Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency.     

GENIA corpus--semantically annotated corpus for bio-textmining

MOTIVATION: Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining. RESULTS: GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400,000 words and almost 100,000 annotations for biological terms.     

Combining literature text mining with microarray data: advances for system biology modeling

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.     

Textpresso: an ontology-based information retrieval and extraction system for biological literature

We have developed Textpresso, a new text-mining system for scientific literature whose capabilities go far beyond those of a simple keyword search engine. Textpresso's two major elements are a collection of the full text of scientific articles split into individual sentences, and the implementation of categories of terms for which a database of articles and individual sentences can be searched. The categories are classes of biological concepts (e.g., gene, allele, cell or cell group, phenotype, etc.) and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., biological process, etc.). Together they form a catalog of types of objects and concepts called an ontology. After this ontology is populated with terms, the whole corpus of articles and abstracts is marked up to identify terms of these categories. The current ontology comprises 33 categories of terms. A search engine enables the user to search for one or a combination of these tags and/or keywords within a sentence or document, and as the ontology allows word meaning to be queried, it is possible to formulate semantic queries. Full text access increases recall of biological data types from 45% to 95%. Extraction of particular biological facts, such as gene-gene interactions, can be accelerated significantly by ontologies, with Textpresso automatically performing nearly as well as expert curators to identify sentences; in searches for two uniquely named genes and an interaction term, the ontology confers a 3-fold increase of search efficiency. Textpresso currently focuses on Caenorhabditis elegans literature, with 3,800 full text articles and 16,000 abstracts. The lexicon of the ontology contains 14,500 entries, each of which includes all versions of a specific word or phrase, and it includes all categories of the Gene Ontology database. Textpresso is a useful curation tool, as well as search engine for researchers, and can readily be extended to other organism-specific corpora of text. Textpresso can be accessed at http://www.textpresso.org or via WormBase at http://www.wormbase.org.     

Which species is it? Species-driven gene name disambiguation using random walks over a mixture of adjacency matrices

MOTIVATION: The scientific literature contains a wealth of information about biological systems. Manual curation lacks the scalability to extract this information due to the ever-increasing numbers of papers being published. The development and application of text mining technologies has been proposed as a way of dealing with this problem. However, the inter-species ambiguity of the genomic nomenclature makes mapping of gene mentions identified in text to their corresponding Entrez gene identifiers an extremely difficult task. We propose a novel method, which transforms a MEDLINE record into a mixture of adjacency matrices; by performing a random walkover the resulting graph, we can perform multi-class supervised classification allowing the assignment of taxonomy identifiers to individual gene mentions. The ability to achieve good performance at this task has a direct impact on the performance of normalizing gene mentions to Entrez gene identifiers. Such graph mixtures add flexibility and allow us to generate probabilistic classification schemes that naturally reflect the uncertainties inherent, even in literature-derived data. RESULTS: Our method performs well in terms of both micro- and macro-averaged performance, achieving micro-F(1) of 0.76 and macro-F(1) of 0.36 on the publicly available DECA corpus. Re-curation of the DECA corpus was performed, with our method achieving 0.88 micro-F(1) and 0.51 macro-F(1). Our method improves over standard classification techniques [such as support vector machines (SVMs)] in a number of ways: flexibility, interpretability and its resistance to the effects of class bias in the training data. Good performance is achieved without the need for computationally expensive parse tree generation or 'bag of words classification'.     

MINT and IntAct contribute to the Second BioCreative challenge: serving the text-mining community with high quality molecular interaction data

Background

In the absence of consolidated pipelines to archive biological data electronically, information dispersed in the literature must be captured by manual annotation. Unfortunately, manual annotation is time consuming and the coverage of published interaction data is therefore far from complete. The use of text-mining tools to identify relevant publications and to assist in the initial information extraction could help to improve the efficiency of the curation process and, as a consequence, the database coverage of data available in the literature. The 2006 BioCreative competition was aimed at evaluating text-mining procedures in comparison with manual annotation of protein-protein interactions.

Results

To aid the BioCreative protein-protein interaction task, IntAct and MINT (Molecular INTeraction) provided both the training and the test datasets. Data from both databases are comparable because they were curated according to the same standards. During the manual curation process, the major cause of data loss in mining the articles for information was ambiguity in the mapping of the gene names to stable UniProtKB database identifiers. It was also observed that most of the information about interactions was contained only within the full-text of the publication; hence, text mining of protein-protein interaction data will require the analysis of the full-text of the articles and cannot be restricted to the abstract.

