Biomedical Digital Library Research Articles

MedGraph: A semantic biomedical information retrieval framework using knowledge graph embedding for PubMed.

Here we study the semantic search and retrieval problem in biomedical digital libraries. First, we introduce MedGraph, a knowledge graph embedding-based method that provides semantic relevance retrieval and ranking for the biomedical literature indexed in PubMed. Second, we evaluate our approach using PubMed's Best Match algorithm. Moreover, we compare our method MedGraph to a traditional TF-IDF-based algorithm. Third, we use a dataset extracted from PubMed, including 30 million articles' metadata such as abstracts, author information, citation information, and extracted biological entity mentions. We pull a subset of the dataset to evaluate MedGraph using predefined queries with ground truth ranked results. To our knowledge, this technique has not been explored before in biomedical information retrieval. In addition, our results provide some evidence that semantic approaches to search and relevance in biomedical digital libraries that rely on knowledge graph modeling offer better search relevance results when compared with traditional methods in terms of objective metrics.

On Rectifying the Mapping between Articles and Institutions in Bibliometric Databases

Today, bibliometric databases are indispensable sources for researchers and research institutions. The main role of these databases is to find research articles and estimate the performance of researchers and institutions. Regarding the evaluation of the research performance of an organization, the accuracy in determining institutions of authors of articles is decisive. However, current popular bibliometric databases such as Scopus and Web of Science have not addressed this point efficiently. To this end, we propose an approach to revise the authors’ affiliation information of articles in bibliometric databases. We build a model to classify articles to institutions with high accuracy by assembling the bag of words and n-grams techniques for extracting features of affiliation strings. After that, these features are weighted to determine their importance to each institution. Affiliation strings of articles are transformed into the new feature space by integrating weights of features and local characteristics of words and phrases contributing to the sequences. Finally, on the feature space, the support vector classifier method is applied to learn a predictive model. Our experimental result shows that the proposed model’s accuracy is about 99.1%.
 Keywords:Affiliation, Disambiguation, Data cleaning, Classification, Supervised learning, if-iif, Support vector machine, Support vector classifier
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The Field of Psychological Assessment: Where it Stands and Where it’s Going – A Personal Analysis of Foci, Gaps, and Implications for EJPA

The Field of Psychological Assessment: Where it Stands and Where it’s Going – A Personal Analysis of Foci, Gaps, and Implications for <i>EJPA</i>

TAKES: Two-step Approach for Knowledge Extraction in Biomedical Digital Libraries

This paper proposes a novel knowledge extraction system, TAKES (Two-step Approach for Knowledge Extraction System), which integrates advanced techniques from Information Retrieval (IR), Information Extraction (IE), and Natural Language Processing (NLP). In particular, TAKES adopts a novel keyphrase extraction-based query expansion technique to collect promising documents. It also uses a Conditional Random Field-based machine learning technique to extract important biological entities and relations. TAKES is applied to biological knowledge extraction, particularly retrieving promising documents that contain Protein-Protein Interaction (PPI) and extracting PPI pairs. TAKES consists of two major components: DocSpotter, which is used to query and retrieve promising documents for extraction, and a Conditional Random Field (CRF)-based entity extraction component known as FCRF. The present paper investigated research problems addressing the issues with a knowledge extraction system and conducted a series of experiments to test our hypotheses. The findings from the experiments are as follows: First, the author verified, using three different test collections to measure the performance of our query expansion technique, that DocSpotter is robust and highly accurate when compared to Okapi BM25 and SLIPPER. Second, the author verified that our relation extraction algorithm, FCRF, is highly accurate in terms of F-Measure compared to four other competitive extraction algorithms: Support Vector Machine, Maximum Entropy, Single POS HMM, and Rapier.

SOA-based digital library services and composition in biomedical applications

Carefully collected, high-quality data are crucial in biomedical visualization, and it is important that the user community has ready access to both this data and the high-performance computing resources needed by the complex, computational algorithms that will process it. Biological researchers generally require data, tools and algorithms from multiple providers to achieve their goals. This paper illustrates our response to the problems that result from this. The Living Human Digital Library (LHDL) project presented in this paper has taken advantage of Web Services to build a biomedical digital library infrastructure that allows clinicians and researchers not only to preserve, trace and share data resources, but also to collaborate at the data-processing level.

A Dynamic and Semantically-Aware Technique for Document Clustering in Biomedical Literature

As an unsupervised learning process, document clustering has been used to improve information retrieval performance by grouping similar documents and to help text mining approaches by providing a high-quality input for them. In this article, the authors propose a novel hybrid clustering technique that incorporates semantic smoothing of document models into a neural network framework. Recently, it has been reported that the semantic smoothing model enhances the retrieval quality in Information Retrieval (IR). Inspired by that, the authors developed and applied a context-sensitive semantic smoothing model to boost accuracy of clustering that is generated by a dynamic growing cell structure algorithm, a variation of the neural network technique. They evaluated the proposed technique on biomedical article sets from MEDLINE, the largest biomedical digital library in the world. Their experimental evaluations show that the proposed algorithm significantly improves the clustering quality over the traditional clustering techniques including k-means and self-organizing map (SOM).

