BACKGROUND
the amount of information in the life sciences is staggering and growing exponentially.
one of the largest biomedical resources of textual scientific information, the medline database, currently contains over  <dig> million abstracts, with an estimated increase in size of more than one article per minute.
scientists are faced with an overload of information, which is particularly pressing in the biological field where high-throughput experiments in genomics and proteomics generate new data at an unprecedented rate.
more often than not, interpretation of these data requires the digestion and integration of information contained in many thousands of articles and other information sources, a daunting task clearly beyond the capacity of human reading and comprehension.
recently, a number of information retrieval systems have been proposed to extract and relate pertinent biological information from large corpora of text  <cit> .
these systems even hold promise for the discovery of new, "tacit" knowledge that is hidden in the literature.
the term "conceptual biology" has already been coined to distinguish this emerging field of research as a branch of biological research in its own right  <cit> .
there are however several issues that limit the practical utility of current text-mining tools  <cit> .
one problem is the highly-variable use of gene nomenclature in the literature  <cit> , producing multiple symbols and names for one and the same gene.
this complicates relating information in different documents that deal with the same gene but use different symbols.
one approach to deal with this synonym problem is to make use of the information about genes and their aliases that is available in existing genetic databases.
a second, probably more intricate, problem is that a single gene symbol may refer to multiple genes, or may also be the abbreviation of terms with completely different, non-gene meanings.
when building gene networks from the literature  <cit> , for example, one would not want to contaminate the network on prostate specific antigen  with puromycin-sensitive aminopeptidase, psoriatric arthritis, pig serum albumin, or one of the more than  <dig> other meanings of psa that can be found in the literature  <cit> .
the extent of this ambiguity or homonym problem has been further subject of two recent studies.
tuason et al.
<cit>  compared gene symbols of four organisms  and showed that up to 20% of the gene symbols of an individual organism were ambiguous with the other three organisms.
in another study by the same group, chen et al.
<cit>  found that 85% of correctly retrieved mouse genes in a set of  <dig>  abstracts were ambiguous with gene names from  <dig> other organisms, while ignoring gene names that were also english words.
when the latter were included, 233% additional "gene" instances were retrieved, most of which were false positives.
in several other studies  <cit> , it was also suggested that solving this ambiguity problem is an important requirement for large-scale application of text-mining tools in the biomedical field.
general word-sense disambiguation has been studied extensively in the field of natural language processing.
a wide variety of approaches has been proposed , including dictionary-based approaches and the use of supervised learning techniques to build classifiers that assign the proper sense to an ambiguous term.
typically, these methods use the words in a window around the ambiguous term, or information derived from this context window, such as part-of-speech or collocation.
recently, several studies have explored the use of disambiguation techniques in the biological field.
hatzivassiloglou et al.
<cit>  applied machine learning methods to classify symbols into one of three categories: genes, proteins, and mrna.
no attempt was made to resolve homonyms with two or more senses within one group, or with a sense outside of these three groups, and performance results were rather moderate, although still better than human interpretation.
the same problem was recently tackled by ginter et al.
<cit> , who proposed a new classifier design and were able to slightly improve on the best method used by hatzivassiloglou  <cit> .
in a series of articles  <cit> , liu and co-workers investigated the effect of different supervised learning techniques, feature representations, and context window sizes on disambiguation performance.
they obtained excellent results on a small number of ambiguous biomedical abbreviations  <cit> , but for training they typically needed dozens of examples for each of the possible senses.
in practice, these numbers may often be difficult to obtain.
widdows et al.
<cit>  compared several methods for disambiguating ambiguous concepts from the medical subject headings  thesaurus  <cit>  on a set of  <dig> ambiguous terms.
their most successful method achieved 74% precision and utilized existing mesh-term co-occurrence data, which were derived from the mesh annotations by human annotators.
however, their method would not work well for gene symbols, which are poorly covered by mesh.
recently, podowski et al.