Conclusion

The development of text-mining tools to extract protein-protein interaction information may increase the literature coverage achieved by manual curation. To support the text-mining community, databases will highlight those sentences within the articles that describe the interactions. These will supply data-miners with a high quality dataset for algorithm development. Furthermore, the dictionary of terms created by the BioCreative competitors could enrich the synonym list of the PSI-MI (Proteomics Standards Initiative-Molecular Interactions) controlled vocabulary, which is used by both databases to annotate their data content.

     

Semi-automated curation of protein subcellular localization: a text mining-based approach to Gene Ontology (GO) Cellular Component curation

Background  

Manual curation of experimental data from the biomedical literature is an expensive and time-consuming endeavor. Nevertheless, most biological knowledge bases still rely heavily on manual curation for data extraction and entry. Text mining software that can semi- or fully automate information retrieval from the literature would thus provide a significant boost to manual curation efforts.     

The FlyBase database of the Drosophila genome projects and community literature

FlyBase (http://flybase.bio.indiana.edu/) provides an integrated view of the fundamental genomic and genetic data on the major genetic model Drosophila melanogaster and related species. FlyBase has primary responsibility for the continual reannotation of the D. melanogaster genome. The ultimate goal of the reannotation effort is to decorate the euchromatic sequence of the genome with as much biological information as is available from the community and from the major genome project centers. A complete revision of the annotations of the now-finished euchromatic genomic sequence has been completed. There are many points of entry to the genome within FlyBase, most notably through maps, gene products and ontologies, structured phenotypic and gene expression data, and anatomy.     

Literature mining and database annotation of protein phosphorylation using a rule-based system

MOTIVATION: A large volume of experimental data on protein phosphorylation is buried in the fast-growing PubMed literature. While of great value, such information is limited in databases owing to the laborious process of literature-based curation. Computational literature mining holds promise to facilitate database curation. RESULTS: A rule-based system, RLIMS-P (Rule-based LIterature Mining System for Protein Phosphorylation), was used to extract protein phosphorylation information from MEDLINE abstracts. An annotation-tagged literature corpus developed at PIR was used to evaluate the system for finding phosphorylation papers and extracting phosphorylation objects (kinases, substrates and sites) from abstracts. RLIMS-P achieved a precision and recall of 91.4 and 96.4% for paper retrieval, and of 97.9 and 88.0% for extraction of substrates and sites. Coupling the high recall for paper retrieval and high precision for information extraction, RLIMS-P facilitates literature mining and database annotation of protein phosphorylation.     

Literature-aided interpretation of gene expression data with the weighted global test

Most methods for the interpretation of gene expression profiling experiments rely on the categorization of genes, as provided by the Gene Ontology (GO) and pathway databases. Due to the manual curation process, such databases are never up-to-date and tend to be limited in focus and coverage. Automated literature mining tools provide an attractive, alternative approach. We review how they can be employed for the interpretation of gene expression profiling experiments. We illustrate that their comprehensive scope aids the interpretation of data from domains poorly covered by GO or alternative databases, and allows for the linking of gene expression with diseases, drugs, tissues and other types of concepts. A framework for proper statistical evaluation of the associations between gene expression values and literature concepts was lacking and is now implemented in a weighted extension of global test. The weights are the literature association scores and reflect the importance of a gene for the concept of interest. In a direct comparison with classical GO-based gene sets, we show that use of literature-based associations results in the identification of much more specific GO categories. We demonstrate the possibilities for linking of gene expression data to patient survival in breast cancer and the action and metabolism of drugs. Coupling with online literature mining tools ensures transparency and allows further study of the identified associations. Literature mining tools are therefore powerful additions to the toolbox for the interpretation of high-throughput genomics data.     

A Chado case study: an ontology-based modular schema for representing genome-associated biological information

