Finding the evidence for protein-protein interactions from PubMed abstracts

MOTIVATION: Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers. RESULTS: As a result of a search for two proteins, PubMed frequently returns hundreds of abstracts. In this paper, a method is introduced that validates protein-protein interactions from PubMed abstracts. A query is generated from two given proteins automatically and abstracts are then collected from PubMed. Following this, target proteins and their synonyms are recognized and their interaction information is extracted from the collection. It was found that 67.37% of the interactions from DIP-PPI corpus were found from the PubMed abstracts and 87.37% of interactions were found from the given full texts. AVAILABILITY: Contact authors.     

AliBaba: PubMed as a graph

The biomedical literature contains a wealth of information on associations between many different types of objects, such as protein-protein interactions, gene-disease associations and subcellular locations of proteins. When searching such information using conventional search engines, e.g. PubMed, users see the data only one-abstract at a time and 'hidden' in natural language text. AliBaba is an interactive tool for graphical summarization of search results. It parses the set of abstracts that fit a PubMed query and presents extracted information on biomedical objects and their relationships as a graphical network. AliBaba extracts associations between cells, diseases, drugs, proteins, species and tissues. Several filter options allow for a more focused search. Thus, researchers can grasp complex networks described in various articles at a glance. AVAILABILITY: http://alibaba.informatik.hu-berlin.de/     

Impact of PubMed search filters on the retrieval of evidence by physicians

Background:

Physicians face challenges when searching PubMed for research evidence, and they may miss relevant articles while retrieving too many nonrelevant articles. We investigated whether the use of search filters in PubMed improves searching by physicians.

Methods:

We asked a random sample of Canadian nephrologists to answer unique clinical questions derived from 100 systematic reviews of renal therapy. Physicians provided the search terms that they would type into PubMed to locate articles to answer these questions. We entered the physician-provided search terms into PubMed and applied two types of search filters alone or in combination: a methods-based filter designed to identify high-quality studies about treatment (clinical queries “therapy”) and a topic-based filter designed to identify studies with renal content. We evaluated the comprehensiveness (proportion of relevant articles found) and efficiency (ratio of relevant to nonrelevant articles) of the filtered and nonfiltered searches. Primary studies included in the systematic reviews served as the reference standard for relevant articles.

Results:

The average physician-provided search terms retrieved 46% of the relevant articles, while 6% of the retrieved articles were nonrelevant (the ratio of relevant to nonrelevant articles was 1:16). The use of both filters together produced a marked improvement in efficiency, resulting in a ratio of relevant to nonrelevant articles of 1:5 (16 percentage point improvement; 99% confidence interval 9% to 22%; p < 0.003) with no substantive change in comprehensiveness (44% of relevant articles found; p = 0.55).

Interpretation:

