PubMed Assistant: a biologist-friendly interface for enhanced PubMed search

MEDLINE is one of the most important bibliographical information sources for biologists and medical workers. Its PubMed interface supports Boolean queries, which are potentially expressive and exact. However, PubMed is also designed to support simplicity of use at the expense of query expressiveness and exactness. Many PubMed users have never tried explicit Boolean queries. We developed a Java program, PubMed Assistant, to make literature access easier in several ways. PubMed Assistant provides an interface that efficiently displays information about the citations and includes useful functions such as keyword highlighting, export to citation managers, clickable links to Google Scholar and others that are lacking in PubMed.     

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     

Open Access Meets Discoverability: Citations to Articles Posted to Academia.edu

Using matching and regression analyses, we measure the difference in citations between articles posted to Academia.edu and other articles from similar journals, controlling for field, impact factor, and other variables. Based on a sample size of 31,216 papers, we find that a paper in a median impact factor journal uploaded to Academia.edu receives 16% more citations after one year than a similar article not available online, 51% more citations after three years, and 69% after five years. We also found that articles also posted to Academia.edu had 58% more citations than articles only posted to other online venues, such as personal and departmental home pages, after five years.     

A quantitative model for linking two disparate sets of articles in MEDLINE

BACKGROUND: Identifying information that implicitly links two disparate sets of articles is a fundamental and intuitive data mining strategy that can help investigators address real scientific questions. The Arrowsmith two-node search finds title words and phrases (so-called B-terms) that are shared across two sets of articles within MEDLINE and displays them in a manner that facilitates human assessment. A serious stumbling-block has been the lack of a quantitative model for predicting which of the hundreds if not thousands of B-terms computed for a given search are most likely to be relevant to the investigator. METHODOLOGY/PRINCIPAL FINDINGS: Using a public two-node search interface, field testers devised a set of two-node searches under real life conditions and a certain number of B-terms were marked relevant. These were employed as 'gold standards;' each B-term was characterized according to eight complementary features that were strongly correlated with relevance. A logistic regression model was developed that permits one to estimate the probability of relevance for each B-term, to rank B-terms according to their likely relevance, and to estimate the overall number of relevant B-terms inherent in a given two-node search. CONCLUSIONS/SIGNIFICANCE: The model greatly simplifies and streamlines the process of carrying out a two-node search, and may be applicable to a number of other literature-based discovery applications, including the so-called one-node search and related gene-centric strategies that incorporate implicit links to predict how genes may be related to each other and to human diseases. This should encourage much wider exploration of text mining for implicit information among the general scientific community. AVAILABILITY: Two-node searches can be carried out freely at http://arrowsmith.psych.uic.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Déjà vu--a study of duplicate citations in Medline

MOTIVATION: Duplicate publication impacts the quality of the scientific corpus, has been difficult to detect, and studies this far have been limited in scope and size. Using text similarity searches, we were able to identify signatures of duplicate citations among a body of abstracts. RESULTS: A sample of 62,213 Medline citations was examined and a database of manually verified duplicate citations was created to study author publication behavior. We found that 0.04% of the citations with no shared authors were highly similar and are thus potential cases of plagiarism. 1.35% with shared authors were sufficiently similar to be considered a duplicate. Extrapolating, this would correspond to 3500 and 117,500 duplicate citations in total, respectively. Availability: eTBLAST, an automated citation matching tool, and Déjà vu, the duplicate citation database, are freely available at http://invention.swmed.edu/ and http://spore.swmed.edu/dejavu     

MedlineR: an open source library in R for Medline literature data mining

SUMMARY: We describe an open source library written in the R programming language for Medline literature data mining. This MedlineR library includes programs to query Medline through the NCBI PubMed database; to construct the co-occurrence matrix; and to visualize the network topology of query terms. The open source nature of this library allows users to extend it freely in the statistical programming language of R. To demonstrate its utility, we have built an application to analyze term-association by using only 10 lines of code. We provide MedlineR as a library foundation for bioinformaticians and statisticians to build more sophisticated literature data mining applications. AVAILABILITY: The library is available from http://dbsr.duke.edu/pub/MedlineR.     

Using Noun Phrases for Navigating Biomedical Literature on Pubmed: How Many Updates Are We Losing Track of?