MOTIVATION: A few years ago, FlyBase undertook to design a new database schema to store Drosophila data. It would fully integrate genomic sequence and annotation data with bibliographic, genetic, phenotypic and molecular data from the literature representing a distillation of the first 100 years of research on this major animal model system. In developing this new integrated schema, FlyBase also made a commitment to ensure that its design was generic, extensible and available as open source, so that it could be employed as the core schema of any model organism data repository, thereby avoiding redundant software development and potentially increasing interoperability. Our question was whether we could create a relational database schema that would be successfully reused. RESULTS: Chado is a relational database schema now being used to manage biological knowledge for a wide variety of organisms, from human to pathogens, especially the classes of information that directly or indirectly can be associated with genome sequences or the primary RNA and protein products encoded by a genome. Biological databases that conform to this schema can interoperate with one another, and with application software from the Generic Model Organism Database (GMOD) toolkit. Chado is distinctive because its design is driven by ontologies. The use of ontologies (or controlled vocabularies) is ubiquitous across the schema, as they are used as a means of typing entities. The Chado schema is partitioned into integrated subschemas (modules), each encapsulating a different biological domain, and each described using representations in appropriate ontologies. To illustrate this methodology, we describe here the Chado modules used for describing genomic sequences. AVAILABILITY: GMOD is a collaboration of several model organism database groups, including FlyBase, to develop a set of open-source software for managing model organism data. The Chado schema is freely distributed under the terms of the Artistic License (http://www.opensource.org/licenses/artistic-license.php) from GMOD (www.gmod.org).     

Building a protein name dictionary from full text: a machine learning term extraction approach

Background  

The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature.     

Towards classifying species in systems biology papers using text mining

Background

In recent years high throughput methods have led to a massive expansion in the free text literature on molecular biology. Automated text mining has developed as an application technology for formalizing this wealth of published results into structured database entries. However, database curation as a task is still largely done by hand, and although there have been many studies on automated approaches, problems remain in how to classify documents into top-level categories based on the type of organism being investigated. Here we present a comparative analysis of state of the art supervised models that are used to classify both abstracts and full text articles for three model organisms.

Results

Ablation experiments were conducted on a large gold standard corpus of 10,000 abstracts and full papers containing data on three model organisms (fly, mouse and yeast). Among the eight learner models tested, the best model achieved an F-score of 97.1% for fly, 88.6% for mouse and 85.5% for yeast using a variety of features that included gene name, organism frequency, MeSH headings and term-species associations. We noted that term-species associations were particularly effective in improving classification performance. The benefit of using full text articles over abstracts was consistently observed across all three organisms.

Conclusions

By comparing various learner algorithms and features we presented an optimized system that automatically detects the major focus organism in full text articles for fly, mouse and yeast. We believe the method will be extensible to other organism types.

     

miRTex: A Text Mining System for miRNA-Gene Relation Extraction

MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes.     

FlyBase: a Drosophila database. Flybase Consortium.

FlyBase (http://flybase.bio.indiana.edu/) is a comprehensive database of genetic and molecular data concerning Drosophila . FlyBase is maintained as a relational database (in Sybase) and is made available as html documents and flat files. The scope of FlyBase includes: genes, alleles (with phenotypes), aberrations, transposons, pointers to sequence data, gene products, maps, clones, stock lists, Drosophila workers and bibliographic references.     

CODON—Software to manual curation of prokaryotic genomes

Genome annotation conceptually consists of inferring and assigning biological information to gene products. Over the years, numerous pipelines and computational tools have been developed aiming to automate this task and assist researchers in gaining knowledge about target genes of study. However, even with these technological advances, manual annotation or manual curation is necessary, where the information attributed to the gene products is verified and enriched. Despite being called the gold standard process for depositing data in a biological database, the task of manual curation requires significant time and effort from researchers who sometimes have to parse through numerous products in various public databases. To assist with this problem, we present CODON, a tool for manual curation of genomic data, capable of performing the prediction and annotation process. This software makes use of a finite state machine in the prediction process and automatically annotates products based on information obtained from the Uniprot database. CODON is equipped with a simple and intuitive graphic interface that assists on manual curation, enabling the user to decide about the analysis based on information as to identity, length of the alignment, and name of the organism in which the product obtained a match. Further, visual analysis of all matches found in the database is possible, impacting significantly in the curation task considering that the user has at his disposal all the information available for a given product. An analysis performed on eleven organisms was used to test the efficiency of this tool by comparing the results of prediction and annotation through CODON to ones from the NCBI and RAST platforms.     

The role of bioinformatics in pathway curation

Diagrams and models of biological pathways are useful tools in biology. Pathway diagrams are mainly used for illustrative purposes for instance in textbooks and in presentations. Pathway models are used in the analysis of genomic data. Bridging the gap between diagrams and models allows not only the analysis of genomics data and interactions but also the visualisation of the results in a variety of different ways. The knowledge needed for pathway creation and curation is available from three distinct sources: databases, literature and experts. We describe the role of bioinformatics in facilitating the creation and curation of pathway.