<cit>  used bayesian classifier models to disambiguate gene symbols found in locuslink  <cit> .
interestingly, their system can distinguish between gene and non-gene meanings of a symbol, acknowledging the fact that many gene symbols are abbreviations of terms with non-gene meanings.
they validated their system on two manually curated test sets of  <dig> gene symbols, and found that the accuracy of the system is mostly over 90% when more than  <dig> abstracts per gene sense were available for training.
although several of these approaches produced very good disambiguation results, they require substantial amounts of training data, typically tens of instances per sense.
for gene symbol disambiguation, these numbers may be difficult to acquire.
given the extent of the homonym problem for gene symbols, manual curation of training data would be extremely laborious.
any practical disambiguation system should be trained with data that are gathered automatically, but even then the required numbers are unlikely to be available for many of the ambiguous symbols.
here we present a disambiguation method for gene symbols, which maintains excellent performance when trained with sparse data.
at the basis of our approach lies a thesaurus that is used to find biomedical concepts, including gene symbols, in text.
focusing on human genes, we first quantify the ambiguity problem for gene symbols, particularly paying attention to ambiguity arising from non-gene meanings of gene symbols.
we then describe our disambiguation approach and assess the performance of the disambiguation algorithm on a large test set of documents.
RESULTS
thesaurus construction and ambiguity of human gene symbols
we extracted human gene symbols and aliases, gene names, and identification numbers from five publicly available databases  <cit> : genew, the genome database , locuslink, online mendelian inheritance in man , and swiss-prot.
genes from the different databases were matched based on identification numbers and overlap in gene symbols or names, and the corresponding information was merged into a new gene thesaurus.
the resulting thesaurus contained information on  <dig>  human genes, with a total of  <dig>  gene symbols.
the percentage of ambiguous gene symbols was  <dig> % , whereas the percentage of genes affected by homonymy was  <dig> % .
gene symbols may not only denote multiple genes, but may also have other, non-gene meanings.
to further gauge the extent of ambiguity, we searched  <dig> years of medline abstracts for abbreviations  and their expansions  using an abbreviation expansion algorithm  <cit> .
a total of  <dig>  unique, case-sensitive short forms were found that matched a gene symbol from our gene thesaurus, with  <dig>  long forms.
of these,  <dig>  long forms  did not match, partially or completely, any of the gene names associated with that symbol, and were assumed to have a non-gene meaning.
the number of different long forms per symbol with a non-gene meaning varies widely , up to  <dig> .
the short forms with at least one non-gene meaning affected  <dig> % of the genes and  <dig> % of the gene symbols in our combined gene thesaurus.
overall, taking into account both gene and non-gene meanings,  <dig> % of the genes in our combined thesaurus had one or more homonymous symbols, and  <dig> % of the gene symbols in the thesaurus were ambiguous.
disambiguation of gene symbol senses
the algorithm to disambiguate homonymous gene symbols operates as follows.
for each of the possible genes that the symbol can denote, a reference description is assumed to be available.
given an ambiguous symbol, the textual context in which it occurs, say, a medline abstract, is matched with each reference description, yielding a set of matching scores.
the gene corresponding with the reference description that best matches the context is then taken to indicate the symbol's meaning.
however, the symbol is assumed to have a non-gene meaning if the context does not match well with any of the reference descriptions and the matching score stays below a homonym-dependent threshold, as determined by a leave-one-out procedure .
for the textual context, we used the title, abstract, and mesh terms that had been assigned to the medline abstract.
as reference descriptions, we selected either gene annotations that were culled from omim, or one or more  medline abstracts about a particular gene.
for training and testing purposes, we automatically compiled a set of annotations and abstracts for  <dig> ambiguous symbols, having  <dig> different gene meanings;  <dig> of the symbols had at least one non-gene meaning.
all abstracts and annotations were sought for concepts from mesh and the gene thesaurus with indexing software from collexis   <cit> .
for each document this yielded a list of biomedical concepts with attached relevance scores , which was used for subsequent processing.
