Relation extraction software tools | Biomedication text mining
Rapidly evolving sequencing technologies have led to a dramatic rise in the number of published articles reporting associations between genomic variations and diseases. There is an estimate that over 10,000 articles are published each year mentioning such associations. Manually collecting this information is both expensive and time consuming. To assist this manual curation, several text-mining (TM) efforts have been attempted. However, most of these efforts are limited to identifying mutation mentions only. The majority utilize regular expressions to detect mutations, although there are some, like tmVar, that use conditional random fields (CRFs), and SETH, which implements an Extended Backus-Naur Form (EBNF) grammar. Only a few of these efforts extend the mutation detection method to associate the mutation with a disease phenotype. Most of these are search based TM tools that do not employ automatic extraction of the mutation-disease relationships expressed in articles.
Finds and allows visualization of indirect associations between biomedical concepts from MEDLINE abstracts. FACTA+ is a real-time text-mining system that can be used as a text search engine, like PubMed, with additional features to assist users in discovering and visualizing indirect associations between important biomedical concepts such as genes, diseases and chemical compounds.
A web-based text mining tool that extracts and incorporates comprehensive knowledge about E3s with their underlying mechanisms. E3Miner integrates available E3 data not only from the published literature but also from the biological databases, using natural language processing techniques.
A web-based NCBI-PubMed search application, which can analyze articles for selected biomedical verbs and give users relational information, such as subject, object, location, manner, time, etc. After receiving keyword query input, BWS retrieves matching PubMed abstracts and lists them along with snippets by order of relevancy to protein-protein interaction. Users can then select articles for further analysis, and BWS will find and mark up biomedical relations in the text. The analysis results can be viewed in the abstract text or in table form.
A syntax convolutional neural network (SCNN) based drug-drug interaction (DDI) extraction approach. In this approach, a novel word embedding (syntax word embedding) is proposed to exploit the syntactic information of a sentence. Then the syntax word embedding is extended by the position and POS features to introduce the position and POS information. In addition, auto-encoder is employed to transfer sparse bag-of-words feature vectors to dense real value feature vectors before they are combined with the convolutional features. Finally, their combination is passed to a softmax to learn the DDI classifier. Experimental results on the DDIExtraction 2013 corpus show that our method achieves an F-score of 0.686 which is superior to those of the state-of-the-art methods.
A free and user-friendly text annotation tool aimed to assist in carrying out the main biocuration tasks and to provide labelled data for the development of text mining systems. MyMiner allows easy classification and labelling of textual data according to user-specified classes as well as predefined biological entities.
Provides protein interaction information (PPI) articles for biologists, baseline system performance for bio-text mining researchers and a compact PubMed-search environment for PubMed users. For easy user access, PIE the search provides a PubMed-like search environment, but the output is the list of articles prioritized by PPI confidence scores. By obtaining PPI-related articles at high rank, researchers can more easily find the up-to-date PPI information, which cannot be found in manually curated PPI databases.
An extractor for gene-gene interactions that identified candidate gene-gene relations within an input sentence. For each candidate relation, DeepDive computed a probability that the relation was a correct interaction. DeepDive enables an improved and more precise under-standing of gene and protein interactions within the cell to further both experimental and computational research.
A framework for the automatic extraction of gene-disease relation from biomedical literature. DTMiner takes large biomedical literature repositories as inputs, identifies credible relationships between diseases and genes, and presents possible genes related to a given disease and possible diseases related to a given gene. DTMiner incorporates name entity recognition (NER), which identifies occurrences of genes and diseases in texts, association detection whereby we extract and evaluate features from gene-disease pairs, and ranking algorithms that estimate how closely the pairs are related.
A package to link chemical features and in vitro biological data with targeted in vivo biological activity. The CIIpro portal can automatically extract in vitro biological data from public resources for user-supplied compounds, and identify the most similar compounds based on their optimized bioprofiles. Compared to the existing hybrid approaches, the CIIPro portal provides a new read-across strategy to deal with missing data and biased data issues when using public data sources.
A machine-learning-based text-mining system for automated identification and extraction of microbial interaction data. @MInter complements manual annotation and curation through the identification of potentially informative texts, to accelerate the pace of data curation. It comprises of two core components, a crawler for the acquisition of article data and a classifier for the identification of abstracts containing interaction information. @MInter can be useful to mine large datasets such as the unstructured text compendiums of scientific literature.
Visualizes the association between proteins and diseases, based on text mining data processed from scientific literature. TIN-X provides an interactive visualization, ranking, and prioritization platform for scientists interested in exploring potentially novel drug targets, and examining the relationship between diseases, disease categories, proteins and protein classes, using automated text mining of biomedical literature. It cannot replace expert human readers and curators, yet, it is increasingly clear that automated bibliometry is essential given the accelerated pace and volume of publications.
