scholarly journals HubMed: a web-based biomedical literature search interface

2006 ◽  
Vol 34 (Web Server) ◽  
pp. W745-W747 ◽  
Author(s):  
A. D. Eaton
Keyword(s):  
Literature Search ◽  
Biomedical Literature ◽  
Search Interface ◽  
Web Based

scholarly journals variant2literature: full text literature search for genetic variants

2019 ◽  
Author(s):  
Yin-Hung Lin ◽  
Yu-Chen Lu ◽  
Ting-Fu Chen ◽  
Jacob Shujui Hsu ◽  
Ko-Han Lee ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Literature Search ◽  
Recall Rate ◽  
Biomedical Literature ◽  
Text Recognition ◽  
Command Line ◽  
False Negatives ◽  
Web Based ◽  
File Formats ◽  
Generation Sequencing

AbstractMotivationWhole genome sequencing (WGS) by next-generation sequencing produces millions of variants for an individual. The retrieval of biomedical literature for such a large number of genetic variants remains challenging, because in many cases the variants are only present in tables as images, or in the supplementary documents of which the file formats are diverse.ResultsThe proposed tool named variant2literature from the TaiGenomics (Toolkits for AI genomics) resolves the problem by incorporating text recognition with image processing. In addition to the adoption of advanced image-based text retrieval, the recall rate of finding the literature containing the variants of interest is further improved by employing the skill of variant normalization. Different variant presentations are transformed into chromosome coordinates (standard VCF format) such that false negatives can be largely avoided. variant2literature is available in two ways. First, a web-based interface is provided to search all the literature in PMC Open Access Subset. Second, the command-line executable can be downloaded such that the users are free to search all the files in a specified directory locally.Availabilityhttp://variant2literature.taigenomics.com/[email protected]

scholarly journals Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Peter Brown ◽  
Aik-Choon Tan ◽  
Mohamed A El-Esawi ◽  
Thomas Liehr ◽  
Oliver Blanck ◽  
...  
Keyword(s):  
Literature Search ◽  
Relevant Literature ◽  
Biomedical Literature ◽  
Medical Subject Headings ◽  
Document Similarity ◽  
Inverse Document Frequency ◽  
Research Fields ◽  
Experience Levels ◽  
Document Frequency ◽  
Systematic Biases

Abstract Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.

scholarly journals PubMed Labs: an experimental system for improving biomedical literature search

Database ◽  
2018 ◽  
Vol 2018 ◽  
Author(s):  
Nicolas Fiorini ◽  
Kathi Canese ◽  
Rostyslav Bryzgunov ◽  
Ievgeniia Radetska ◽  
Asta Gindulyte ◽  
...  
Keyword(s):  
Literature Search ◽  
Experimental System ◽  
Biomedical Literature

scholarly journals TeamTat: a collaborative text annotation tool

2020 ◽  
Vol 48 (W1) ◽  
pp. W5-W11
Author(s):  
Rezarta Islamaj ◽  
Dongseop Kwon ◽  
Sun Kim ◽  
Zhiyong Lu
Keyword(s):  
Project Managers ◽  
Biomedical Literature ◽  
Task Completion ◽  
Annotation Tool ◽  
Web Based ◽  
Domain Experts ◽  
Plain Text ◽  
Text Annotation ◽  
User Friendly ◽  
Input Format

Abstract Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.

scholarly journals A Web-Based platform to support a child cohort: The Italian NASCITA (Preprint)

2020 ◽  
Author(s):  
Michele Zanetti ◽  
Antonio Clavenna ◽  
Chiara Pandolfini ◽  
Claudia Pansieri ◽  
Maria Grazia Calati ◽  
...  
Keyword(s):  
Health Status ◽  
National Level ◽  
Biomedical Literature ◽  
Accurate Data ◽  
Data Input ◽  
Birth Cohorts ◽  
Web Portal ◽  
Formula Milk ◽  
Web Based ◽  
Set Up

BACKGROUND Many diseases occurring in adults can be pinned down to early childhood and birth cohorts are the optimal means to study this connection. Birth cohorts have contributed to the understanding of many diseases and their risk factors. OBJECTIVE To improve the knowledge of the health status of Italian children early on and how it is affected by social and health determinants, we set up a longitudinal, prospective, national level, population-based birth cohort, the NASCITA study (NAscere e creSCere in ITAlia). The main aim of this cohort is to evaluate physical, cognitive, and psychological development, health status, and health resource use in the first six years of life in newborns, and potential associated factors. A web-based system was set up with the aim to host the cohort, provide ongoing information to pediatricians and to families, and facilitate accurate data input, monitoring, and analysis. This article describes the informatics methodology used to set up and maintain the NASCITA cohort with its web-based platform, and provides a general description of the cohort data at six months. METHODS Family pediatricians were contacted for participation in the cohort and began enrolling newborns in April 2019 at their first well-child visit. Information collected involves basic data that are part of those routinely collected by the family pediatricians, but also parental data, such as medical history, characteristics and lifestyle, and indoor and outdoor environment. A specific web portal for the NASCITA cohort study was developed and an electronic case report form for data input was created and tested. Interactive data charts, including growth curves, are being made available to pediatricians with their patients’ data. Newsletters covering the current biomedical literature on child cohorts are periodically being put up for pediatricians, and, for parents, evidence-based information on common illnesses and problems in children. RESULTS After 6 months, there were 160 participating pediatricians, distributed throughout Italy, and 2264 enrolled children, representing 24% of children born in 2018 covered by those same pediatricians. At the second routine visit (programmed in the first 60-90 days of life), 59% of the children were still being exclusively breastfed. Of the mothers who were no longer exclusively breastfeeding, a majority (59%) were giving formula milk and 41% breast and formula milk. CONCLUSIONS The NASCITA Cohort is well underway and the current population size will already permit significant conclusions to be drawn. The key role of pediatricians in obtaining clinical data directly, along with the national level representativity, will make the findings even more solid. In addition to promoting accurate data input, the multiple functions of the web portal, with its interactive platform, help maintain a solid relationship with the pediatricians and keep parents informed and interested in participating. CLINICALTRIAL ClinicalTrials.gov NCT03894566

