Thesaurus-based disambiguation of gene symbols

BACKGROUND: 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. RESULTS: 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 (33% of the genes in our combined thesaurus had one or more ambiguous symbols), 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 52,529 Medline abstracts, containing 690 ambiguous human gene symbols taken from OMIM, was automatically generated. Overall accuracy of the disambiguation algorithm was up to 92.7% on the test set. CONCLUSION: 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

The strength of co-authorship in gene name disambiguation

<p>Abstract</p> <p>Background</p> <p>A biomedical entity mention in articles and other free texts is often ambiguous. For example, 13% of the gene names (aliases) might refer to more than one gene. The task of Gene Symbol Disambiguation (GSD) – a special case of Word Sense Disambiguation (WSD) – is to assign a unique gene identifier for all identified gene name aliases in biology-related articles. Supervised and unsupervised machine learning WSD techniques have been applied in the biomedical field with promising results. We examine here the utilisation potential of the fact – one of the special features of biological articles – that the authors of the documents are known through graph-based semi-supervised methods for the GSD task.</p> <p>Results</p> <p>Our key hypothesis is that a biologist refers to each particular gene by a fixed gene alias and this holds for the co-authors as well. To make use of the co-authorship information we decided to build the inverse co-author graph on MedLine abstracts. The nodes of the inverse co-author graph are articles and there is an edge between two nodes if and only if the two articles have a mutual author. We introduce here two methods using distances (based on the graph) of abstracts for the GSD task. We found that a disambiguation decision can be made in 85% of cases with an extremely high (99.5%) precision rate just by using information obtained from the inverse co-author graph. We incorporated the co-authorship information into two GSD systems in order to attain full coverage and in experiments our procedure achieved precision of 94.3%, 98.85%, 96.05% and 99.63% on the human, mouse, fly and yeast GSD evaluation sets, respectively.</p> <p>Conclusion</p> <p>Based on the promising results obtained so far we suggest that the co-authorship information and the circumstances of the articles' release (like the title of the journal, the year of publication) can be a crucial building block of any sophisticated similarity measure among biological articles and hence the methods introduced here should be useful for other biomedical natural language processing tasks (like organism or target disease detection) as well.</p

Disclosing ambiguous gene aliases by automatic literature profiling

Submitted by Nuzia Santos ([email protected]) on 2015-01-14T10:55:18Z No. of bitstreams: 1 Disclosing ambiguous gene aliases by automatic.pdf: 217573 bytes, checksum: ce54aa2c4ea49eb989f9e7308d827ce6 (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2015-01-14T10:55:25Z (GMT) No. of bitstreams: 1 Disclosing ambiguous gene aliases by automatic.pdf: 217573 bytes, checksum: ce54aa2c4ea49eb989f9e7308d827ce6 (MD5)Approved for entry into archive by Nuzia Santos ([email protected]) on 2015-01-14T11:01:59Z (GMT) No. of bitstreams: 1 Disclosing ambiguous gene aliases by automatic.pdf: 217573 bytes, checksum: ce54aa2c4ea49eb989f9e7308d827ce6 (MD5)Made available in DSpace on 2015-01-14T11:01:59Z (GMT). No. of bitstreams: 1 Disclosing ambiguous gene aliases by automatic.pdf: 217573 bytes, checksum: ce54aa2c4ea49eb989f9e7308d827ce6 (MD5) Previous issue date: 2010Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Centro de Excelência em Bioinformática. Belo Horizonte, MG, Brasil/Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Grupo de Genômica e Biologia Computacional. Belo Horizonte, MG, BrasilGlaxoSmithKline Moore Dr. Molecular Discovery Research. Research Triangle Park, NC, USAFundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Centro de Excelência em Bioinformática. Belo Horizonte, MG, Brasil/Fundação Oswaldo Cruz. Centro de Pesquisa René Rachou. Grupo de Genômica e Biologia Computacional. Belo Horizonte, MG, BrasilBackground Retrieving pertinent information from biological scientific literature requires cutting-edge text mining methods which may be able to recognize the meaning of the very ambiguous names of biological entities. Aliases of a gene share a common vocabulary in their respective collections of PubMed abstracts. This may be true even when these aliases are not associated with the same subset of documents. This gene-specific vocabulary defines a unique fingerprint that can be used to disclose ambiguous aliases. The present work describes an original method for automatically assessing the ambiguity levels of gene aliases in large gene terminologies based exclusively in the content of their associated literature. The method can deal with the two major problems restricting the usage of current text mining tools: 1) different names associated with the same gene; and 2) one name associated with multiple genes, or even with non-gene entities. Important, this method does not require training examples. Results Aliases were considered “ambiguous” when their Jaccard distance to the respective official gene symbol was equal or greater than the smallest distance between the official gene symbol and one of the three internal controls (randomly picked unrelated official gene symbols). Otherwise, they were assigned the status of “synonyms”. We evaluated the coherence of the results by comparing the frequencies of the official gene symbols in the text corpora retrieved with their respective “synonyms” or “ambiguous” aliases. Official gene symbols were mentioned in the abstract collections of 42 % (70/165) of their respective synonyms. No official gene symbol occurred in the abstract collections of any of their respective ambiguous aliases. In overall, querying PubMed with official gene symbols and “synonym” aliases allowed a 3.6-fold increase in the number of unique documents retrieved. Conclusions These results confirm that this method is able to distinguish between synonyms and ambiguous gene aliases based exclusively on their vocabulary fingerprint. The approach we describe could be used to enhance the retrieval of relevant literature related to a gen