Allows biomedical data management, analysis, and visualization. SATORI is a web-based exploration system that combines search with visual browsing to provide an integrated exploration experience. The visualizations serve two purposes: supporting the information foraging loop and pattern discovery of attribute distributions, as well as ontology-guided semantic querying of the data repository.
Constructs a de-identified corpus of medical message board (MMB) posts and then extracts information from it. Medpie is useful to collect and investigate MMBs and aims to simplify further research of online communities such as MMBs. It provides four distinct modules: web-crawling, HTML-cleaning, de-identification and information extraction. This tool gathers a controlled vocabulary of drug, dietary supplement and event terms.
Clusters analysis on variables, providing a unified similarity metric that can compute a score for all combinations of numerical, categorical and ordinal variables. Clustermatch retrieves hidden relationships by clustering each variable separately, and then computing how much those clusters match. It can include variables of very different nature and construct a similarity matrix that can be processed by any clustering algorithm.
A tool to support article selection and information extraction of functional impact of phosphorylated proteins. The current version focuses on protein-protein interactions (PPIs) as functional impact. In eFIP, PPIs refer to interactions between protein elements, including protein complexes and classes of proteins. Impact is defined as any direct relation between protein phosphorylation and PPI. The relation could be positive (phosphorylation of A increases binding to B), negative (when phosphorylated A dissociates from B) or neutral (phosphorylated A binds B).
Extracts microRNAs (miRNA)-target relations, miRNA-gene and gene-miRNA regulation relations embedded in individual sentences. miRTex is a text mining system which can be integrated into literature-based curation pipelines. 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.
Creates expert candidate gene lists. GLAD4U is a web-based gene retrieval and prioritization tool. It uses NCBI eSearch API to find publications related to a user query and on the gene-to-publication link table to identify genes from the retrieved publications. It also provides additional functionalities such as sending queries towards WebGestalt for analyzing functional enrichment, or provides a direct link to visualize interactions among the protein products of the genes based on the Cytoscape Web utility.
Identifies and retrieves chemical series from sets of molecules. CheTo permits users to interactively visualize the chemical topic model. It can discover hidden structure in molecule sets and find alternative relations between molecules. This tool main’s advantage is that the topic model belongs to the class of mixed-membership models and thereby enables a fuzzy clustering.
Detects and allows to semantically annotate results obtained from quantitative trait locus (QTL) mapping experiments. QTM extracts articles in a syntactically interopable format XML. This software filters candidate trait tables out of all tables in an article. It exploits the Apache Solr search platform to extract and annotate biological entities in these statements with domain-specific ontologies. This tool aims to provide QTL information in machine readable and semantically interopable formats.
Automatically extracts information from biological literature to build a topology of the plant defense signaling (PDS) model. Bio3graph is based on a domain specific vocabulary that is composed of two parts: a list of components and a list of reactions together with their synonyms. It is composed of a series of text mining, information extraction, graph construction and graph visualization steps, offering reusability, repeatability, and extension with additional components.
Offers a literature-based repository for discovery focusing on molecular biology and cancer. LION LBD allows users to navigate published information and supports hypothesis generation and testing. This database includes a selection of various co-occurrence-based metrics for analyzing the strength of entity associations. Its design allows real-time search to uncover indirect associations between entities in a source of up to tens of millions of publications.
A resource which aims to be the most comprehensive freely available database of disease–gene associations. DISEASES is based on an open-source text-mining software that recognizes diseases and human genes in text and extracts disease–gene associations. We integrate the associations extracted through automatic text mining with evidence from databases with permissive licenses, namely manually curated associations from Genetics Home Reference (GHR) and UniProt Knowledgebase (UniProtKB), GWAS results from DistiLD, and mutation data from Catalog of Somatic Mutations in Cancer (COSMIC).
A corpus that contains more than 400 variants and their relations with genes, diseases, drugs, and cell lines in the context of cancer and anti-tumor drug screening research. BRONCO can be utilized to evaluate and train new methods used for extracting biomedical entity relations from full-text publications, and thus be a valuable resource to the biomedical text mining research community.
A silver standard corpus for chemical-induced diseases (CID) relation extraction. Two novel aspects that makes othe SilverCID corpus different from other resources are (i) it was built automatically and (ii) it is a sentence-level corpus (i.e. a set of sentences that contains at least one intra-sentence CID relation with its participating chemical and disease entities), which covered about 60% of CID relations in the CTD database.
Allows semantic searching from the public literature sources. XTractor provides association information with reference to various biomedical entities. The metadata is linked to more than 20 external databases and provides outputs for more than 13 million relationships.
Supports development of advanced text-mining (TM) systems on gene-cancer relations. CoMAGC is a corpus that allows multi-faceted annotation with a structured format that can express gene and cancer evolution and the causality between the gene and the cancer. The proposed gene annotation system allows users to classify genes into oncogenes, tumor suppressor genes and biomarkers according to the prospective roles of genes in cancers.
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