Abstract
An understanding of the molecular basis of musculoskeletal pain is necessary for the development of therapeutics, their management, and possible personalization. One-in-three Americans use OTC pain killers, and one tenth use prescription drugs to manage pain. The CDC also estimates that about 20% Americans suffer from chronic pain. As the experience of acute or chronic pain varies due to individual genetics and physiology, it is imperative that researchers continue to find novel therapeutics to treat or manage symptoms. In this paper, our goal is to develop a seed knowledgebased computational platform, called BioNursery, that will allow biologists to computationally hypothesize, define and test molecular mechanisms underlying pain. In our knowledge ecosystem, we accumulate curated information from users about the relationships among biological databases, analysis tools, and database contents to generate biological analyses modules, called \(\pi \)-graphs, or process graphs. We propose a mapping function from a natural language description of a hypothesized molecular model to a computational workflow for testing in BioNursery. We use a crowd computing feedback and curation system, called Explorer, to improve proposed computational models for molecular mechanism discovery, and growing the knowledge ecosystem.
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References
Clark, C.A.C., Helikar, T., Dauer, J.: Simulating a computational biological model, rather than reading, elicits changes in brain activity during biological reasoning. CBE Life Sci. Educ. 19(3), ar45 (2020)
Jamil, H., Naha, K.: Mapping strategies for declarative queries over online heterogeneous biological databases for intelligent responses. In: SAC 2023, Tallinn, 27–31 March 2023. ACM (2023)
Jamil, H.M.: Knowledge rich natural language queries over structured biological databases. In: BCB 2017, Boston, 20–23 August 2017, pp. 352–361 (2017)
Jamil, H.M., Sadri, F.: Crowd enabled curation and querying of large and noisy text mined protein interaction data. Distribut. Parall. Datab. 36(1), 9–45 (2018)
Liu, G.,et al.: Aging atlas: a multi-omics database for aging biology. Nucl. Acids Res. 49(Database-Issue), D825–D830 (2021)
Medlock, L., Sekiguchi, K., Hong, S., Dura-Bernal, S., Lytton, W.W., Prescott, S.A.: Multiscale computer model of the spinal dorsal horn reveals changes in network processing associated with chronic pain. J. Neurosci. 42(15), 3133–3149 (2022)
Mou, X., Jamil, H.M.: Visual life sciences workflow design using distributed and heterogeneous resources. IEEE/ACM TCBB 17(4), 1459–1473 (2020)
Rasu, R., et al.: Cost of pain medication to treat adult patients with nonmalignant chronic pain in the United States. J. Manag. Care Spec. Pharm. 20(9), 921–928 (2014)
Roos, M., et al.: Structuring and extracting knowledge for the support of hypothesis generation in molecular biology. BMC Bioinformatcis 10(S-10), 9 (2009)
Russ, D.E., et al.: A harmonized atlas of mouse spinal cord cell types and their spatial organization. Nat. Commun. 12(1) (2021)
Sadri, F.: On the foundations of probabilistic information integration. In: CIKM, Maui, 29 October–02 November 2012, pp. 882–891 (2012)
Acknowledgement
This research was partially supported by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant #P20GM103408.
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Jamil, H.M., Krawetz, S., Gow, A. (2023). Automatic Hypotheses Testing Over Heterogeneous Biological Databases Using Open Knowledge Networks. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_34
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DOI: https://doi.org/10.1007/978-3-031-48316-5_34
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