Knowledge management in biomedical libraries: A semantic web approach

In recent years, technological advances in high-throughput techniques and efficient data gathering methods, coupled with a world-wide effort in computational biology, have resulted in an enormous amount of life science data available in repositories devoted to biomedical literature. These repositories lack the ability to attain an effective and accurate search. Using semantic technologies as the key for interoperation enables searching and processing of biomedical literature in a more efficient way. However, emerging semantic applications take for granted specific knowledge that biomedical researchers may not have. This paper presents design principles for easy-to-use biomedical semantic applications by means of ontology-based annotations and faceted search. The proposed approach is backed with a usable prototype that shows the breakthroughs of adding these principles to a biomedical digital library where identifying and searching information are critical aspects for non-semantic Web experts.

A Graph-Based Biomedical Literature Clustering Approach Utilizing Term's Global and Local Importance Information

In this article, we present a graph-based knowledge representation for biomedical digital library literature clustering. An efficient clustering method is developed to identify the ontology-enriched k-highest density term subgraphs that capture the core semantic relationship information about each document cluster. The distance between each document and the k term graph clusters is calculated. A document is then assigned to the closest term cluster. The extensive experimental results on two PubMed document sets (Disease10 and OHSUMED23) show that our approach is comparable to spherical k-means. The contributions of our approach are the following: (1) we provide two corpus-level graph representations to improve document clustering, a term co-occurrence graph and an abstract-title graph; (2) we develop an efficient and effective document clustering algorithm by identifying k distinguishable class-specific core term subgraphs using terms’ global and local importance information; and (3) the identified term clusters give a meaningful explanation for the document clustering results.

A semi-supervised efficient learning approach to extract biological relationships from web-based biomedical digital library

Many biological results are published only in plain --text documents and these documents or their abstracts are collected in web-based digital libraries such as PubMed and BioMed Central. To expedite the progress of functional bioinformatics, it is important to efficiently process large amounts of these documents, to extract these results into a structured format, and to store them in a database so that these results can be retrieved and analyzed by biologists and medical researchers. Automated discovery and extraction of the biological knowledge from biomedical web documents has become essential because of the enormous amount of biomedical literature published each year. In this paper we present a semi-supervised efficient learning approach to automatically extract biological knowledge from the web-based digital libraries. Our method integrates ontology-based semantic tagging as well as information extraction and data mining together. Our method automatically learns the patterns based on a few user seed tuples and then extracts new tuples from the biomedical web documents based on the discovered patterns. A novel system, SPIE Scalable and Portable Information Extraction, is implemented and tested on the PuBMed to find the chromatin protein --protein interaction. The experimental results indicate our approach is very effective in extracting biological knowledge from a huge collection of biomedical web documents.

The future of Biomedical Digital Libraries

Our colleague Charles J. Greenberg founded Biomedical Digital Libraries (BDL) in 2004, with the support of an outstanding team of editors and advisors [1]. He envisioned the journal as a alternative to traditional specialty journals in the field, which have subscription fees and assumption of copyright by the publisher [2]. Charlie has now decided to pursue other worthy endeavors, and remains on our editorial board. As the new editors-in-chief of BDL, we share his belief in the power of open access and in the potential of this journal. BDL has covered a broad terrain. We have published resource reviews and commentaries, and documented innovative uses of technology that further scientific discovery and inform collection development. Readers are welcome to peruse all articles published thus far [3]. One strength of BDL is the variety of article types, including hypotheses, which are, short articles presenting an untested original hypothesis backed solely by previously published results rather than new evidence Hypotheses should outline significant progress in thinking that would also be testable [4]. We hope to publish the first hypothesis in BDL soon. Print-only articles are not the only form of scholarly communication in the online era. BDL stands ready to publish multimedia works, including podcasts. Ramsey's Multimedia Bootcamp paper is the first BDL piece to feature movies [5], and we expect that many more will follow. Regardless of the presentation format, our dedicated team of peer reviewers will critique submissions fairly and rigorously. After a healthy internal discussion BDL recently adopted an open peer review process, in which the authors and reviewers are all known to each other. Despite legitimate concerns that non-anonymity will cause reviewers to be less candid, we believe that transparency will lead to more thorough reviews overall. BioMed Central, BDL's publisher, has endorsed the concept of open peer review as well [6]. New presentation formats, new reviewing protocols - All of this causes us to think we are looking at a new type of journal. In an era of rapidly disseminated pre-print articles and frequently cited web log postings, the concept of the formal journal must evolve in order to remain vibrant. We believe that BDL is up to this challenge, and cordially invite the insights of our editors, advisors, authors and readers as we begin the journey.

Good old days?

Alternative models of subsidizing scholarly publishing and dissemination have germinated and gathered momentum in the fertile soil of dissatisfaction. Like the stubborn spring dandelion that needs but a small crack in the sidewalk to flower boldly, the first flowers of Open Access in library literature, including Biomedical Digital Libraries, have sensed their opportunity to change the existing paradigm of giving away our scholarship and intellectual property, only to buy it back for the privilege of knowing it can be read. Will biomedical digital library and informatics researchers understand their role in a new era of Open Access simply by desiring an immediate uninhibited global audience and recognizing the necessity of open access peer-reviewed literature to become self-sufficient?