The use of PubMed search filters improves the efficiency of physician searches. Improved search performance may enhance the transfer of research into practice and improve patient care.Retrieving health literature is a cornerstone of evidence-based practice. With the rapid increase in available evidence, physicians can no longer rely on one or two key journals to stay current. Increasingly, physicians search bibliographic databases, such as PubMed, for research evidence, which is dispersed across hundreds of journals. Each year, physicians perform over 200 million searches in PubMed.^1,2 Physicians face challenges while searching PubMed and often miss relevant articles while retrieving too many nonrelevant articles.^3–6 Clinical decision-making based on evidence from a search may be impaired if relevant articles are missed. Retrieving many nonrelevant articles impedes the efficiency of searching. Improved search strategies are therefore necessary to retrieve a manageable amount of information. The use of PubMed search filters may help solve this problem. Filters are objectively derived, pretested strategies optimized to help users efficiently retrieve articles for a specific purpose.^7,8PubMed provides two types of clinical search filters: methods-based and topic-based. Methods-based filters (known as clinical queries) were designed to retrieve articles on therapy, diagnosis, prognosis and etiology.^9–13 For example, the clinical queries “therapy” filter is optimized to retrieve publications of randomized controlled trials. Methods-based filters can be used for any clinical discipline and are available for general use in PubMed (www.ncbi.nlm.nih.gov/pubmed/clinical). Topic-based filters, in contrast, are designed to retrieve articles within a specific discipline or topic. For example, the recently developed nephrology filters were optimized to retrieve articles with renal content.¹Physicians can use methods- and topic-based filters alone or in combination. For example, Figure 1A shows a search without search filters for studies about the effectiveness of hepatitis B vaccination in patients with chronic kidney disease. Alternatively, this search could be performed with search filters (Figure 1B). Using filters removes the task of generating and including method-specific or topic-specific terms in a search strategy because the filters act as optimized substitutes. For example, applying the nephrology filter eliminates the need to enter renal terms and synonyms in a search query (e.g., chronic kidney disease, end-stage renal disease, chronic renal failure). The nephrology filter, instead, maximizes the retrieval of all renal content (see the nephrology filter strategy in Figure 1B).Open in a separate windowFigure 1:PubMed searches without (A) and with (B) filters. This figure was created from the PubMed clinical queries Web interface; this page currently does not feature a “clinical category” section. When we performed searches with the nephrology filter (B), we removed the term “chronic kidney disease” because the filter acts as an optimized substitute for clinical content terms.In theory, filters should make searching more efficient; however, empiric evidence of this among physicians is lacking. We conducted this study to determine whether the use of methods-based filters and topic-based filters (alone and in combination) improve the efficiency of physician searches in PubMed. The area of renal medicine is an excellent test model because the literature in this field is dispersed across 400 multidisciplinary journals, and many nephrologists search PubMed for information to guide patient care.^14,15     

GAPscreener: An automatic tool for screening human genetic association literature in PubMed using the support vector machine technique

Background  

Synthesis of data from published human genetic association studies is a critical step in the translation of human genome discoveries into health applications. Although genetic association studies account for a substantial proportion of the abstracts in PubMed, identifying them with standard queries is not always accurate or efficient. Further automating the literature-screening process can reduce the burden of a labor-intensive and time-consuming traditional literature search. The Support Vector Machine (SVM), a well-established machine learning technique, has been successful in classifying text, including biomedical literature. The GAPscreener, a free SVM-based software tool, can be used to assist in screening PubMed abstracts for human genetic association studies.     

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research.

Availability

http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx     

Tracker: continuous HMMER and BLAST searching

SUMMARY: Tracker is a web-based email alert system for monitoring protein database searches using HMMER and Blast-P, nucleotide searches using Blast-N and literature searches of the PubMed database. Users submit searches via a web-based interface. Searches are saved and run against updated databases to alert users about new information. If there are new results from the saved searches, users will be notified by email and will then be able to access results and link to additional information on the NCBI website. Tracker supports Boolean AND/OR operations on HMMER and BLASTP result sets to allow users to broaden or narrow protein searches. AVAILABILITY: The server is located at http://jay.bioinformatics.ku.edu/tracker/index.html. A distribution package including detailed installation procedure is freely available from http://jay.bioinformatics.ku.edu/download/tracker/.     

Retrieval with gene queries

Background  

Accuracy of document retrieval from MEDLINE for gene queries is crucially important for many applications in bioinformatics. We explore five information retrieval-based methods to rank documents retrieved by PubMed gene queries for the human genome. The aim is to rank relevant documents higher in the retrieved list. We address the special challenges faced due to ambiguity in gene nomenclature: gene terms that refer to multiple genes, gene terms that are also English words, and gene terms that have other biological meanings.     

MineBlast: a literature presentation service supporting protein annotation by data mining of BLAST results

MineBlast is a web service for literature search and presentation based on data-mining results received from UniProt. Users can submit a simple list of protein sequences via a web-based interface. MineBlast performs a BLASTP search in UniProt to identify names and synonyms based on homologous proteins and subsequently queries PubMed, using combined search terms inorder to find and present relevant literature.     

