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12 results for “medline”
Word embeddings learnt on MEDLINE abstracts
<p>Accompanying a preprint manuscript and code repository, this folder contains both raw text data and learnt word embeddings. The data source is the set of MEDLINE articles published on or after 2000. Preprocessing consists of extraction of each article's title and abstract and some minor text processing. The result is a corpus of 10.5 million documents in a single 14 GB file. </p> <p>word2vec and fastText are used to learn word embeddings on this corpus and three sets of word embeddings are shared here: 1) word2vec skip-gram, 2) word2vec CBOW, and 3) fastText skip-gram. All three sets use the default parameters of the software (e.g. context=5) with the exception of hierarchical softmax optimization and dimension=200.</p> <p>Preprint manuscript: https://arxiv.org/abs/1705.06262<br> GitHub repository: https://github.com/vincentmajor/ctsa_prediction</p>
Complete Medline abstracts corpus between 2015-2019 annotated Whatizit text annotation tool
<p><strong>Background: </strong></p> <p>Whatizit is a text processing system that allows you to do text-mining tasks on text. It is great at identifying molecular biology terms and linking them to publicly available databases. Identified terms are wrapped with XML tags that carry additional information, such as the primary keys to the databases where all the relevant information is kept. The wrapping XML is translated into HTML hypertext links. This service is highly appreciated by people who are reading literature and need to quickly find more information about a particular term, e.g. its Gene Ontology term.</p> <p>Whatizit is used in identifying formalized language patterns, specialized, syntactically formalized, technical notation. The annotation speed of a given pipeline is almost independent of the size of the vocabulary behind it and is currently based on pattern matching. In addition, several vocabularies can be integrated in a single pipeline.</p> <p><strong>Methodology:</strong></p> <p>The pipeline used is comprised of 175k Gene Ontology terms (preferred labels + synonyms).<br> The annotation on Medline 2015-2019 corpus is done with <em>Gene Ontology (GO)</em> integrated dictionary.</p> <p>The .zip file contains 10 XML files - each file is for half an year of MEDLINE annotated abstracts.<br> In addition to the abstract, the title is also annotated for further information enrichment.</p> <p>Respective DOIs, PMIDs are also included in the XML, when applicable.</p> <p><strong>Further development:</strong></p> <p>The XML files can be converted into JSON, JSON-LD format.</p> <p> </p> <p> </p>
MEDLINE
<p>MEDLINE (MED) a subset of the MedLine dataset, with 861,454 documents classified into 7 distinct classes related to Medicine. This dataset was obtained from [1]. In that work the authors considered the first level of the taxonomy so that each document article is classified under only one category, avoiding dealing with multilabel cases.</p> <p>[1] L. Rocha, F. Mourao, A. Pereira, M. A. Gon¸calves, and W. Meira, Jr. Exploiting temporal contexts in text classification. In Proc. CIKM, pages 243–252, 2008.</p> <p>The files:<br> texts.txt: Document set (text). One per line.<br> score.txt: Document class whose index is associated with texts.txt<br> split_<k>.pkl: pandas DataFrame with k-cross validation partition.</p> <p>The .zip contains all aforementioned files + the tfidf representation in the CSR matrix format.</p>
Medline Impactful Discoveries
<p>This dataset contains both the input and output data related to the experiments "Medline impactful discoveries".</p> <ul> <li>Paper: <em>pending</em></li> <li>Code and documentation: <a href="https://github.com/erwanm/medline-discoveries">https://github.com/erwanm/medline-discoveries</a></li> <li>Exploration tool: <a href="https://brainmend.adaptcentre.ie/">https://brainmend.adaptcentre.ie/</a></li> </ul> <p><strong>Important:</strong> the raw data from which this data is derived was downloaded from <a href="https://www.nlm.nih.gov/medline/medline_overview.html">Medline</a>, provided <a href="https://www.nlm.nih.gov/databases/download/terms_and_conditions.html">courtesy of the U.S. National Library of Medicine (NLM)</a>. The data was extracted in January 2021 and do not reflect the most current/accurate data available from NLM. In order to generate a similar dataset from up to date data, see the instructions in the repository mentioned above.</p> <p> </p>
PheneBank: Processed Medline Abstracts and PMC full articles + Phenotype-Disease Associations
<p><strong>The PheneBank project:</strong></p> <p>Free text scientific literature has the potential to be an incredibly valuable source of data for uncovering the often hidden relationships between genes, diseases and phenotypes. Phenotypic descriptions cover abnormalities in anatomical structures, processes and behaviours. For example 'growth delay' and 'body weight loss'. Such descriptions form the basis for determining the existence and treatment of a disease but, because of their inherent complexity, have previously received less attention by the text mining community. In recent years, significant effort has been spent by a small number of expert curators to create coding systems for phenotypes (called "ontologies"), such as the Human Phenotype Ontology (HP) and the Mammalian Phenotype Ontology (MP). The PheneBank project proposes to support and speed up curation using terms discovered directly from the literature and to automatically integrate them with such standard ontologies. <br> <br> The project seeks to harness texts for extracting statistically significant associations between phenotypes, diseases and genes. Earlier approaches have suffered from not providing deep semantic