for each gene sense of a symbol, five randomly chosen abstracts were set aside for generating different reference cfps; the remaining abstracts were used for testing.
the test set contained  <dig>  abstracts.
the matching score between textual context and reference description was defined as the normalized cosine-vector score  <cit>  between the cfps of these two texts.
overall accuracy of the disambiguation algorithm, defined as the percentage of abstracts in our test set in which the correct meaning of the homonym was chosen, was  <dig> % when omim annotations were used as reference description.
this was comparable to using one abstract as the reference , while accuracy increased to  <dig> % when a cfp combination of five abstracts was used as the reference .
for comparison, a simple majority rule  resulted in a baseline accuracy of  <dig> %.
we made a breakdown of the errors when a combination of five abstracts was used as the reference description.
as shown in table  <dig>  symbols indicating a gene were assigned a non-gene meaning in  <dig> % of the cases; symbols with a non-gene meaning were misclassified as a gene even less frequently .
for gene symbols with multiple gene meanings,  <dig> % of the symbols were assigned to the incorrect genes .
discussion
ambiguity of gene symbols in free text is an impediment for the massive application of text mining and literature-based discovery methods.
our assessment of gene symbol ambiguity indicates that the homonym problem cannot be ignored when text mining in the biological field is performed, corroborating findings of previous studies  <cit> .
we found up to 33% of the genes in our thesaurus being affected by homonymy, and even this high figure is underestimating the problem because we limited ourselves to human genes only, not considering other organisms and gene products.
disambiguation would be a trivial task if each ambiguous symbol in an abstract were accompanied by its corresponding long form at least once.
unfortunately, this approach is of limited practical value.
we recently checked  <dig>  nature genetics and biomed central articles and found that only 30% of the gene symbols in the abstracts are accompanied by a matching long form  <cit> .
for an additional 8% of the symbols in the abstracts, the long form could be found in the full text.
of all gene symbols mentioned in the full-text articles, only 18% were accompanied by a long form.
we compared two sources of reference descriptions, gene annotations and abstracts about a particular gene.
omim annotations did not perform better as a reference description than randomly chosen abstracts about a gene.
the performance increased when the reference cfp was constructed from the information of several abstracts combined, but the improvement appeared marginal when more than three abstracts were used .
this suggests that excellent disambiguation results can be obtained with relatively simple reference descriptions, and offers a viable way for massive acquisition of such descriptions from literature links in genetic databases, which in view of the extent of the homonym problem should be automatic for all practical purposes.
our disambiguation algorithm could be used as part of a gene identification module in an information extraction application.
in a recent review article on term identification, krauthammer and nenadic  <cit>  distinguish between two types of disambiguation: at the broader level of pinpointing the type of a concept  and at the specific level of resolving different meanings of a term within a term class .
our approach addresses both types of disambiguation.
while the algorithm was developed and tested for disambiguation of gene symbols, the approach is general and easy to apply to other ambiguous entities as well, provided adequate reference descriptions are available.
we would like to emphasize the practicality of our approach in terms of processing speed, scalability, and accuracy.
the initial indexing process is the most time-consuming step, but in principle has to be done only once.
for our whole test set of  <dig>  abstracts, indexing currently takes about two hours on a standard pentium iv computer.
once the context and reference fingerprints are available, the disambiguation process itself is very fast, taking about two minutes for the whole test set.
in practical applications, reference descriptions will be needed for many more than the almost  <dig> homonymous gene symbols that we used in this study.
considering that even a single abstract about a particular gene can provide an adequate reference description, our approach can easily be scaled up, for instance by taking the abstracts that are referenced with each of the gene descriptions in locuslink.
in our download of locuslink,  <dig>  genes had at least one reference, and  <dig>  had three or more.
automatic determination of a gene/non-gene threshold may be more difficult, as our approach presently requires the availability of examples of non-gene meanings of a symbol.
we are currently investigating automatic threshold setting based on a general set of abstracts with non-gene meanings, obviating the need to acquire non-gene examples of each specific symbol.