MedKit: a helper toolkit for automatic mining of MEDLINE/PubMed citations

SUMMARY: MEDLINE/PubMed is one of the most important information sources for bioinformatics text mining. However, there remain limitations in working with MEDLINE/PubMed citations. For example, PubMed imposes an upper limit of 10,000 for downloading PMID list or citations; and MEDLINE files are too large for most off-the-shelf XML parsers. We developed a Java package, MedKit, to work-around the limitations, as well as provide other useful functionalities, e.g. random sampling. Its four modules (querier, sampler, fetcher and parser) can work independently, or be pipelined in various combinations. It can be used as a stand-alone GUI application, or integrated into other text-mining systems. Text mining researchers and others may download and use the toolkit free for non-commercial purposes. AVAILABILITY: http://metnetdb.gdcb.iastate.edu/medkit CONTACT: berleant@iastate.edu.     

Bibliography search in biology and medicine: a tutorial for Medline PubMed use

To be informed about the last publications recently published or to produce a bibliography in a given thematic field is essential for researchers in the biomedical field. If the use of Internet information searching tools such as "Google" or "Alltheweb" makes possible to discover a great part of the grey literature, bibliographic databases like Embase, Current Contents, Biosis or Medline via PubMed are essential tools to locate scientific articles. Among these bibliographic databases, Medline PubMed, thanks to its free access, is the most used. However, a correct utilization of the various functionalities proposed (thesaurus MeSH, systematization of bibliographic searches...), and consequently the quality of bibliographic researches carried out in this database, requires to master elementary knowledge which are exposed in this article.     

The Ontology Lookup Service, a lightweight cross-platform tool for controlled vocabulary queries

Background  

With the vast amounts of biomedical data being generated by high-throughput analysis methods, controlled vocabularies and ontologies are becoming increasingly important to annotate units of information for ease of search and retrieval. Each scientific community tends to create its own locally available ontology. The interfaces to query these ontologies tend to vary from group to group. We saw the need for a centralized location to perform controlled vocabulary queries that would offer both a lightweight web-accessible user interface as well as a consistent, unified SOAP interface for automated queries.     

A tool for gene expression based PubMed search through combining data sources

SUMMARY: We present a new tool for the semi-automated querying of PubMed using a batch of tens to thousands of GenBank accession numbers or UniGene cluster ids. By combining information from UniGene and SWISS-PROT, microGENIE obtains information on the biological relevance of expressed genes, as identified by micro-array experiments, with minimal user intervention and time investment. AVAILABILITY: microGENIE is freely available from http://www.cs.vu.nl/microgenie SUPPLEMENTARY INFORMATION: The web site above supplies examples of input and output files.     

GlycoSuiteDB: a new curated relational database of glycoprotein glycan structures and their biological sources

GlycoSuiteDB is a relational database that curates information from the scientific literature on glyco-protein derived glycan structures, their biological sources, the references in which the glycan was described and the methods used to determine the glycan structure. To date, the database includes most published O:-linked oligosaccharides from the last 50 years and most N:-linked oligosaccharides that were published in the 1990s. For each structure, information is available concerning the glycan type, linkage and anomeric configuration, mass and composition. Detailed information is also provided on native and recombinant sources, including tissue and/or cell type, cell line, strain and disease state. Where known, the proteins to which the glycan structures are attached are reported, and cross-references to the SWISS-PROT/TrEMBL protein sequence databases are given if applicable. The GlycoSuiteDB annotations include literature references which are linked to PubMed, and detailed information on the methods used to determine each glycan structure are noted to help the user assess the quality of the structural assignment. GlycoSuiteDB has a user-friendly web interface which allows the researcher to query the database using mono-isotopic or average mass, monosaccharide composition, glycosylation linkages (e.g. N:- or O:-linked), reducing terminal sugar, attached protein, taxonomy, tissue or cell type and GlycoSuiteDB accession number. Advanced queries using combinations of these parameters are also possible. GlycoSuiteDB can be accessed on the web at http://www.glycosuite.com.     

