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An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges
- Karamarie Fecho, Chris Bizon, Tursynay Issabekova, Sierra Moxon, Anne E. Thessen, Shervin Abdollahi, Sergio E. Baranzini, Basazin Belhu, William E. Byrd, Lawrence Chung, Andrew Crouse, Marc P. Duby, Stephen Ferguson, Aleksandra Foksinska, Laura Forero, Jennifer Friedman, Vicki Gardner, Gwênlyn Glusman, Jennifer Hadlock, Kristina Hanspers, Eugene Hinderer, Charlotte Hobbs, Gregory Hyde, Sui Huang, David Koslicki, Philip Mease, Sandrine Muller, Christopher J. Mungall, Stephen A. Ramsey, Jared Roach, Irit Rubin, Shepherd H. Schurman, Anath Shalev, Brett Smith, Karthik Soman, Sarah Stemann, Andrew I. Su, Casey Ta, Paul B. Watkins, Mark D. Williams, Chunlei Wu, Colleen H. Xu, The Biomedical Data Translator Consortium
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- Journal:
- Journal of Clinical and Translational Science / Volume 7 / Issue 1 / 2023
- Published online by Cambridge University Press:
- 14 September 2023, e214
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- Article
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Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.
Assessment of Target Capital for General Insurance Firms
- A. N. Hitchcox, I. A. Hinder, A. M. Kaufman, T. J. Maynard, A. D. Smith, M. G. White
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- Journal:
- British Actuarial Journal / Volume 13 / Issue 1 / 01 March 2007
- Published online by Cambridge University Press:
- 10 June 2011, pp. 81-168
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Capital and cost of capital form a bridge between the insurance firm and the financial markets. The term capital is used in various ways. In current parlance, economic capital is frequently used to mean capital calculated using a risk-based measure which is independent of the regulatory requirements. In this paper we discuss the concept of target capital, where the firm takes account of three different approaches to risk appetite: regulatory capital plus a buffer; rating agency views; and the views of shareholders, where they make commitments to customers and wish to protect franchise value. We describe how, when blending these views, the firm needs to understand the trade-offs between too high and too low amounts of capital, with reference to the double taxation burden, insurance gearing (leverage of premiums to capital ratio), and the impact of the firm's credit rating on maximising franchise value. We then discuss the main drivers of the cost of capital, which we define as the required total return on the market value of the firm, as determined by reference to the opportunity cost of alternative investments of equivalent risk. We explain that, because the stock market value of the firm is not the same as the capital held inside the firm, the cost of capital derived from external studies cannot be directly applied to internal measures of target return such as return on equity (ROE); it is necessary to translate between the two measures. We separate the risk of the firm between the investment risk and the insurance risk. We describe the frictional costs of investing in an insurance firm, and explain the role of parameter and model risk arising from the uncertainty of the future claim costs of the firm. We describe the findings of two studies of the actual historical stock market returns of United States P&C companies. One of them suggests that applying the Fama-French model produces higher and more accurate cost of capital estimates than the capital asset pricing model (CAPM) method. This is explained by linking the price to book ratio to the costs of financial distress, which are particularly important for general insurance firms, given the influence of insurance strength ratings from the rating agencies. Finally, we attempt to estimate the risk load required in premiums to compensate investors for the elements of cost of capital which we have described, in a way that combines the financial economic approaches to insurance target returns with the traditional actuarial approaches to assessing the risks in the insurance business.