the accuracy of any disambiguation algorithm must be very high in order to be of practical value in massive literature mining.
in this respect, gene symbols that are assigned a non-gene meaning  may be less of a problem than the other way around , or than being assigned the wrong gene meaning .
it should be remarked that these results pertain to our test set, which is large but still limited in scope because we only selected gene symbols that occur in omim and had six or more abstracts per gene sense.
this may have favored selection of relatively well-known genes.
it is conceivable that some abstracts with gene symbols that were not selected provide a less focussed context that would perform less well.
in our data selection, we focused on human genes.
the abstracts in our test set with ambiguous symbols that indicated a gene, were taken from two sources: omim, which is a database about human genes and diseases, or the sf/lf data set, if both short form and long form in the abstract exactly matched an entry in our human gene thesaurus.
however, previous investigations  <cit>  showed that substantial ambiguity of gene symbols exists across species, and suggested that most of this ambiguity was attributable to homologous genes.
in a random sample of  <dig> abstracts from our test set with symbols that had a gene meaning,  <dig> symbols referred to non-human genes, mostly from mouse or rat .
all of these were apparently homologous to the human genes with identical names.
in this study, we did not attempt to distinguish between homologous genes.
model organisms are often used to understand the biology of human genes and the distinction between homologous genes in text is often difficult to make, or not useful.
if disambiguation of homologous genes is important, though, our approach could be extended by including reference descriptions of genes from other species.
finally, the current performance is based on reference descriptions that were acquired fully automatically.
manual curation of these descriptions or their corresponding fingerprints for low-scoring symbols may further add to the algorithm's performance.
CONCLUSIONS
the ambiguity of gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings.
a simple, thesaurus-based disambiguation approach can resolve most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions.
the proposed method is fast and scalable, enabling gene-symbol disambiguation in massive text mining and information extraction applications.

 the amount of information in the life sciences is staggering and growing exponentially. one of the largest biomedical resources of textual scientific information, the medline database, currently contains over million abstracts, with an estimated increase in size of more than one article per minute. scientists are faced with an overload of information, which is particularly pressing in the biological field where high-throughput experiments in genomics and proteomics generate new data at an unprecedented rate. more often than not, interpretation of these data requires the digestion and integration of information contained in many thousands of articles and other information sources, a daunting task clearly beyond the capacity of human reading and comprehension. recently, a number of information retrieval systems have been proposed to extract and relate pertinent biological information from large corpora of text. these systems even hold promise for the discovery of new, "tacit" knowledge that is hidden in the literature. the term "conceptual biology" has already been coined to distinguish this emerging field of research as a branch of biological research in its own right. there are however several issues that limit the practical utility of current text-mining tools. one problem is the highly-variable use of gene nomenclature in the literature, producing multiple symbols and names for one and the same gene. this complicates relating information in different documents that deal with the same gene but use different symbols. one approach to deal with this synonym problem is to make use of the information about genes and their aliases that is available in existing genetic databases. a second, probably more intricate, problem is that a single gene symbol may refer to multiple genes, or may also be the abbreviation of terms with completely different, non-gene meanings. when building gene networks from the literature, for example, one would not want to contaminate the network on prostate specific antigen with puromycin-sensitive aminopeptidase, psoriatric arthritis, pig serum albumin, or one of the more than other meanings of psa that can be found in the literature. the extent of this ambiguity or homonym problem has been further subject of two recent studies. tuason et al. compared gene symbols of four organisms and showed that up to 20% of the gene symbols of an individual organism were ambiguous with the other three organisms. in another study by the same group, chen et al. found that 85% of correctly retrieved mouse genes in a set of abstracts were ambiguous with gene names from other organisms, while ignoring gene names that were also english words. when the latter were included, 233% additional "gene" instances were retrieved, most of which were false positives. in several other studies, it was also suggested that solving this ambiguity problem is an important requirement for large-scale application of text-mining tools