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DTMBIO 2012: international workshop on data and text mining in biomedical informatics

Published: 29 October 2012 Publication History
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  • Abstract

    The organizers of ACM Sixth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO 12) are happy announce that the sixth DTMBIO will be held in conjunction with CIKM, one of the largest data management conferences. The major interests of DTMBIO are on the state-of-the-art applications of data and text mining on biomedical research problems. DTMBIO 12 will be a forum of discussing and exchanging informatics related techniques and problems in the context of biomedical research.

    References

    [1]
    Malyszko, J., Filipowska, A. 2012. Lexicon-free and context-free drug names identification methods using Hidden Markov Models and Pointwise Mutual Information. In DTMBIO 12.
    [2]
    Tang, B., Cao, H., Wu, Y., Jiang, M., Xu, H. 2012. Clinical Entity Recognition using Structural Support Vector Machines with Rich Features. In DTMBIO 12.
    [3]
    Restificar, A., Sophia Ananiadou, A. 2012. Inferring Appropriate Eligibility Criteria in Clinical Trial Protocols without Labeled Data. In DTMBIO 12.
    [4]
    Rao, A., Maiden, K., Carterette, B., Ehrentha, D. 2012. Predicting Baby Feeding Method from Unstructured Electron Health Record. In DTMBIO 12.
    [5]
    MacKinlay, A., Karin Verspoor, K. 2012. Extracting Structured Information from Free-Text Medication Prescriptions Using Dependencies. In DTMBIO 12.
    [6]
    Kim, S., Lee, S., Yu, H. 2012. Indexing Methods for Efficient Protein 3D Surface Search. In DTMBIO 12.
    [7]
    Ahn, J., Lee, D. H., Yoon, Y. Yeu, Y., Park, S. 2012. Protein Complex Prediction via Bottleneck-Based Graph Partitioning. In DTMBIO 12.
    [8]
    Kugaonkar, R., Gangopadhyay, A., Yesha, Y., Joshi, A., Yesha, Y., Grasso, M., Brady, M., Rishe, N. 2012. Finding Associations among SNPs for Prostate Cancer using Collaborative Filtering. In DTMBIO 12.
    [9]
    Han, Y., Yi, G. S. 2012. Prediction of E3-specific Substrates by Using Known E3-Substrate Network. In DTMBIO 12.
    [10]
    Kang, C., Yu, H., Yi, G. S. 2012. Detecting Type 2 Diabetes Causal SNP Combinations from GWAS Dataset with Optimal Filtration. In DTMBIO 12.
    [11]
    Choi, J., Kim, K., Song, M., Lee, D. H. 2012. TNMCA: Generation and Application of Network Motif-Based Inference Models for Drug Repositioning. In DTMBIO 12.
    [12]
    Lee, J., Kim, S. Lee, S., Lee, K., Kang, J. 2012. High Precision Rule Based PPI Extraction and Per-Pair Basis Performance. In DTMBIO 12.
    [13]
    Hwang, W. Hwang, Y., Lee, S., Lee, D. H. 2012. Rule-based whole body modeling for analyzing multi-compound effects. In DTMBIO 12.

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    • (2024)An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation StudyJMIR Medical Informatics10.2196/5531812(e55318)Online publication date: 8-Apr-2024

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      cover image ACM Conferences
      CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
      October 2012
      2840 pages
      ISBN:9781450311564
      DOI:10.1145/2396761

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      Association for Computing Machinery

      New York, NY, United States

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      Published: 29 October 2012

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      • (2024)An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing: Algorithm Development and Validation StudyJMIR Medical Informatics10.2196/5531812(e55318)Online publication date: 8-Apr-2024

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