Anni 2.0: a multipurpose text-mining tool for the life sciences

Anni 2.0 provides an ontology-based interface to MEDLINE

GeneSeer: A sage for gene names and genomic resources

BACKGROUND: Independent identification of genes in different organisms and assays has led to a multitude of names for each gene. This balkanization makes it difficult to use gene names to locate genomic resources, homologs in other species and relevant publications. METHODS: We solve the naming problem by collecting data from a variety of sources and building a name-translation database. We have also built a table of homologs across several model organisms: H. sapiens, M. musculus, R. norvegicus, D. melanogaster, C. elegans, S. cerevisiae, S. pombe and A. thaliana. This allows GeneSeer to draw phylogenetic trees and identify the closest homologs. This, in turn, allows the use of names from one species to identify homologous genes in another species. A website is connected to the database to allow user-friendly access to our tools and external genomic resources using familiar gene names. CONCLUSION: GeneSeer allows access to gene information through common names and can map sequences to names. GeneSeer also allows identification of homologs and paralogs for a given gene. A variety of genomic data such as sequences, SNPs, splice variants, expression patterns and others can be accessed through the GeneSeer interface. It is freely available over the web and can be incorporated in other tools through an http-based software interface described on the website. It is currently used as the search engine in the RNAi codex resource, which is a portal for short hairpin RNA (shRNA) gene-silencing constructs

Retrieval with gene queries

BACKGROUND: Accuracy of document retrieval from MEDLINE for gene queries is crucially important for many applications in bioinformatics. We explore five information retrieval-based methods to rank documents retrieved by PubMed gene queries for the human genome. The aim is to rank relevant documents higher in the retrieved list. We address the special challenges faced due to ambiguity in gene nomenclature: gene terms that refer to multiple genes, gene terms that are also English words, and gene terms that have other biological meanings. RESULTS: Our two baseline ranking strategies are quite similar in performance. Two of our three LocusLink-based strategies offer significant improvements. These methods work very well even when there is ambiguity in the gene terms. Our best ranking strategy offers significant improvements on three different kinds of ambiguities over our two baseline strategies (improvements range from 15.9% to 17.7% and 11.7% to 13.3% depending on the baseline). For most genes the best ranking query is one that is built from the LocusLink (now Entrez Gene) summary and product information along with the gene names and aliases. For others, the gene names and aliases suffice. We also present an approach that successfully predicts, for a given gene, which of these two ranking queries is more appropriate. CONCLUSION: We explore the effect of different post-retrieval strategies on the ranking of documents returned by PubMed for human gene queries. We have successfully applied some of these strategies to improve the ranking of relevant documents in the retrieved sets. This holds true even when various kinds of ambiguity are encountered. We feel that it would be very useful to apply strategies like ours on PubMed search results as these are not ordered by relevance in any way. This is especially so for queries that retrieve a large number of documents

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

Background Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments. Results In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. FAUN is tested on three manually constructed gene document collections. Its utility and performance as a knowledge discovery tool is demonstrated using a set of genes associated with Autism. Conclusions FAUN not only assists researchers to use biomedical literature efficiently, but also provides utilities for knowledge discovery. This Web-based software environment may be useful for the validation and analysis of functional associations in gene subsets identified by high-throughput experiments

Literature Lab: a method of automated literature interrogation to infer biology from microarray analysis

<p>Abstract</p> <p>Background</p> <p>The biomedical literature is a rich source of associative information but too vast for complete manual review. We have developed an automated method of literature interrogation called "Literature Lab" that identifies and ranks associations existing in the literature between gene sets, such as those derived from microarray experiments, and curated sets of key terms (i.e. pathway names, medical subject heading (MeSH) terms, etc).</p> <p>Results</p> <p>Literature Lab was developed using differentially expressed gene sets from three previously published cancer experiments and tested on a fourth, novel gene set. When applied to the genesets from the published data including an <it>in vitro </it>experiment, an <it>in vivo </it>mouse experiment, and an experiment with human tumor samples, Literature Lab correctly identified known biological processes occurring within each experiment. When applied to a novel set of genes differentially expressed between locally invasive and metastatic prostate cancer, Literature Lab identified a strong association between the pathway term "FOSB" and genes with increased expression in metastatic prostate cancer. Immunohistochemistry subsequently confirmed increased nuclear FOSB staining in metastatic compared to locally invasive prostate cancers.</p> <p>Conclusion</p> <p>This work demonstrates that Literature Lab can discover key biological processes by identifying meritorious associations between experimentally derived gene sets and key terms within the biomedical literature.</p

Standards and legacies: Pragmatic constraints on a uniform gene nomenclature

Over the past half-century, there have been concerted efforts to standardize how clinicians and medical researchers refer to genetic material. However, practical and historical impediments thwart this goal. In the current paper I argue that the ontological status of a genetic mutation cannot be cleanly separated from its pragmatic role in therapy. Attempts at standardization fail due to the non-standardized ends to which genetic information is employed, along with historical inertia and unregulated local innovation. These factors prevent rationalistic attempts to ‘modernize’ what is otherwise trumpeted as the most modern of the medical sciences