in the biomedical field. general word-sense disambiguation has been studied extensively in the field of natural language processing. a wide variety of approaches has been proposed, including dictionary-based approaches and the use of supervised learning techniques to build classifiers that assign the proper sense to an ambiguous term. typically, these methods use the words in a window around the ambiguous term, or information derived from this context window, such as part-of-speech or collocation. recently, several studies have explored the use of disambiguation techniques in the biological field. hatzivassiloglou et al. applied machine learning methods to classify symbols into one of three categories: genes, proteins, and mrna. no attempt was made to resolve homonyms with two or more senses within one group, or with a sense outside of these three groups, and performance results were rather moderate, although still better than human interpretation. the same problem was recently tackled by ginter et al., who proposed a new classifier design and were able to slightly improve on the best method used by hatzivassiloglou. in a series of articles, liu and co-workers investigated the effect of different supervised learning techniques, feature representations, and context window sizes on disambiguation performance. they obtained excellent results on a small number of ambiguous biomedical abbreviations, but for training they typically needed dozens of examples for each of the possible senses. in practice, these numbers may often be difficult to obtain. widdows et al. compared several methods for disambiguating ambiguous concepts from the medical subject headings thesaurus on a set of ambiguous terms. their most successful method achieved 74% precision and utilized existing mesh-term co-occurrence data, which were derived from the mesh annotations by human annotators. however, their method would not work well for gene symbols, which are poorly covered by mesh. recently, podowski et al. used bayesian classifier models to disambiguate gene symbols found in locuslink. interestingly, their system can distinguish between gene and non-gene meanings of a symbol, acknowledging the fact that many gene symbols are abbreviations of terms with non-gene meanings. they validated their system on two manually curated test sets of gene symbols, and found that the accuracy of the system is mostly over 90% when more than abstracts per gene sense were available for training. although several of these approaches produced very good disambiguation results, they require substantial amounts of training data, typically tens of instances per sense. for gene symbol disambiguation, these numbers may be difficult to acquire. given the extent of the homonym problem for gene symbols, manual curation of training data would be extremely laborious. any practical disambiguation system should be trained with data that are gathered automatically, but even then the required numbers are unlikely to be available for many of the ambiguous symbols. here we present a disambiguation method for gene symbols, which maintains excellent performance when trained with sparse data. at the basis of our approach lies a thesaurus that is used to find biomedical concepts, including gene symbols, in text. focusing on human genes, we first quantify the ambiguity problem for gene symbols, particularly paying attention to ambiguity arising from non-gene meanings of gene symbols. we then describe our disambiguation approach and assess the performance of the disambiguation algorithm on a large test set of documents.  thesaurus construction and ambiguity of human gene symbols we extracted human gene symbols and aliases, gene names, and identification numbers from five publicly available databases: genew, the genome database, locuslink, online mendelian inheritance in man, and swiss-prot. genes from the different databases were matched based on identification numbers and overlap in gene symbols or names, and the corresponding information was merged into a new gene thesaurus. the resulting thesaurus contained information on human genes, with a total of gene symbols. the percentage of ambiguous gene symbols was %, whereas the percentage of genes affected by homonymy was %. gene symbols may not only denote multiple genes, but may also have other, non-gene meanings. to further gauge the extent of ambiguity, we searched years of medline abstracts for abbreviations and their expansions using an abbreviation expansion algorithm. a total of unique, case-sensitive short forms were found that matched a gene symbol from our gene thesaurus, with long forms. of these, long forms did not match, partially or completely, any of the gene names associated with that symbol, and were assumed to have a non-gene meaning. the number of different long forms per symbol with a non-gene meaning varies widely, up to. the short forms with at least one non-gene meaning affected % of the genes and % of the gene symbols in our combined gene thesaurus. overall, taking into account both gene and non-gene meanings, % of the genes in our combined thesaurus had one or more homonymous symbols, and % of the gene symbols in the thesaurus were ambiguous. disambiguation of gene symbol senses the algorithm to disambiguate homonymous gene symbols operates as follows. for each of the possible genes that the symbol can denote, a reference description is assumed to be available. given