A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience.
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ABSTRACT:
SUBMITTER: Shardlow M
PROVIDER: S-EPMC6594987 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Transformation product (TP) information is essential to accurately evaluate the hazards compounds pose to human health and the environment. However, information about TPs is often limited, and existing data is often not fully Findable, Accessible, Interoperable, and Reusable (FAIR). FAIRifying existing TP knowledge is a relatively easy path toward improving access to data for identification workflows and for machine-learning-based algorithms. ShinyTPs was developed to curate existing transformation information derived from text-mined data within the PubChem database. The application (available as an R package) visualizes the text-mined chemical names to facilitate the user validation of the automatically extracted reactions. ShinyTPs was applied to a case study using 436 tentatively identified compounds to prioritize TP retrieval. This resulted in the extraction of 645 reactions (associated with 496 compounds), of which 319 were not previously available in PubChem. The curated reactions were added to the PubChem Transformations library, which was used as a TP suspect list for identification of TPs using the open-source workflow patRoon. In total, 72 compounds from the library were tentatively identified, 18% of which were curated using ShinyTPs, showing that the app can help support TP identification in non-target analysis workflows.
| S-EPMC10569035 | biostudies-literature
Project description:BackgroundThe biomedical literature continues to grow at a rapid pace, making the challenge of knowledge retrieval and extraction ever greater. Tools that provide a means to search and mine the full text of literature thus represent an important way by which the efficiency of these processes can be improved.ResultsWe describe the next generation of the Textpresso information retrieval system, Textpresso Central (TPC). TPC builds on the strengths of the original system by expanding the full text corpus to include the PubMed Central Open Access Subset (PMC OA), as well as the WormBase C. elegans bibliography. In addition, TPC allows users to create a customized corpus by uploading and processing documents of their choosing. TPC is UIMA compliant, to facilitate compatibility with external processing modules, and takes advantage of Lucene indexing and search technology for efficient handling of millions of full text documents. Like Textpresso, TPC searches can be performed using keywords and/or categories (semantically related groups of terms), but to provide better context for interpreting and validating queries, search results may now be viewed as highlighted passages in the context of full text. To facilitate biocuration efforts, TPC also allows users to select text spans from the full text and annotate them, create customized curation forms for any data type, and send resulting annotations to external curation databases. As an example of such a curation form, we describe integration of TPC with the Noctua curation tool developed by the Gene Ontology (GO) Consortium.ConclusionTextpresso Central is an online literature search and curation platform that enables biocurators and biomedical researchers to search and mine the full text of literature by integrating keyword and category searches with viewing search results in the context of the full text. It also allows users to create customized curation interfaces, use those interfaces to make annotations linked to supporting evidence statements, and then send those annotations to any database in the world. Textpresso Central URL: http://www.textpresso.org/tpc.
| S-EPMC5845379 | biostudies-literature
Project description:BackgroundHuman mitochondrial DNA has an important role in the cellular energy production through oxidative phosphorylation. Therefore, this process may be the cause and have an effect on mitochondrial DNA mutability, functional alteration, and disease onset related to a wide range of different clinical expressions and phenotypes. Although a large part of the observed variations is fixed in a population and hence expected to be benign, the estimation of the degree of the pathogenicity of any possible human mitochondrial DNA variant is clinically pivotal.MethodsIn this scenario, the establishment of standard criteria based on functional studies is required. In this context, a "data and text mining" pipeline is proposed here, developed using the programming language R, capable of extracting information regarding mitochondrial DNA functional studies and related clinical assessments from the literature, thus improving the annotation of human mitochondrial variants reported in the HmtVar database.ResultsThe data mining pipeline has produced a list of 1,073 Pubmed IDs (PMIDs) from which the text mining pipeline has retrieved information on 932 human mitochondrial variants regarding experimental validation and clinical features.ConclusionsThe application of the pipeline will contribute to supporting the interpretation of pathogenicity of human mitochondrial variants by facilitating diagnosis to clinicians and researchers faced with this task.
| S-EPMC7005629 | biostudies-literature
Project description:Large-scale genome sequencing has identified millions of protein-coding genes whose function is unknown. Many of these proteins are similar to characterized proteins from other organisms, but much of this information is missing from annotation databases and is hidden in the scientific literature. To make this information accessible, PaperBLAST uses EuropePMC to search the full text of scientific articles for references to genes. PaperBLAST also takes advantage of curated resources (Swiss-Prot, GeneRIF, and EcoCyc) that link protein sequences to scientific articles. PaperBLAST's database includes over 700,000 scientific articles that mention over 400,000 different proteins. Given a protein of interest, PaperBLAST quickly finds similar proteins that are discussed in the literature and presents snippets of text from relevant articles or from the curators. PaperBLAST is available at http://papers.genomics.lbl.gov/. IMPORTANCE With the recent explosion of genome sequencing data, there are now millions of uncharacterized proteins. If a scientist becomes interested in one of these proteins, it can be very difficult to find information as to its likely function. Often a protein whose sequence is similar, and which is likely to have a similar function, has been studied already, but this information is not available in any database. To help find articles about similar proteins, PaperBLAST searches the full text of scientific articles for protein identifiers or gene identifiers, and it links these articles to protein sequences. Then, given a protein of interest, it can quickly find similar proteins in its database by using standard software (BLAST), and it can show snippets of text from relevant papers. We hope that PaperBLAST will make it easier for biologists to predict proteins' functions.
