLE at CINECA. [http://www.cineca.it/HPSystems/Chimica/medmole]
- DNA Array Analysis with GEISHA. [http://www.pdg.cnb.uam.es/blaschke/cgi-bin/geisha]
- PubGene Gene Database and Tools. [http://www.pubgene.org]
- Hu Y, Hines L, Weng H, Zuo D, Rivera M, Richardson A, LaBaer J: Analysis of genomic and proteomic data using advanced literature mining. J Proteome Res. 2003, 2: 405-412. 10.1021/pr0340227.PubMedView ArticleGoogle Scholar
- MedGene Database. [http://hipseq.med.harvard.edu/MEDGENE]
- Perez-Iratxeta C, Bork P, Andrade M: Association of genes to genetically inherited diseases using data mining. Nat Genet. 2002, 31: 316-319.PubMedGoogle Scholar
- G2D Candidate Genes to Inherited Diseases. [http://www.bork.embl-heidelberg.de/g2d]
- Chiang J, Yu H: MeKE: discovering the functions of gene products from biomedical literature via sentence alignment. Bioinformatics. 2003, 19: 1417-1422. 10.1093/bioinformatics/btg160.PubMedView ArticleGoogle Scholar
- MeKE (Medical Knowledge Explorer). [http://ismp.csie.ncku.edu.tw/~yuhc/meke]
- Java Remote Method Invocation (Java RMI). [http://java.sun.com/products/jdk/rmi]
- Baeza-Yates R, Ribeiro-Neto B: Modern Information Retrieval. 1999, Reading, MA: Addison-Wesley/ACM PressGoogle Scholar
- Porter MF: An algorithm for suffix stripping. Program. 1980, 14: 130-137.View ArticleGoogle Scholar
- Saccharomyces Genome Database. [http://www.yeastgenome.org]
- OMIM - Online Mendelian Inheritance in Man. [http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/entrez/query.fcgi?db=OMIM]
- HUGO Gene Nomenclature Commitee (HGNC). [http://www.gene.ucl.ac.uk/nomenclature]
- Jain A, Dubes R: Algorithms for Clustering Data. 1988, Upper Saddle River, NJ: Prentice HallGoogle Scholar
- Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 1998, 9: 3273-3297.PubMedPubMed CentralView ArticleGoogle Scholar
- Glenisson P, Mathys J, Moreau Y, De Moor B: Scoring and summarizing gene groups from text using the vector space model. Technical Report 03-97, ESAT-SISTA. 2003, Leuven, Belgium: K.U.Leuven, [ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/glenisson/reports/genomebiol/TR03-97.pdf]Google Scholar
- Eisen M, Spellman P, Brown P, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA. 1998, 95: 14863-14868. 10.1073/pnas.95.25.14863.PubMedPubMed CentralView ArticleGoogle Scholar
- AmiGO Gene Ontology browser. [http://www.godatabase.org]
- Kas K, Voz ML, Roijer E, Astrom AK, Meyen E, Stenman G, Van de Ven WJ: Promoter swapping between the genes for a novel zinc finger protein and beta-catenin in pleiomorphic adenomas with t(3;8)(p21;q12) translocations. Nat Genet. 1997, 15: 170-174.PubMedView ArticleGoogle Scholar
- Voz ML, Mathys J, Hensen K, Pendeville H, Van Valckenborgh I, Van Huffel C, Chavez M, Van Damme B, De Moor B, Moreau Y, Van de Ven WJ: Microarray screening for target genes of the proto-oncogene PLAG1. Oncogene. 2004, 23: 179-191. 10.1038/sj.onc.1207013.PubMedView ArticleGoogle Scholar
- Stephens M, Palakal M, Mukhopadhyay S, Raje R, Mostafa J: Detecting gene relations from Medline abstracts. Pac Symp Biocomput. 2001, 483-495.Google Scholar
- Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. Nat Genet. 2000, 25: 25-29. 10.1038/75556.PubMedPubMed CentralView ArticleGoogle Scholar
- Raychaudhuri S, Chang JT, Imam F, Altman RB: The computational analysis of scientific literature to define and recognize gene expression clusters. Nucleic Acids Res. 2003, 31: 4553-4560. 10.1093/nar/gkg636.PubMedPubMed CentralView ArticleGoogle Scholar
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