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Evaluating Multiple-Concept Biomedical Hypotheses Based on Deep Sets

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Artificial Neural Networks and Machine Learning – ICANN 2021 (ICANN 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12891))

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Abstract

Given hypotheses that connect two “irrelevant” concepts of interest via one or multiple concepts, Hypothesis Generation (HG) attempts to judge their meaningfulness and compare or rank them accordingly. HG can accelerate scientific research and is becoming increasingly important. The basic idea of prior studies is to conduct a two or higher-order search between the two input concepts for the one-concept and multiple-concept hypotheses to evaluate them. However, these approaches inevitably encounter exponential-growing searching space when addressing multiple-concept hypotheses, making it impractical to tackle such hypotheses. We propose HG Set Net (HSN) that forms a hypothesis with any number of connecting concepts as a set and learns to evaluate the set to address this problem. HSN can evaluate any hypotheses with the same complexity and avoid higher-order search, making it computationally possible to evaluate hypotheses with numerous concepts. Besides, we present a double-margin loss to train HSN to resolve the lack of labeled hypotheses. Experiments show that HSN can not only address hypotheses with efficiency but also outperform previous approaches. The double-margin loss also reveals to boost HSN’s performance.

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References

  1. Akujuobi, U., Spranger, M., Palaniappan, S.K., Zhang, X.: T-PAIR: temporal node-pair embedding for automatic biomedical hypothesis generation. IEEE Trans. Knowl. Data Eng. 1 (2020)

    Google Scholar 

  2. Bhattacharya, S., Ha-Thuc, V., Srinivasan, P.: MeSH: a window into full text for document summarization. Bioinformatics 27(13), i120–i128 (2011)

    Article  Google Scholar 

  3. Cameron, D., et al.: A graph-based recovery and decomposition of Swanson’s hypothesis using semantic predications. J. Biomed. Inform. 46(2), 238–251 (2013)

    Article  Google Scholar 

  4. Cameron, D., Kavuluru, R., Rindflesch, T.C., Sheth, A.P., Thirunarayan, K., Bodenreider, O.: Context-driven automatic subgraph creation for literature-based discovery. J. Biomed. Inform. 54, 141–157 (2015)

    Article  Google Scholar 

  5. Cohen, A.M., Hersh, W.R.: A survey of current work in biomedical text mining. Brief. Bioinform. 6(1), 57–71 (2005)

    Article  Google Scholar 

  6. Crichton, G., Baker, S., Guo, Y., Korhonen, A.: Neural networks for open and closed literature-based discovery. PLoS ONE 15(5), e0232891 (2020)

    Google Scholar 

  7. Hu, X., Zhang, X., Yoo, I., Wang, X., Feng, J.: Mining hidden connections among biomedical concepts from disjoint biomedical literature sets through semantic-based association rule. Int. J. Intell. Syst. 25(2), 207–223 (2010)

    Google Scholar 

  8. Jha, K., Xun, G., Wang, Y., Zhang, A.: Hypothesis generation from text based on co-evolution of biomedical concepts. In: KDD 2018, pp. 843–851 (2019)

    Google Scholar 

  9. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NeurIPS 2013, pp. 3111–3119 (2013)

    Google Scholar 

  10. Smalheiser, N.R.: Rediscovering Don Swanson: the past, present and future of literature-based discovery. J. Data Inf. Sci. 2(4), 43–64 (2017)

    Google Scholar 

  11. Srinivasan, P.: Text mining: generating hypotheses from MEDLINE. J. Assoc. Inf. Sci. Technol. 55(5), 396–413 (2004)

    Article  Google Scholar 

  12. Swanson, D.R.: Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect. Biol. Med. 30(1), 7–18 (1986)

    Article  Google Scholar 

  13. Swanson, D.R., Smalheiser, N.R.: An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artif. Intell. 91(2), 183–203 (1997)

    Article  Google Scholar 

  14. Thilakaratne, M., Falkner, K., Atapattu, T.: A systematic review on literature-based discovery: general overview, methodology, & statistical analysis. ACM Comput. Surv. 52(6), 1–34 (2019)

    Article  Google Scholar 

  15. Wilkowski, B., et al.: Graph-based methods for discovery browsing with semantic predications. In: AMIA 2011, vol. 2011, p. 1514 (2011)

    Google Scholar 

  16. Xun, G., Jha, K., Gopalakrishnan, V., Li, Y., Zhang, A.: Generating medical hypotheses based on evolutionary medical concepts. In: ICDM 2017, pp. 535–544 (2017)

    Google Scholar 

  17. Yetisgen-Yildiz, M., Pratt, W.: A new evaluation methodology for literature-based discovery systems. J. Biomed. Inform. 42(4), 633–643 (2009)

    Article  Google Scholar 

  18. Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: NeurIPS 2017, Long Beach, CA, USA, pp. 3391–3401 (2017)

    Google Scholar 

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Authors and Affiliations

  1. University of North Texas, Denton, TX, 76201, USA

    Juncheng Ding & Wei Jin

Authors
  1. Juncheng Ding
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  2. Wei Jin
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Correspondence to Juncheng Ding .

Editor information

Editors and Affiliations

  1. Comenius University in Bratislava, Bratislava, Slovakia

    Igor Farkaš

  2. iMotions A/S, Copenhagen, Denmark

    Paolo Masulli

  3. University of Tübingen, Tübingen, Baden-Württemberg, Germany

    Sebastian Otte

  4. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

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Ding, J., Jin, W. (2021). Evaluating Multiple-Concept Biomedical Hypotheses Based on Deep Sets. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12891. Springer, Cham. https://doi.org/10.1007/978-3-030-86362-3_39

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  • DOI: https://doi.org/10.1007/978-3-030-86362-3_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86361-6

  • Online ISBN: 978-3-030-86362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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