WikiHyperGlossary (WHG): an information literacy technology for chemistry documents.

BACKGROUND: The WikiHyperGlossary is an information literacy technology that was created to enhance reading comprehension of documents by connecting them to socially generated multimedia definitions as well as semantically relevant data. The WikiHyperGlossary enhances reading comprehension by using the lexicon of a discipline to generate dynamic links in a document to external resources that can provide implicit information the document did not explicitly provide. Currently, the most common method to acquire additional information when reading a document is to access a search engine and browse the web. This may lead to skimming of multiple documents with the novice actually never returning to the original document of interest. The WikiHyperGlossary automatically brings information to the user within the current document they are reading, enhancing the potential for deeper document understanding. RESULTS: The WikiHyperGlossary allows users to submit a web URL or text to be processed against a chosen lexicon, returning the document with tagged terms. The selection of a tagged term results in the appearance of the WikiHyperGlossary Portlet containing a definition, and depending on the type of word, tabs to additional information and resources. Current types of content include multimedia enhanced definitions, ChemSpider query results, 3D molecular structures, and 2D editable structures connected to ChemSpider queries. Existing glossaries can be bulk uploaded, locked for editing and associated with multiple social generated definitions. CONCLUSION: The WikiHyperGlossary leverages both social and semantic web technologies to bring relevant information to a document. This can not only aid reading comprehension, but increases the users' ability to obtain additional information within the document. We have demonstrated a molecular editor enabled knowledge framework that can result in a semantic web inductive reasoning process, and integration of the WikiHyperGlossary into other software technologies, like the Jikitou Biomedical Question and Answer system. Although this work was developed in the chemical sciences and took advantage of open science resources and initiatives, the technology is extensible to other knowledge domains. Through the DeepLit (Deeper Literacy: Connecting Documents to Data and Discourse) startup, we seek to extend WikiHyperGlossary technologies to other knowledge domains, and integrate them into other knowledge acquisition workflows.

Automatic extraction of biomolecular interactions: an empirical approach.

BACKGROUND: We describe a method for extracting data about how biomolecule pairs interact from texts. This method relies on empirically determined characteristics of sentences. The characteristics are efficient to compute, making this approach to extraction of biomolecular interactions scalable. The results of such interaction mining can support interaction network annotation, question answering, database construction, and other applications. RESULTS: We constructed a software system to search MEDLINE for sentences likely to describe interactions between given biomolecules. The system extracts a list of the interaction-indicating terms appearing in those sentences, then ranks those terms based on their likelihood of correctly characterizing how the biomolecules interact. The ranking process uses a tf-idf (term frequency-inverse document frequency) based technique using empirically derived knowledge about sentences, and was applied to the MEDLINE literature collection. Software was developed as part of the MetNet toolkit (http://www.metnetdb.org). CONCLUSIONS: Specific, efficiently computable characteristics of sentences about biomolecular interactions were analyzed to better understand how to use these characteristics to extract how biomolecules interact.The text empirics method that was investigated, though arising from a classical tradition, has yet to be fully explored for the task of extracting biomolecular interactions from the literature. The conclusions we reach about the sentence characteristics investigated in this work, as well as the technique itself, could be used by other systems to provide evidence about putative interactions, thus supporting efforts to maximize the ability of hybrid systems to support such tasks as annotating and constructing interaction networks.

Usability survey of biomedical question answering systems.

We live in an age of access to more information than ever before. This can be a double-edged sword. Increased access to information allows for more informed and empowered researchers, while information overload becomes an increasingly serious risk. Thus, there is a need for intelligent information retrieval systems that can summarize relevant and reliable textual sources to satisfy a user's query. Question answering is a specialized type of information retrieval with the aim of returning precise short answers to queries posed as natural language questions. We present a review and comparison of three biomedical question answering systems: askHERMES (http://www.askhermes.org/), EAGLi (http://eagl.unige.ch/EAGLi/), and HONQA (http://services.hon.ch/cgi-bin/QA10/qa.pl).

BirdsEyeView (BEV): graphical overviews of experimental data.

BACKGROUND: Analyzing global experimental data can be tedious and time-consuming. Thus, helping biologists see results as quickly and easily as possible can facilitate biological research, and is the purpose of the software we describe. RESULTS: We present BirdsEyeView, a software system for visualizing experimental transcriptomic data using different views that users can switch among and compare. BirdsEyeView graphically maps data to three views: Cellular Map (currently a plant cell), Pathway Tree with dynamic mapping, and Gene Ontology http://www.geneontology.org Biological Processes and Molecular Functions. By displaying color-coded values for transcript levels across different views, BirdsEyeView can assist users in developing hypotheses about their experiment results. CONCLUSIONS: BirdsEyeView is a software system available as a Java Webstart package for visualizing transcriptomic data in the context of different biological views to assist biologists in investigating experimental results. BirdsEyeView can be obtained from http://metnetdb.org/MetNet_BirdsEyeView.htm.

ISDB: Interaction Sentence Database.

BACKGROUND: Rapid growth in the scientific literature available on-line continues to motivate shifting data analysis from humans to computers. For example, greater knowledge of sentence characteristics indicative of interaction between two biological entities is needed to aid in the creation of better-performing information extraction tools for effectively using this rich body of information. FINDINGS: The Interaction Sentence Database (ISDB) allows users to retrieve sets of sentences fitting specified characteristics. To support this, a database of sentences from abstracts in MEDLINE was created. The sentences in the database all contain at least two biomolecule terms and one interaction-indicating term. A web interface to the database allows the user to query for sentences containing an interaction-indicating term, a single biomolecule name, or two biomolecule names, as well as for a list of biomolecules co-occurring with a given biomolecule in at least one sentence. CONCLUSIONS: The system supports researchers needing conveniently available sets of sample sentences for corpus-based research on sentence properties. It also illustrates a model architecture for a sentence-based retrieval system which would be useful to people seeking information and knowledge on-line. ISDB can be freely accessed over the Web at http://bioinformatics.ualr.edu/cgi-bin/services/ISDB/isdb.cgi, and the processed database will be provided upon request.

PathBinder--text empirics and automatic extraction of biomolecular interactions.

MOTIVATION: The increasingly large amount of free, online biological text makes automatic interaction extraction correspondingly attractive. Machine learning is one strategy that works by uncovering and using useful properties that are implicit in the text. However these properties are usually not reported in the literature explicitly. By investigating specific properties of biological text passages in this paper, we aim to facilitate an alternative strategy, the use of text empirics, to support mining of biomedical texts for biomolecular interactions. We report on our application of this approach, and also report some empirical findings about an important class of passages. These may be useful to others who may also wish to use the empirical properties we describe. RESULTS: We manually analyzed syntactic and semantic properties of sentences likely to describe interactions between biomolecules. The resulting empirical data were used to design an algorithm for the PathBinder system to extract biomolecular interactions from texts. PathBinder searches PubMed for sentences describing interactions between two given biomolecules. PathBinder then uses probabilistic methods to combine evidence from multiple relevant sentences in PubMed to assess the relative likelihood of interaction between two arbitrary biomolecules. A biomolecular interaction network was constructed based on those likelihoods. CONCLUSION: The text empirics approach used here supports computationally friendly, performance competitive, automatic extraction of biomolecular interactions from texts. AVAILABILITY: http://www.metnetdb.org/pathbinder.

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.

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

UNLABELLED: 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.