1,388 research outputs found
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
Citations: Indicators of Quality? The Impact Fallacy
We argue that citation is a composed indicator: short-term citations can be
considered as currency at the research front, whereas long-term citations can
contribute to the codification of knowledge claims into concept symbols.
Knowledge claims at the research front are more likely to be transitory and are
therefore problematic as indicators of quality. Citation impact studies focus
on short-term citation, and therefore tend to measure not epistemic quality,
but involvement in current discourses in which contributions are positioned by
referencing. We explore this argument using three case studies: (1) citations
of the journal Soziale Welt as an example of a venue that tends not to publish
papers at a research front, unlike, for example, JACS; (2) Robert Merton as a
concept symbol across theories of citation; and (3) the Multi-RPYS
("Multi-Referenced Publication Year Spectroscopy") of the journals
Scientometrics, Gene, and Soziale Welt. We show empirically that the
measurement of "quality" in terms of citations can further be qualified:
short-term citation currency at the research front can be distinguished from
longer-term processes of incorporation and codification of knowledge claims
into bodies of knowledge. The recently introduced Multi-RPYS can be used to
distinguish between short-term and long-term impacts.Comment: accepted for publication in Frontiers in Research Metrics and
Analysis; doi: 10.3389/frma.2016.0000
Large-scale event extraction from literature with multi-level gene normalization
Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons -Attribution - Share Alike (CC BY-SA) license
Chi-square-based scoring function for categorization of MEDLINE citations
Objectives: Text categorization has been used in biomedical informatics for
identifying documents containing relevant topics of interest. We developed a
simple method that uses a chi-square-based scoring function to determine the
likelihood of MEDLINE citations containing genetic relevant topic. Methods: Our
procedure requires construction of a genetic and a nongenetic domain document
corpus. We used MeSH descriptors assigned to MEDLINE citations for this
categorization task. We compared frequencies of MeSH descriptors between two
corpora applying chi-square test. A MeSH descriptor was considered to be a
positive indicator if its relative observed frequency in the genetic domain
corpus was greater than its relative observed frequency in the nongenetic
domain corpus. The output of the proposed method is a list of scores for all
the citations, with the highest score given to those citations containing MeSH
descriptors typical for the genetic domain. Results: Validation was done on a
set of 734 manually annotated MEDLINE citations. It achieved predictive
accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method
by comparing it to three machine learning algorithms (support vector machines,
decision trees, na\"ive Bayes). Although the differences were not statistically
significantly different, results showed that our chi-square scoring performs as
good as compared machine learning algorithms. Conclusions: We suggest that the
chi-square scoring is an effective solution to help categorize MEDLINE
citations. The algorithm is implemented in the BITOLA literature-based
discovery support system as a preprocessor for gene symbol disambiguation
process.Comment: 34 pages, 2 figure
Ambiguity of human gene symbols in LocusLink and MEDLINE: creating an inventory and a disambiguation test collection
Genes are discovered almost on a daily basis and new names have to be
found. Although there are guidelines for gene nomenclature, the naming
process is highly creative. Human genes are often named with a gene symbol
and a longer, more descriptive term; the short form is very often an
abbreviation of the long form. Abbreviations in biomedical language are
highly ambiguous, i.e., one gene symbol often refers to more than one
gene.Using an existing abbreviation expansion algorithm,we explore MEDLINE
for the use of human gene symbols derived from LocusLink. It turns out
that just over 40% of these symbols occur in MEDLINE, however, many of
these occurrences are not related to genes. Along the process of making an
inventory, a disambiguation test collection is constructed automatically
ProNormz – An integrated approach for human proteins and protein kinases normalization
AbstractThe task of recognizing and normalizing protein name mentions in biomedical literature is a challenging task and important for text mining applications such as protein–protein interactions, pathway reconstruction and many more. In this paper, we present ProNormz, an integrated approach for human proteins (HPs) tagging and normalization. In Homo sapiens, a greater number of biological processes are regulated by a large human gene family called protein kinases by post translational phosphorylation. Recognition and normalization of human protein kinases (HPKs) is considered to be important for the extraction of the underlying information on its regulatory mechanism from biomedical literature. ProNormz distinguishes HPKs from other HPs besides tagging and normalization. To our knowledge, ProNormz is the first normalization system available to distinguish HPKs from other HPs in addition to gene normalization task. ProNormz incorporates a specialized synonyms dictionary for human proteins and protein kinases, a set of 15 string matching rules and a disambiguation module to achieve the normalization. Experimental results on benchmark BioCreative II training and test datasets show that our integrated approach achieve a fairly good performance and outperforms more sophisticated semantic similarity and disambiguation systems presented in BioCreative II GN task. As a freely available web tool, ProNormz is useful to developers as extensible gene normalization implementation, to researchers as a standard for comparing their innovative techniques, and to biologists for normalization and categorization of HPs and HPKs mentions in biomedical literature. URL: http://www.biominingbu.org/pronormz