CTGA: Graph-based Biomedical Literature Search

2019 ◽  
Author(s):  
Tianwen Jiang ◽  
Zhihan Zhang ◽  
Tong Zhao ◽  
Bing Qin ◽  
Ting Liu ◽  
...  
Keyword(s):  
Literature Search ◽  
Biomedical Literature

scholarly journals Better synonyms for enriching biomedical search

2020 ◽  
Vol 27 (12) ◽  
pp. 1894-1902 ◽  
Author(s):  
Lana Yeganova ◽  
Sun Kim ◽  
Qingyu Chen ◽  
Grigory Balasanov ◽  
W John Wilbur ◽  
...  
Keyword(s):  
Gold Standard ◽  
Literature Search ◽  
Similarity Score ◽  
Isotonic Regression ◽  
Biomedical Literature ◽  
Cosine Similarity ◽  
Experimental Results ◽  
Word Embeddings ◽  
Embedding Method ◽  
Embedding Methods

Abstract Objective In a biomedical literature search, the link between a query and a document is often not established, because they use different terms to refer to the same concept. Distributional word embeddings are frequently used for detecting related words by computing the cosine similarity between them. However, previous research has not established either the best embedding methods for detecting synonyms among related word pairs or how effective such methods may be. Materials and Methods In this study, we first create the BioSearchSyn set, a manually annotated set of synonyms, to assess and compare 3 widely used word-embedding methods (word2vec, fastText, and GloVe) in their ability to detect synonyms among related pairs of words. We demonstrate the shortcomings of the cosine similarity score between word embeddings for this task: the same scores have very different meanings for the different methods. To address the problem, we propose utilizing pool adjacent violators (PAV), an isotonic regression algorithm, to transform a cosine similarity into a probability of 2 words being synonyms. Results Experimental results using the BioSearchSyn set as a gold standard reveal which embedding methods have the best performance in identifying synonym pairs. The BioSearchSyn set also allows converting cosine similarity scores into probabilities, which provides a uniform interpretation of the synonymy score over different methods. Conclusions We introduced the BioSearchSyn corpus of 1000 term pairs, which allowed us to identify the best embedding method for detecting synonymy for biomedical search. Using the proposed method, we created PubTermVariants2.0: a large, automatically extracted set of synonym pairs that have augmented PubMed searches since the spring of 2019.

scholarly journals LitSense: making sense of biomedical literature at sentence level

2019 ◽  
Vol 47 (W1) ◽  
pp. W594-W599 ◽  
Author(s):  
Alexis Allot ◽  
Qingyu Chen ◽  
Sun Kim ◽  
Roberto Vera Alvarez ◽  
Donald C Comeau ◽  
...  
Keyword(s):  
Biomedical Literature ◽  
Publication Date ◽  
Routine Practice ◽  
Term Weighting ◽  
Web Based ◽  
Making Sense ◽  
User Query ◽  
Sentence Level ◽  
New Findings ◽  
User Friendly

AbstractLiterature search is a routine practice for scientific studies as new discoveries build on knowledge from the past. Current tools (e.g. PubMed, PubMed Central), however, generally require significant effort in query formulation and optimization (especially in searching the full-length articles) and do not allow direct retrieval of specific statements, which is key for tasks such as comparing/validating new findings with previous knowledge and performing evidence attribution in biocuration. Thus, we introduce LitSense, which is the first web-based system that specializes in sentence retrieval for biomedical literature. LitSense provides unified access to PubMed and PMC content with over a half-billion sentences in total. Given a query, LitSense returns best-matching sentences using both a traditional term-weighting approach that up-weights sentences that contain more of the rare terms in the user query as well as a novel neural embedding approach that enables the retrieval of semantically relevant results without explicit keyword match. LitSense provides a user-friendly interface that assists its users to quickly browse the returned sentences in context and/or further filter search results by section or publication date. LitSense also employs PubTator to highlight biomedical entities (e.g. gene/proteins) in the sentences for better result visualization. LitSense is freely available at https://www.ncbi.nlm.nih.gov/research/litsense.

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