Author-supplied citations are a fraction of the related literature for a paper. The "related citations" on PubMed is typically dozens or hundreds of results long, and does not offer hints why these results are related. Using noun phrases derived from the sentences of the paper, we show it is possible to more transparently navigate to PubMed updates through search terms that can associate a paper with its citations. The algorithm to generate these search terms involved automatically extracting noun phrases from the paper using natural language processing tools, and ranking them by the number of occurrences in the paper compared to the number of occurrences on the web. We define search queries having at least one instance of overlap between the author-supplied citations of the paper and the top 20 search results as citation validated (CV). When the overlapping citations were written by same authors as the paper itself, we define it as CV-S and different authors is defined as CV-D. For a systematic sample of 883 papers on PubMed Central, at least one of the search terms for 86% of the papers is CV-D versus 65% for the top 20 PubMed "related citations." We hypothesize these quantities computed for the 20 million papers on PubMed to differ within 5% of these percentages. Averaged across all 883 papers, 5 search terms are CV-D, and 10 search terms are CV-S, and 6 unique citations validate these searches. Potentially related literature uncovered by citation-validated searches (either CV-S or CV-D) are on the order of ten per paper--many more if the remaining searches that are not citation-validated are taken into account. The significance and relationship of each search result to the paper can only be vetted and explained by a researcher with knowledge of or interest in that paper.     

An online literature mining tool for protein phosphorylation

A web-based version of the RLIMS-P literature mining system was developed for online mining of protein phosphorylation information from MEDLINE abstracts. The online tool presents extracted phosphorylation objects (phosphorylated proteins, phosphorylation sites and protein kinases) in summary tables and full reports with evidence-tagged abstracts. The tool further allows mapping of phosphorylated proteins to protein entries in the UniProt Knowledgebase based on PubMed ID and/or protein name. The literature mining, coupled with database association, allows retrieval of rich biological information for the phosphorylated proteins and facilitates database annotation of phosphorylation features.     

Gene clustering by latent semantic indexing of MEDLINE abstracts

MOTIVATION: A major challenge in the interpretation of high-throughput genomic data is understanding the functional associations between genes. Previously, several approaches have been described to extract gene relationships from various biological databases using term-matching methods. However, more flexible automated methods are needed to identify functional relationships (both explicit and implicit) between genes from the biomedical literature. In this study, we explored the utility of Latent Semantic Indexing (LSI), a vector space model for information retrieval, to automatically identify conceptual gene relationships from titles and abstracts in MEDLINE citations. RESULTS: We found that LSI identified gene-to-gene and keyword-to-gene relationships with high average precision. In addition, LSI identified implicit gene relationships based on word usage patterns in the gene abstract documents. Finally, we demonstrate here that pairwise distances derived from the vector angles of gene abstract documents can be effectively used to functionally group genes by hierarchical clustering. Our results provide proof-of-principle that LSI is a robust automated method to elucidate both known (explicit) and unknown (implicit) gene relationships from the biomedical literature. These features make LSI particularly useful for the analysis of novel associations discovered in genomic experiments. AVAILABILITY: The 50-gene document collection used in this study can be interactively queried at http://shad.cs.utk.edu/sgo/sgo.html.     

Genetic susceptibility to chronic Chagas disease: an overview of single nucleotide polymorphisms of cytokine genes

Chagas disease is a parasitic infection that is a significant public health problem in Latin America. The mechanisms responsible for susceptibility to the infection and the mechanisms involved in the development of cardiac and digestive forms of chronic Chagas disease remain poorly understood. However, there is growing evidence that differences in susceptibility in endemic areas may be attributable to host genetic factors. The aim of this overview was to analyze the genetic susceptibility to human Chagas disease, particularly that of single nucleotide polymorphisms of cytokine genes. A review of the literature was conducted on the following databases: PubMed/MEDLINE and Scopus. The search strategy included using the following terms: "Cytokines", "Single Nucleotide Polymorphisms" and "Chagas Disease". After screening 25 citations from the databases, 19 studies were selected for the overview. A critical analysis of the data presented in the articles suggests that genetic susceptibility to Chagas disease and chronic Chagas cardiomyopathy is highly influenced by the complexity of the immune response of the host. Follow-up studies based on other populations where Chagas disease is endemic (with distinct ethnic and genetic backgrounds) need to be conducted. These should use a large sample population so as to establish what cytokine genes are involved in susceptibility to and/or progression of the disease.     

MedEvi: retrieving textual evidence of relations between biomedical concepts from Medline

Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results. AVAILABILITY: http://www.ebi.ac.uk/tc-test/textmining/medevi/     

ADAM: another database of abbreviations in MEDLINE

MOTIVATION: Abbreviations are an important type of terminology in the biomedical domain. Although several groups have already created databases of biomedical abbreviations, these are either not public, or are not comprehensive, or focus exclusively on acronym-type abbreviations. We have created another abbreviation database, ADAM, which covers commonly used abbreviations and their definitions (or long-forms) within MEDLINE titles and abstracts, including both acronym and non-acronym abbreviations. RESULTS: A model of recognizing abbreviations and their long-forms from titles and abstracts of MEDLINE (2006 baseline) was employed. After grouping morphological variants, 59 405 abbreviation/long-form pairs were identified. ADAM shows high precision (97.4%) and includes most of the frequently used abbreviations contained in the Unified Medical Language System (UMLS) Lexicon and the Stanford Abbreviation Database. Conversely, one-third of abbreviations in ADAM are novel insofar as they are not included in either database. About 19% of the novel abbreviations are non-acronym-type and these cover at least seven different types of short-form/long-form pairs. AVAILABILITY: A free, public query interface to ADAM is available at http://arrowsmith.psych.uic.edu, and the entire database can be downloaded as a text file.     