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Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues
BACKGROUND: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information retrieval systems because ambiguous words negatively impact accurate access to literature containing biomolecular entities, such as genes, proteins, cells, diseases, and other important entities. Automated techniques have been developed that address the WSD problem for a number of text processing situations, but the problem is still a challenging one. Supervised WSD machine learning (ML) methods have been applied in the biomedical domain and have shown promising results, but the results typically incorporate a number of confounding factors, and it is problematic to truly understand the effectiveness and generalizability of the methods because these factors interact with each other and affect the final results. Thus, there is a need to explicitly address the factors and to systematically quantify their effects on performance. RESULTS: Experiments were designed to measure the effect of "sample size" (i.e. size of the datasets), "sense distribution" (i.e. the distribution of the different meanings of the ambiguous word) and "degree of difficulty" (i.e. the measure of the distances between the meanings of the senses of an ambiguous word) on the performance of WSD classifiers. Support Vector Machine (SVM) classifiers were applied to an automatically generated data set containing four ambiguous biomedical abbreviations: BPD, BSA, PCA, and RSV, which were chosen because of varying degrees of differences in their respective senses. Results showed that: 1) increasing the sample size generally reduced the error rate, but this was limited mainly to well-separated senses (i.e. cases where the distances between the senses were large); in difficult cases an unusually large increase in sample size was needed to increase performance slightly, which was impractical, 2) the sense distribution did not have an effect on performance when the senses were separable, 3) when there was a majority sense of over 90%, the WSD classifier was not better than use of the simple majority sense, 4) error rates were proportional to the similarity of senses, and 5) there was no statistical difference between results when using a 5-fold or 10-fold cross-validation method. Other issues that impact performance are also enumerated. CONCLUSION: Several different independent aspects affect performance when using ML techniques for WSD. We found that combining them into one single result obscures understanding of the underlying methods. Although we studied only four abbreviations, we utilized a well-established statistical method that guarantees the results are likely to be generalizable for abbreviations with similar characteristics. The results of our experiments show that in order to understand the performance of these ML methods it is critical that papers report on the baseline performance, the distribution and sample size of the senses in the datasets, and the standard deviation or confidence intervals. In addition, papers should also characterize the difficulty of the WSD task, the WSD situations addressed and not addressed, as well as the ML methods and features used. This should lead to an improved understanding of the generalizablility and the limitations of the methodology
Overview of BioCreative II gene normalization
Background: The goal of the gene normalization task is to link genes or gene products mentioned in the literature to biological databases. This is a key step in an accurate search of the biological literature. It is a challenging task, even for the human expert; genes are often described rather than referred to by gene symbol and, confusingly, one gene name may refer to different genes (often from different organisms). For BioCreative II, the task was to list the Entrez Gene identifiers for human genes or gene products mentioned in PubMed/MEDLINE abstracts. We selected abstracts associated with articles previously curated for human genes. We provided 281 expert-annotated abstracts containing 684 gene identifiers for training, and a blind test set of 262 documents containing 785 identifiers, with a gold standard created by expert annotators. Inter-annotator agreement was measured at over 90%. Results: Twenty groups submitted one to three runs each, for a total of 54 runs. Three systems achieved F-measures (balanced precision and recall) between 0.80 and 0.81. Combining the system outputs using simple voting schemes and classifiers obtained improved results; the best composite system achieved an F-measure of 0.92 with 10-fold cross-validation. A 'maximum recall' system based on the pooled responses of all participants gave a recall of 0.97 (with precision 0.23), identifying 763 out of 785 identifiers. Conclusion: Major advances for the BioCreative II gene normalization task include broader participation (20 versus 8 teams) and a pooled system performance comparable to human experts, at over 90% agreement. These results show promise as tools to link the literature with biological databases
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