To compare PubMed Clinical Queries and UpToDate in teaching information mastery to clinical residents: a crossover randomized controlled trial

Purpose

To compare PubMed Clinical Queries and UpToDate regarding the amount and speed of information retrieval and users'' satisfaction.

Method

A cross-over randomized trial was conducted in February 2009 in Tehran University of Medical Sciences that included 44 year-one or two residents who participated in an information mastery workshop. A one-hour lecture on the principles of information mastery was organized followed by self learning slide shows before using each database. Subsequently, participants were randomly assigned to answer 2 clinical scenarios using either UpToDate or PubMed Clinical Queries then crossed to use the other database to answer 2 different clinical scenarios. The proportion of relevantly answered clinical scenarios, time to answer retrieval, and users'' satisfaction were measured in each database.

Results

Based on intention-to-treat analysis, participants retrieved the answer of 67 (76%) questions using UpToDate and 38 (43%) questions using PubMed Clinical Queries (P<0.001). The median time to answer retrieval was 17 min (95% CI: 16 to 18) using UpToDate compared to 29 min (95% CI: 26 to 32) using PubMed Clinical Queries (P<0.001). The satisfaction with the accuracy of retrieved answers, interaction with UpToDate and also overall satisfaction were higher among UpToDate users compared to PubMed Clinical Queries users (P<0.001).

Conclusions

For first time users, using UpToDate compared to Pubmed Clinical Querries can lead to not only a higher proportion of relevant answer retrieval within a shorter time, but also a higher users'' satisfaction. So, addition of tutoring pre-appraised sources such as UpToDate to the information mastery curricula seems to be highly efficient.     

Clustering of PubMed abstracts using nearer terms of the domain

Literature search is a process in which external developers provide alternative representations for efficient data mining of biomedical literature such as ranking search results, displaying summarized knowledge of semantics and clustering results into topics. In clustering search results, prominent vocabularies, such as GO (Gene Ontology), MeSH(Medical Subject Headings) and frequent terms extracted from retrieved PubMed abstracts have been used as topics for grouping. In this study, we have proposed FNeTD (Frequent Nearer Terms of the Domain) method for PubMed abstracts clustering. This is achieved through a two-step process viz; i) identifying frequent words or phrases in the abstracts through the frequent multi-word extraction algorithm and ii) identifying nearer terms of the domain from the extracted frequent phrases using the nearest neighbors search. The efficiency of the clustering of PubMed abstracts using nearer terms of the domain was measured using F-score. The present study suggests that nearer terms of the domain can be used for clustering the search results.     

基于CNKI和PubMed数据库的双歧杆菌研究文献分析

目的 统计分析国内外已发表的与双歧杆菌相关的期刊文献,了解其中的研究热点与发展趋势,为相关科研工作者提供参考。方法 研究资料来源于CNKI和PubMed数据库,应用文献计量学的方法对两个数据库所收录的双歧杆菌相关文献进行分析。结果 截至2018年8月15日,CNKI和PubMed分别收录了9277和5130篇相关文献。在数量上,国内外对双歧杆菌的研究都从20世纪90年代开始快速增长。从学科分布和关键词来看,中外研究共同关注了消化道疾病和儿科学,同时中外研究侧重点又有明显的差异。国内研究机构参与发表在国际杂志上的文章数量较多,但是其中作为第一发表单位的文章数量偏少。结论 双歧杆菌相关的基础与应用研究还有许多需要深入和拓展的方面,研究者应当在相关领域进行实质性突破。     

Figure summarizer browser extensions for PubMed Central

SUMMARY: Figures in biomedical articles present visual evidence for research facts and help readers understand the article better. However, when figures are taken out of context, it is difficult to understand their content. We developed a summarization algorithm to summarize the content of figures and used it in our figure search engine (http://figuresearch.askhermes.org/). In this article, we report on the development of web browser extensions for Mozilla Firefox, Google Chrome and Apple Safari to display summaries for figures in PubMed Central and NCBI Images. AVAILABILITY: The extensions can be downloaded from http://figuresearch.askhermes.org/articlesearch/extensions.php.