an ambiguous symbol, the textual context in which it occurs, say, a medline abstract, is matched with each reference description, yielding a set of matching scores. the gene corresponding with the reference description that best matches the context is then taken to indicate the symbol's meaning. however, the symbol is assumed to have a non-gene meaning if the context does not match well with any of the reference descriptions and the matching score stays below a homonym-dependent threshold, as determined by a leave-one-out procedure. for the textual context, we used the title, abstract, and mesh terms that had been assigned to the medline abstract. as reference descriptions, we selected either gene annotations that were culled from omim, or one or more medline abstracts about a particular gene. for training and testing purposes, we automatically compiled a set of annotations and abstracts for ambiguous symbols, having different gene meanings; of the symbols had at least one non-gene meaning. all abstracts and annotations were sought for concepts from mesh and the gene thesaurus with indexing software from collexis. for each document this yielded a list of biomedical concepts with attached relevance scores, which was used for subsequent processing. for each gene sense of a symbol, five randomly chosen abstracts were set aside for generating different reference cfps; the remaining abstracts were used for testing. the test set contained abstracts. the matching score between textual context and reference description was defined as the normalized cosine-vector score between the cfps of these two texts. overall accuracy of the disambiguation algorithm, defined as the percentage of abstracts in our test set in which the correct meaning of the homonym was chosen, was % when omim annotations were used as reference description. this was comparable to using one abstract as the reference, while accuracy increased to % when a cfp combination of five abstracts was used as the reference. for comparison, a simple majority rule resulted in a baseline accuracy of %. we made a breakdown of the errors when a combination of five abstracts was used as the reference description. as shown in table symbols indicating a gene were assigned a non-gene meaning in % of the cases; symbols with a non-gene meaning were misclassified as a gene even less frequently. for gene symbols with multiple gene meanings, % of the symbols were assigned to the incorrect genes. discussion ambiguity of gene symbols in free text is an impediment for the massive application of text mining and literature-based discovery methods. our assessment of gene symbol ambiguity indicates that the homonym problem cannot be ignored when text mining in the biological field is performed, corroborating findings of previous studies. we found up to 33% of the genes in our thesaurus being affected by homonymy, and even this high figure is underestimating the problem because we limited ourselves to human genes only, not considering other organisms and gene products. disambiguation would be a trivial task if each ambiguous symbol in an abstract were accompanied by its corresponding long form at least once. unfortunately, this approach is of limited practical value. we recently checked nature genetics and biomed central articles and found that only 30% of the gene symbols in the abstracts are accompanied by a matching long form. for an additional 8% of the symbols in the abstracts, the long form could be found in the full text. of all gene symbols mentioned in the full-text articles, only 18% were accompanied by a long form. we compared two sources of reference descriptions, gene annotations and abstracts about a particular gene. omim annotations did not perform better as a reference description than randomly chosen abstracts about a gene. the performance increased when the reference cfp was constructed from the information of several abstracts combined, but the improvement appeared marginal when more than three abstracts were used. this suggests that excellent disambiguation results can be obtained with relatively simple reference descriptions, and offers a viable way for massive acquisition of such descriptions from literature links in genetic databases, which in view of the extent of the homonym problem should be automatic for all practical purposes. our disambiguation algorithm could be used as part of a gene identification module in an information extraction application. in a recent review article on term identification, krauthammer and nenadic distinguish between two types of disambiguation: at the broader level of pinpointing the type of a concept and at the specific level of resolving different meanings of a term within a term class. our approach addresses both types of disambiguation. while the algorithm was developed and tested for disambiguation of gene symbols, the approach is general and easy to apply to other ambiguous entities as well, provided adequate reference descriptions are available. we would like to emphasize the practicality of our approach in terms of processing speed, scalability, and accuracy. the initial indexing process is the most time-consuming step, but in principle has to be done only once. for our whole test set of abstracts, indexing currently takes about two hours on a standard pentium iv computer. once the context and reference fingerprints are available, the disambiguation process itself is very