SciLite: a platform for displaying text-mined annotations as a means to link research articles with biological data.
Ontology highlight
ABSTRACT:
SUBMITTER: Venkatesan A
PROVIDER: S-EPMC5527546 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88,629 articles relating over 1,200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 254,173 toxicogenomic interactions (152,173 chemical-disease, 58,572 chemical-gene, 5,345 gene-disease and 38,083 phenotype interactions). All chemical-gene-disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer's text-mining process to collate the articles, and CTD's curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug-disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades' worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities. Database URL: http://ctdbase.org/
| S-EPMC3842776 | biostudies-literature
Project description:Successful publishing of an article depends on several factors, including the structure of the main text, the so-called introduction, methods, results and discussion structure (IMRAD). The first objective of our work is to provide recent results on the number of paragraphs (pars.) per section used in articles published in major medical journals. Our second objective is the investigation of other structural elements, i.e., number of tables, figures and references and the availability of supplementary material. We analyzed data from randomly selected original articles published in years 2005, 2010 and 2015 from the journals The BMJ, The Journal of the American Medical Association, The Lancet, The New England Journal of Medicine and PLOS Medicine. Per journal and year 30 articles were investigated. Random effect meta-analyses were performed to provide pooled estimates. The effect of time was analyzed by linear mixed models. All articles followed the IMRAD structure. The number of pars. per section increased for all journals over time with 1.08 (95% confidence interval (CI): 0.70-1.46) pars. per every two years. The largest increase was observed for the methods section (0.29 pars. per year; 95% confidence interval (CI): 0.19-0.39). PLOS Medicine had the highest number of pars. The number of tables did not change, but number of figures and references increased slightly. Not only the standard IMRAD structure should be used to increase the likelihood for publication of an article but also the general layout of the target journal. Supplementary material has become standard. If no journal-specific information is available, authors should use 3/10/9/8 pars. for the introduction/methods/results/discussion sections.
| S-EPMC7544105 | biostudies-literature
Project description:Documented occurrences of fossil taxa are the empirical foundation for understanding large-scale biodiversity changes and evolutionary dynamics in deep time. The fossil record contains vast amounts of understudied taxa. Yet the compilation of huge volumes of data remains a labour-intensive impediment to a more complete understanding of Earth's biodiversity history. Even so, many occurrence records of species and genera in these taxa can be uncovered in the palaeontological literature. Here, we extract observations of fossils and their inferred ages from unstructured text in books and scientific articles using machine-learning approaches. We use Bryozoa, a group of marine invertebrates with a rich fossil record, as a case study. Building on recent advances in computational linguistics, we develop a pipeline to recognize taxonomic names and geologic time intervals in published literature and use supervised learning to machine-read whether the species in question occurred in a given age interval. Intermediate machine error rates appear comparable to human error rates in a simple trial, and resulting genus richness curves capture the main features of published fossil diversity studies of bryozoans. We believe our automated pipeline, that greatly reduced the time required to compile our dataset, can help others compile similar data for other taxa.
| S-EPMC6501925 | biostudies-literature
Project description:Materials discovery has become significantly facilitated and accelerated by high-throughput ab-initio computations. This ability to rapidly design interesting novel compounds has displaced the materials innovation bottleneck to the development of synthesis routes for the desired material. As there is no a fundamental theory for materials synthesis, one might attempt a data-driven approach for predicting inorganic materials synthesis, but this is impeded by the lack of a comprehensive database containing synthesis processes. To overcome this limitation, we have generated a dataset of "codified recipes" for solid-state synthesis automatically extracted from scientific publications. The dataset consists of 19,488 synthesis entries retrieved from 53,538 solid-state synthesis paragraphs by using text mining and natural language processing approaches. Every entry contains information about target material, starting compounds, operations used and their conditions, as well as the balanced chemical equation of the synthesis reaction. The dataset is publicly available and can be used for data mining of various aspects of inorganic materials synthesis.
| S-EPMC6794279 | biostudies-literature
Project description:Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.
| S-EPMC1560402 | biostudies-literature
Project description:BackgroundThe Portable Document Format (PDF) is the most commonly used file format for online scientific publications. The absence of effective means to extract text from these PDF files in a layout-aware manner presents a significant challenge for developers of biomedical text mining or biocuration informatics systems that use published literature as an information source. In this paper we introduce the 'Layout-Aware PDF Text Extraction' (LA-PDFText) system to facilitate accurate extraction of text from PDF files of research articles for use in text mining applications.ResultsOur paper describes the construction and performance of an open source system that extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles and is meant as a baseline for further experiments into more advanced extraction methods that handle multi-modal content, such as images and graphs. The system works in a three-stage process: (1) Detecting contiguous text blocks using spatial layout processing to locate and identify blocks of contiguous text, (2) Classifying text blocks into rhetorical categories using a rule-based method and (3) Stitching classified text blocks together in the correct order resulting in the extraction of text from section-wise grouped blocks. We show that our system can identify text blocks and classify them into rhetorical categories with Precision1 = 0.96% Recall = 0.89% and F1 = 0.91%. We also present an evaluation of the accuracy of the block detection algorithm used in step 2. Additionally, we have compared the accuracy of the text extracted by LA-PDFText to the text from the Open Access subset of PubMed Central. We then compared this accuracy with that of the text extracted by the PDF2Text system, 2commonly used to extract text from PDF. Finally, we discuss preliminary error analysis for our system and identify further areas of improvement.ConclusionsLA-PDFText is an open-source tool for accurately extracting text from full-text scientific articles. The release of the system is available at http://code.google.com/p/lapdftext/.
| S-EPMC3441580 | biostudies-literature
Project description:Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services.
| S-EPMC3667078 | biostudies-literature
Project description:BackgroundGenes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.ResultsIn this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.ConclusionGEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.
| S-EPMC2335285 | biostudies-literature
Project description:BackgroundIncreasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway - metabolic pathways - has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein-protein interactions.ResultsWhen evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task.ConclusionsWe conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein-protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.
| S-EPMC3475109 | biostudies-literature
Project description:Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases difficult and poses a challenge in using these for data mining or question answering.We report on the semi-automatic construction of the PHARE relationship ontology (the PHArmacogenomic RElationships Ontology) consisting of 200 curated relations from over 40,000 heterogeneous relationships extracted via text-mining. These heterogeneous relations are then mapped to the PHARE ontology using synonyms, entity descriptions and hierarchies of entities and roles. Once mapped, relationships can be normalized and compared using the structure of the ontology to identify relationships that have similar semantics but different syntax. We compare and contrast the manual procedure with a fully automated approach using WordNet to quantify the degree of integration enabled by iterative curation and refinement of the PHARE ontology. The result of such integration is a repository of normalized biomedical relationships, named PHARE-KB, which can be queried using Semantic Web technologies such as SPARQL and can be visualized in the form of a biological network.The PHARE ontology serves as a common semantic framework to integrate more than 40,000 relationships pertinent to pharmacogenomics. The PHARE ontology forms the foundation of a knowledge base named PHARE-KB. Once populated with relationships, PHARE-KB (i) can be visualized in the form of a biological network to guide human tasks such as database curation and (ii) can be queried programmatically to guide bioinformatics applications such as the prediction of molecular interactions. PHARE is available at http://purl.bioontology.org/ontology/PHARE.
| S-EPMC3102890 | biostudies-other