A Survey of State of the Art Biomedical Text Mining Techniques for Semantic Analysis

Dai, Hong-Jie; Lin, Jinjian; Huang, Chi-Hsin; Chou, Pei-Hsuan; Tsai, Richard Tzong-Han; Hsu, Wen-Lian

doi:10.1109/sutc.2008.86

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…XplorMed organizes results by MeSH categories, extracts topic keywords and their co-occurrences and furthermore it provides an interactive navigation through abstracts. For a comprehensive survey of such biomedical text mining systems along with their various characteristics and features, one can consult [4,5,15,16].…”

Section: Biomedical Text Miningmentioning

confidence: 99%

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Mining Biological Data on the Cloud – A MapReduce Approach

Ioannou

Nodarakis

Sioutas

et al. 2014

IFIP Advances in Information and Communication Technology

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Abstract. During last decades, bioinformatics has proven to be an emerging field of research leading to the development of a wide variety of applications. The primary goal of bioinformatics is to detect useful knowledge hidden under large volumes biological and biomedical data, gain a greater insight into their relationships and, therefore, enhance the discovery and the comprehension of biological processes. To achieve this, a great number of text mining techniques have been developed that efficiently manage and disclose meaningful patterns and correlations from biological and biomedical data repositories. However, as the volume of data grows rapidly these techniques cannot cope with the computational burden that is produced since they apply only in centralized environments. Consequently, a turn into distributed and parallel solutions is indispensable. In the context of this work, we propose an efficient and scalable solution, in the MapReduce framework, for mining and analyzing biological and biomedical data.

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Section: Biomedical Text Miningmentioning

confidence: 99%

“…To perform such tasks in an enormous corpus like PubMed is unthinkable. Most existing methods in literature [4,5,10,11,15,16] apply to a few hundreds or thousands of records. As a result, high scalable implementations are required.…”

Section: Introductionmentioning

confidence: 99%

Mining Biological Data on the Cloud – A MapReduce Approach

Ioannou

Nodarakis

Sioutas

et al. 2014

IFIP Advances in Information and Communication Technology

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A set of novel mining tools for efficient biological knowledge discovery

et al. 2013

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Categorizing biomedicine images using novel image features and sparse coding representation

Sheng

Luo

2013

BMC Med Genomics

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BackgroundImages embedded in biomedical publications carry rich information that often concisely summarize key hypotheses adopted, methods employed, or results obtained in a published study. Therefore, they offer valuable clues for understanding main content in a biomedical publication. Prior studies have pointed out the potential of mining images embedded in biomedical publications for automatically understanding and retrieving such images' associated source documents. Within the broad area of biomedical image processing, categorizing biomedical images is a fundamental step for building many advanced image analysis, retrieval, and mining applications. Similar to any automatic categorization effort, discriminative image features can provide the most crucial aid in the process.MethodWe observe that many images embedded in biomedical publications carry versatile annotation text. Based on the locations of and the spatial relationships between these text elements in an image, we thus propose some novel image features for image categorization purpose, which quantitatively characterize the spatial positions and distributions of text elements inside a biomedical image. We further adopt a sparse coding representation (SCR) based technique to categorize images embedded in biomedical publications by leveraging our newly proposed image features.Resultswe randomly selected 990 images of the JPG format for use in our experiments where 310 images were used as training samples and the rest were used as the testing cases. We first segmented 310 sample images following the our proposed procedure. This step produced a total of 1035 sub-images. We then manually labeled all these sub-images according to the two-level hierarchical image taxonomy proposed by [1]. Among our annotation results, 316 are microscopy images, 126 are gel electrophoresis images, 135 are line charts, 156 are bar charts, 52 are spot charts, 25 are tables, 70 are flow charts, and the remaining 155 images are of the type "others". A serial of experimental results are obtained. Firstly, each image categorizing results is presented, and next image categorizing performance indexes such as precision, recall, F-score, are all listed. Different features which include conventional image features and our proposed novel features indicate different categorizing performance, and the results are demonstrated. Thirdly, we conduct an accuracy comparison between support vector machine classification method and our proposed sparse representation classification method. At last, our proposed approach is compared with three peer classification method and experimental results verify our impressively improved performance.ConclusionsCompared with conventional image features that do not exploit characteristics regarding text positions and distributions inside images embedded in biomedical publications, our proposed image features coupled with the SR based representation model exhibit superior performance for classifying biomedical images as demonstrated in our comparative benchmark study.

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A Survey of State of the Art Biomedical Text Mining Techniques for Semantic Analysis

Abstract: In

Cited by 4 publications

References 40 publications

Mining Biological Data on the Cloud – A MapReduce Approach

Mining Biological Data on the Cloud – A MapReduce Approach

A set of novel mining tools for efficient biological knowledge discovery

Categorizing biomedicine images using novel image features and sparse coding representation

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