| S-EPMC5557654 | biostudies-literature
Project description:microRNAs (miRNAs) are essential gene regulators, and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications and developing new hypotheses built on previous knowledge. Here, we present extracting miRNA Information from Text (emiRIT), a text-miningbased resource, which presents miRNA information mined from the literature through a user-friendly interface. We collected 149 ,233 miRNA -PubMed ID pairs from Medline between January 1997 and May 2020. emiRIT currently contains 'miRNA -gene regulation' (69 ,152 relations), 'miRNA disease (cancer)' (12 ,300 relations), 'miRNA -biological process and pathways' (23, 390 relations) and circulatory 'miRNAs in extracellular locations' (3782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text-mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource's information coverage, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of cancer and a third case study to assess the usage of emiRIT in the curation of miRNA information. Database URL: https://research.bioinformatics.udel.edu/emirit/.
| S-EPMC8163238 | biostudies-literature
Project description:BackgroundThe amount of published full-text articles has increased dramatically. Text mining tools configure an essential approach to building biological networks, updating databases and providing annotation for new pathways. PESCADOR is an online web server based on LAITOR and NLProt text mining tools, which retrieves protein-protein co-occurrences in a tabular-based format, adding a network schema. Here we present an HPC-oriented version of PESCADOR's native text mining tool, renamed to LAITOR4HPC, aiming to access an unlimited abstract amount in a short time to enrich available networks, build new ones and possibly highlight whether fields of research have been exhaustively studied.ResultsBy taking advantage of parallel computing HPC infrastructure, the full collection of MEDLINE abstracts available until June 2017 was analyzed in a shorter period (6 days) when compared to the original online implementation (with an estimated 2 years to run the same data). Additionally, three case studies were presented to illustrate LAITOR4HPC usage possibilities. The first case study targeted soybean and was used to retrieve an overview of published co-occurrences in a single organism, retrieving 15,788 proteins in 7894 co-occurrences. In the second case study, a target gene family was searched in many organisms, by analyzing 15 species under biotic stress. Most co-occurrences regarded Arabidopsis thaliana and Zea mays. The third case study concerned the construction and enrichment of an available pathway. Choosing A. thaliana for further analysis, the defensin pathway was enriched, showing additional signaling and regulation molecules, and how they respond to each other in the modulation of this complex plant defense response.ConclusionsLAITOR4HPC can be used for an efficient text mining based construction of biological networks derived from big data sources, such as MEDLINE abstracts. Time consumption and data input limitations will depend on the available resources at the HPC facility. LAITOR4HPC enables enough flexibility for different approaches and data amounts targeted to an organism, a subject, or a specific pathway. Additionally, it can deliver comprehensive results where interactions are classified into four types, according to their reliability.
| S-EPMC7447576 | biostudies-literature
Project description:Immunopeptidomics has made tremendous contributions to our understanding of antigen processing and presentation, by identifying and quantifying antigenic peptides presented on the cell surface by Major Histocompatibility Complex (MHC) molecules. Large and complex immunopeptidomics datasets can now be routinely generated using Liquid Chromatography-Mass Spectrometry techniques. The analysis of this data - often consisting of multiple replicates/conditions - rarely follows a standard data processing pipeline, hindering the reproducibility and depth of analysis of immunopeptidomic data. Here, we present Immunolyser, an automated pipeline designed to facilitate computational analysis of immunopeptidomic data with a minimal initial setup. Immunolyser brings together routine analyses, including peptide length distribution, peptide motif analysis, sequence clustering, peptide-MHC binding affinity prediction, and source protein analysis. Immunolyser provides a user-friendly and interactive interface via its webserver and is freely available for academic purposes at https://immunolyser.erc.monash.edu/. The open-access source code can be downloaded at our GitHub repository: https://github.com/prmunday/Immunolyser. We anticipate that Immunolyser will serve as a prominent computational pipeline to facilitate effortless and reproducible analysis of immunopeptidomic data.
| S-EPMC9988424 | biostudies-literature
Project description:BackgroundPhenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text.MethodsTo stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013.ResultsEvaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set.ConclusionsPhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus.
| S-EPMC4474585 | biostudies-literature
Project description:BackgroundFor ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles). So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations) from textual sources of any kind.ResultsEnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved) of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings.Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude), thus allowing the calculation of distance between the individual locations.ConclusionEnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical variables of sampling sites, thus facilitating the performance of ecological analyses. EnvMine can also help in the development of standards for the annotation of environmental features.
| S-EPMC2901371 | biostudies-literature
Project description:The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.
| S-EPMC10269341 | biostudies-literature