NetiNeti: discovery of scientific names from text using machine learning methods

ABSTRACT: BACKGROUND: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. RESULTS: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9 % and recall = 70.5 %) compared to a popular dictionary based approach (precision = 97.5 % and recall = 54.3 %) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5 % and 96.2 % respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Additionally, we present the comparison results of various machine learning algorithms on our annotated corpus. Naive Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms. CONCLUSIONS: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org.     

The RESID Database of protein structure modifications and the NRL-3D Sequence-Structure Database

The RESID Database is a comprehensive collection of annotations and structures for protein post-translational modifications including N-terminal, C-terminal and peptide chain cross-link modifications. The RESID Database includes systematic and frequently observed alternate names, Chemical Abstracts Service registry numbers, atomic formulas and weights, enzyme activities, taxonomic range, keywords, literature citations with database cross-references, structural diagrams and molecular models. The NRL-3D Sequence-Structure Database is derived from the three-dimensional structure of proteins deposited with the Research Collaboratory for Structural Bioinformatics Protein Data Bank. The NRL-3D Database includes standardized and frequently observed alternate names, sources, keywords, literature citations, experimental conditions and searchable sequences from model coordinates. These databases are freely accessible through the National Cancer Institute-Frederick Advanced Biomedical Computing Center at these web sites: http://www. ncifcrf.gov/RESID, http://www.ncifcrf.gov/NRL-3D; or at these National Biomedical Research Foundation Protein Information Resource web sites: http://pir.georgetown.edu/pirwww/dbinfo/resid .html, http://pir.georgetown.edu/pirwww/dbinfo/nrl3d .html     

一种基于MeSH主题词的临床?鄄组学关系挖掘方法

癌症的发生发展与机体内基因的改变有密切联系,在临床上表现为症状或检测指标的异常．通过挖掘分析临床表现与基因改变之间的关系,可为癌症早期诊断和精准治疗提供临床决策支持．从文献数据出发,利用结论性数据挖掘基因与临床表现的关系具有重要意义．本文提出一种基于医学主题词(Medical Subject Headings,MeSH)的生物医学实体关系挖掘方法．该方法利用PubMed中提供的文献信息,借用向量空间模型思想,使用MeSH主题词矢量表达待研究实体,引入文献相互引用因素对结果进行修正,将关系挖掘转化为矢量间的数学运算,实现定量分析．本文将该方法应用于结直肠癌临床表现和基因关系的研究中,得到与结直肠癌相关的203个基因和对应的临床-基因462个关系．通过结合使用基因功能和通路分析工具g:Profiler和KEGG等,对结果进行分析验证．结果表明,基于MeSH主题词的文献挖掘方法,避免传统“共现”方法对发现潜在关系的限制和复杂语义分析带来的大量计算,为生物实体之间潜在关系的挖掘提供一种新的思路和方法．     

Annotating proteins by mining protein interaction networks

MOTIVATION: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins. RESULTS: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%. AVAILABILITY: Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN.     

BSD: the Biodegradative Strain Database

The Biodegradative Strain Database (BSD) is a freely-accessible, web-based database providing detailed information on degradative bacteria and the hazardous substances that they degrade, including corresponding literature citations, relevant patents and links to additional web-based biological and chemical data. The BSD (http://bsd.cme.msu.edu) is being developed within the phylogenetic framework of the Ribosomal Database Project II (RDPII: http://rdp.cme.msu.edu/html) to provide a biological complement to the chemical and degradative pathway data of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD: http://umbbd.ahc.umn.edu). Data is accessible through a series of strain, chemical and reference lists or by keyword search. The web site also includes on-line data submission and user survey forms to solicit user contributions and suggestions. The current release contains information on over 250 degradative bacterial strains and 150 hazardous substances. The transformation of xenobiotics and other environmentally toxic compounds by microorganisms is central to strategies for biocatalysis and the bioremediation of contaminated environments. However, practical, comprehensive, strain-level information on biocatalytic/biodegradative microbes is not readily available and is often difficult to compile. Similarly, for any given environmental contaminant, there is no single resource that can provide comparative information on the array of identified microbes capable of degrading the chemical. A web site that consolidates and cross-references strain, chemical and reference data related to biocatalysis, biotransformation, biodegradation and bioremediation would be an invaluable tool for academic and industrial researchers and environmental engineers.