fast, taking about two minutes for the whole test set. in practical applications, reference descriptions will be needed for many more than the almost homonymous gene symbols that we used in this study. considering that even a single abstract about a particular gene can provide an adequate reference description, our approach can easily be scaled up, for instance by taking the abstracts that are referenced with each of the gene descriptions in locuslink. in our download of locuslink, genes had at least one reference, and had three or more. automatic determination of a gene/non-gene threshold may be more difficult, as our approach presently requires the availability of examples of non-gene meanings of a symbol. we are currently investigating automatic threshold setting based on a general set of abstracts with non-gene meanings, obviating the need to acquire non-gene examples of each specific symbol. the accuracy of any disambiguation algorithm must be very high in order to be of practical value in massive literature mining. in this respect, gene symbols that are assigned a non-gene meaning may be less of a problem than the other way around, or than being assigned the wrong gene meaning. it should be remarked that these results pertain to our test set, which is large but still limited in scope because we only selected gene symbols that occur in omim and had six or more abstracts per gene sense. this may have favored selection of relatively well-known genes. it is conceivable that some abstracts with gene symbols that were not selected provide a less focussed context that would perform less well. in our data selection, we focused on human genes. the abstracts in our test set with ambiguous symbols that indicated a gene, were taken from two sources: omim, which is a database about human genes and diseases, or the sf/lf data set, if both short form and long form in the abstract exactly matched an entry in our human gene thesaurus. however, previous investigations showed that substantial ambiguity of gene symbols exists across species, and suggested that most of this ambiguity was attributable to homologous genes. in a random sample of abstracts from our test set with symbols that had a gene meaning, symbols referred to non-human genes, mostly from mouse or rat. all of these were apparently homologous to the human genes with identical names. in this study, we did not attempt to distinguish between homologous genes. model organisms are often used to understand the biology of human genes and the distinction between homologous genes in text is often difficult to make, or not useful. if disambiguation of homologous genes is important, though, our approach could be extended by including reference descriptions of genes from other species. finally, the current performance is based on reference descriptions that were acquired fully automatically. manual curation of these descriptions or their corresponding fingerprints for low-scoring symbols may further add to the algorithm's performance.  the ambiguity of gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. a simple, thesaurus-based disambiguation approach can resolve most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. the proposed method is fast and scalable, enabling gene-symbol disambiguation in massive text mining and information extraction applications. 
however, disambiguation of gene symbols is a major bottleneck. the extent of the homonym problem for human gene symbols is shown to be substantial, not only because one symbol can refer to multiple genes, but also because a gene symbol can have many non-gene meanings. a test set of medline abstracts, containing ambiguous human gene symbols taken from omim, was automatically generated. the ambiguity of human gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. we developed a simple thesaurus-based disambiguation algorithm that can operate with very little training data. overall accuracy of the disambiguation algorithm was up to % on the test set. the algorithm is fast and scalable, enabling gene-symbol disambiguation in massive text mining applications. the proposed disambiguation approach resolves most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge.
 massive text mining of the biological literature holds great promise of relating disparate information and discovering new knowledge. however, disambiguation of gene symbols is a major bottleneck.  we developed a simple thesaurus-based disambiguation algorithm that can operate with very little training data. the thesaurus comprises the information from five human genetic databases and mesh. the extent of the homonym problem for human gene symbols is shown to be substantial, not only because one symbol can refer to multiple genes, but also because a gene symbol can have many non-gene meanings. a test set of medline abstracts, containing ambiguous human gene symbols taken from omim, was automatically generated. overall accuracy of the disambiguation algorithm was up to % on the test set.  the ambiguity of human gene symbols is substantial, not only because one symbol may denote multiple genes but particularly because many symbols have other, non-gene meanings. the proposed disambiguation approach resolves most ambiguities in our test set with high accuracy, including the important gene/not a gene decisions. the algorithm is fast and scalable, enabling gene-symbol disambiguation in massive text mining applications. 