Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table

General topic: applications of transgenic rabbits in biomedical research - based on literature search

[EN] Transgenic rabbits are widely used as a model organism for biomedical research, and the transgenic rabbit system is especially valuable because it fills an important niche between laboratory mice and larger domesticated mammals. In order to describe the current status and development trends of the use of transgenic rabbits in biomedical research precisely, we performed a quantitative analysis of the published data, collected by searching biomedical databases. Currently, there are about 217 papers related to transgenic rabbits, originating from 22 countries. The number of publications has slowly increased over time, reaching its peak in 2004 and 2007. Approximately one third of the publications come from the USA, and one quarter come from Japan. The USA, Japan and France were the top three producers of publications related to transgenic rabbits. These publications mainly focused on cardiovascular disease (CVD) and the study of therapeutic protein bioreactors. Approximately 19 transgenic rabbit lines have been established for the study of CVD, and 20 recombinant proteins have been produced from transgenic rabbit milk or blood. The remaining publications largely focused on virology and immunology, diabetes mellitus, cancer, and genetics. These publications provide new insights into the mechanisms responsible for the development of human disease and shed light on the management of some genetic disorders. Thus, this quantitative review of the literature reveals that transgenic rabbits play an increasingly important role in biomedical research.This work was partly supported by the National Natural Science Foundation of China (grant no. 30900526)Zhao, S.; Wei, K.; Yu, Q.; Li, Y.; Cheng, F.; Wang, Y.; Yang, P.... (2010). General topic: applications of transgenic rabbits in biomedical research - based on literature search. World Rabbit Science. 18(3). doi:10.4995/wrs.2010.727918

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians. © 2006Bekhuis; licensee BioMed Central Ltd

The U.S. Science and Engineering Workforce: Recent, Current, and Projected Employment, Wages, and Unemployment

[Excerpt] As Congress develops policies and programs and makes appropriations to help address the nation’s needs for scientists and engineers, it may wish to consider past, current, and projected S&E workforce trends. In this regard, this report provides employment, wage, and unemployment information for the computer occupations, mathematical occupations, engineers, life scientists, physical scientists, and S&E management occupations, in three sections: “Current Employment, Wages, and Unemployment” provides a statistical snapshot of the S&E workforce in 2011 (the latest year for which data are available) with respect to occupational employment, wage, and unemployment data. “Recent Trends in Employment, Wages, and Unemployment” provides a perspective on how S&E employment, wages, and unemployment have changed during the 2008-2011 period. “Employment Projections, 2010-2020” provides an analysis of the Bureau of Labor Statistics’ occupational projections examining how the number employed in S&E occupations are expected to change during the 2010-2020 period, as well as how many openings will be created by workers exiting each occupation (replacement needs). A final section, “Concluding Observations,” provides various stakeholder perspectives that Congress may wish to consider as it seeks to ensure that the United States has an adequate S&E workforce to meet the demands of the 21st century

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care