representations of the phenotypes they tried to target. Our deep learning-based approach is an attempt to overcome this issue by reducing the uncertainty between textual and ontological forms of phenotypes. Specifically, the model treats multitoken named entities as a single token which allows more reliable handling of multiword expressions. The approach builds on ground breaking research at the European Bininformatics Institute by the PI (Nigel Collier) and the Co-investigator (Damian Smedley, Queen Mary University London), including terminology alignment of phenotypes using pairwise scoring of the conceptual elements that make up the phenotype. </p> <p><a href="http://www.phenebank.org">http://www.phenebank.org</a></p> <p><br> <strong>The dataset:</strong></p> <p>As an output of the PheneBank project, we release the set of 24 million MEDLINE abstracts as well as 3.8M open-access PMC full articles annotated with 9 classes of entity: Phenotype, Disease, Anatomy, Cell, Cell_line, GPR, Gene_variant, Molecule, and Pathway. The entities have been mapped to five major ontologies: SNOMED, HPO, MeSH, PRO, and FMA.</p> <p>In addition, we release the phenotype-disease associations that are automatically extracted based on co-occurrences statistics in Medline abstracts. Among different statistical measures we evaluated, the Fisher test best corresponded to the known tuples available from the curated associations available from the Monarch Initiative (https://monarchinitiative.org).</p> <p><br> <strong>Processing:</strong></p> <p>The NER tagging has been done using a BiLSTM-CRF neural model (<a href="https://github.com/pilehvar/phenebank">https://github.com/pilehvar/phenebank</a>) trained on expert-annotated data (to be released for research). The grounding to ontologies relies on semantic embedding of concepts and entities in a unified semantic space.</p> <p><br> <strong>Data format:</strong></p> <p><strong>PheneBank_Processed_PubMed.part[x].tar.gz </strong>contains 24,359,010 .txt files that are classified into 812 directories. Each .txt file is named with a PubMed article ID and contains the corresponding article's abstract and its annotations. The dataset is split into four (unequal) parts based on PubMed's structure:<br> part1: medline16n00* medline16n01* medline16n02* [299 directories, 2.8GB]<br> part2: medline16n03* medline16n04* [200 directories, 4.7GB]<br> part3: medline16n05* medline16n06* [200 directories, 5.3GB]<br> part4: medline16n07* medline16n08* [113 directories, 3.1GB]</p> <p>The <strong>PheneBank_Processed_PMC.tar.gz</strong> files has 6,180 directories which are named after the journal titles from which the articles have been drawn. There are three files per each article (i.e., 3 .txt files for the 3,751,770 distinct articles), containing text from different parts of the article: .title.txt, .abstract.txt, and .body.txt. </p> <p>Each line starts with a word; for those words that are identified as entities, entity type and mapping information are followed in the same line (tab separated), with the following format:</p> <p>word <TAB> ::: <TAB> entity_type <TAB> entity_concept_ID_1##confidence_score_1 entity_concept_ID_2##confidence_score_2 ...</p> <p>Note that the concepts are sorted according to their mapping confidence scores.</p> <p><br> As for the <strong>PheneBank_Associations.tsv</strong> file, there are ten columns that correspond to the following (left to right):</p> <p>- Disease Name<br> - Disease (MONDO) ID<br> - Phenotype Name<br> - Phenotype (HPO) ID<br> - Co-occurrence Frequency<br> - Disease Frequency<br> - Phenotype Frequency<br> - Fisher (log)<br> - Dice<br> - Normalized PMI</p> <p> </p> <p> </p>
TopicTracker Medline files and query logs generated retrieving papers on autonomy, equity, privacy, proportionality and trust in the context of Covid-19
<p>To determine the core areas of discussion about the interplay between the Core Five and the Covid-19 pandemic, we ran a set of five queries in the TopicTracker. Each query collects articles regarding Covid-19 and one of the Core Five Enduring Values, published between January 2019 and March 2022.</p>
Search history, Cinahl, Medline, Scopus, Psycinfo, Embase
<p>Search history, Cinahl, Medline, Scopus, Psycinfo, Embase</p>
Semantic metadata annotation: tagging medline abstracts for enhanced information access
<p>The object of this study is to develop methods for automatically annotating the argumentative role of sentences in scientific abstracts. Working from Medline abstracts, we classified sentences into four major argumentative roles: objective, method, result, conclusion. The idea is that if the role of each sentence can be marked up, then this metadata can be used during information retrieval to seek for particular types of information such as novelty, conclusions, methodologies, aims/goals of a scientific piece of work.</p> <p> </p>
medline dataset
<p>ftp://ftp.nlm.nih.gov/nlmdata/sample/medline/</p>
SpO2 Accuracy Comparison of Medline ReNewal Sensors to Arterial Blood CO-Oximetry
ClinicalTrials.gov study NCT03565679. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Which are the most sensitive search filters to identify randomized controlled trials in MEDLINE?
<p>The raw data is attached for the paper: </p> <p>Which are the most sensitive search filters to identify randomized controlled trials in MEDLINE?</p>
WMT'16 Biomedical Translation Task - Medline parallel datasets
<p>Parallel data from Medline for the Biomedical Translation Task in the First Conference on Machine Translation (WMT 16) (http://www.statmt.org/wmt16/biomedical-translation-task.html).</p> <p>It contains parallel data for es/en, fr/en and pt/en.</p> <p>The documents were derived from the Medline/PubMed database (https://pubmed.ncbi.nlm.nih.gov/).</p>
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