{"id":29691,"date":"2021-11-03T21:55:12","date_gmt":"2021-11-03T13:55:12","guid":{"rendered":"https:\/\/swarma.org\/?p=29691"},"modified":"2021-11-03T21:55:12","modified_gmt":"2021-11-03T13:55:12","slug":"%e7%b3%bb%e7%bb%9f%e7%94%9f%e7%89%a9%e5%ad%a6%e4%b8%ad%e7%9a%84%e5%bb%ba%e6%a8%a1%ef%bc%9a%e5%9b%a0%e6%9e%9c%e7%90%86%e8%a7%a3%e5%85%88%e4%ba%8e%e9%a2%84%e6%b5%8b","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=29691","title":{"rendered":"\u7cfb\u7edf\u751f\u7269\u5b66\u4e2d\u7684\u5efa\u6a21\uff1a\u56e0\u679c\u7406\u89e3\u5148\u4e8e\u9884\u6d4b?"},"content":{"rendered":"
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2021\u5e746\u6708\uff0c\u53d1\u8868\u4e8eCell <\/em>\u65d7\u4e0bPatterns <\/em>\u6742\u5fd7\u7684\u8bba\u6587\u201c\u86cb\u767d\u8d28\u7ec4\u4e2d\u7684\u56e0\u679c\u76f8\u4e92\u4f5c\u7528\uff1a\u5206\u5b50\u6570\u636e\u9047\u89c1\u901a\u8def\u77e5\u8bc6\u201d\uff0c\u4ecb\u7ecd\u4e86\u4e00\u6b3e\u540d\u4e3a CausalPath<\/em> \u7684\u56e0\u679c\u63a8\u65ad\u5de5\u5177\uff0c\u80fd\u591f\u7ed3\u5408\u65b0\u7684\u6d4b\u91cf\u7ed3\u679c\u4e0e\u5148\u9a8c\u77e5\u8bc6\uff0c\u63a8\u65ad\u86cb\u767d\u8d28\u7ec4\u6570\u636e\u4e2d\u56e0\u679c\u4fe1\u53f7\u7684\u76f8\u4e92\u4f5c\u7528\u3002\u8fd9\u79cd\u65b9\u6cd5\u6a21\u4eff\u4e86\u751f\u7269\u5b66\u5bb6\u7528\u5148\u9a8c\u77e5\u8bc6\u89e3\u91ca\u65b0\u7684\u5b9e\u9a8c\u6570\u636e\u7684\u4f20\u7edf\u65b9\u6cd5\uff0c\u4f46\u53ef\u4ee5\u5728\u6570\u5341\u4e07\u53cd\u5e94\u7684\u89c4\u6a21\u4e0a\u8fdb\u884c\u3002\u4ee5\u4e0b\u662f Patterns <\/em>\u6742\u5fd7\u5bf9\u8fd9\u9879\u5de5\u4f5c\u7684\u8bc4\u8bba\u3002<\/strong><\/p>\n


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Szilvia Barsi, Bence Szalai<\/span> | \u4f5c\u8005<\/strong><\/span><\/strong><\/p>\n

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Causal interactions from proteomic profiles: Molecular data meet pathway knowledge<\/span><\/p>\n

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\u5df4\u5e03\u5c14\u7b49\u4eba<\/span>[Babur et al. (2021) ]<\/span>\u4e3a\u8fd9\u4e2a\u540e\u6765\u7684\u5de5\u5177\u96c6\u6dfb\u52a0\u4e86\u4e00\u79cd\u6709\u8da3\u7684\u65b0\u65b9\u6cd5\uff0c\u80fd\u591f\u7528\u6765\u533a\u5206\u7b80\u5355\u7684\u76f8\u5173\u6027\u548c\u8f83\u4e3a\u590d\u6742\u7684\u56e0\u679c\u6027\u3002CausalPath <\/em>\u65b9\u6cd5\u4f7f\u7528\u6765\u81ea\u8def\u5f84\u5171\u7528\u6570\u636e\u5e93<\/span>\uff08Pathway Commons database\uff09<\/span>\u7684\u6fc0\u9176\/\u78f7\u9178\u9176\u5e95\u7269*\u548c\u8f6c\u5f55\u56e0\u5b50\u6240\u8c03\u8282\u7684\u57fa\u56e0\u5173\u7cfb\uff0c\u6765\u521b\u5efa\u56fe\u5f62\u6a21\u5f0f\u3002\u8fd9\u4e9b\u56fe\u5f62\u6a21\u5f0f\u662f\u7c7b\u4f3c\u4e0b\u9762\u8fd9\u6837\u7684\u56e0\u679c\u5173\u8054\uff1a\u5f53P1\u4f4d\u70b9\u78f7\u9178\u5316\u65f6\uff0c\u6fc0\u9176A\u662f\u6d3b\u8dc3\u7684\uff1b\u6d3b\u6027\u6fc0\u9176A\u5728P2\u4f4d\u70b9\u4e0a\u78f7\u9178\u5316\u86cb\u767d\u8d28B\u3002\u8fd9\u4e9b\u56fe\u5f62\u6a21\u5f0f\u4e0e\u5982\u4e0b\u6d4b\u91cf\u7ed3\u679c\u76f8\u5339\u914d\uff1a\u6fc0\u9176A\u5728P1\u4f4d\u70b9\u78f7\u9178\u5316\uff0c\u800c\u86cb\u767d\u8d28B\u5728P2\u4f4d\u70b9\u78f7\u9178\u5316\uff1b\u4ece\u800c\u4f7f\u6211\u4eec\u53ef\u4ee5\u505a\u51fa\u56e0\u679c\u63a8\u6d4b\uff0c\u8bc6\u522b\u4fe1\u53f7\u7684\u6f5c\u5728\u56e0\u679c\u65b9\u5f0f<\/strong>\uff1a\u7ed9\u5b9a\u6570\u636e\u96c6\u4e2d\u6fc0\u9176A\u78f7\u9178\u5316\u86cb\u767d\u8d28B\u3002<\/span><\/p>\n


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CausalPath <\/span><\/em>\u65b9\u6cd5\u8fd8\u4f7f\u7528\u57fa\u4e8e\u6570\u636e\u6807\u7b7e\u6392\u5217\u7684\u65b9\u6cd5\u68c0\u9a8c\u7ed3\u679c\u7684\u7edf\u8ba1\u663e\u8457\u6027\u3002\u5728\u4ed6\u4eec\u7684\u8bba\u6587\u4e2d\uff0c\u4f5c\u8005\u6d4b\u8bd5\u4e86\u4e0d\u540c\u764c\u75c7\u76f8\u5173\u6570\u636e\u96c6\u7684\u65b9\u6cd5\uff0c\u6210\u529f\u5730\u4ece\u86cb\u767d\u8d28\u7ec4\u5b66\u6570\u636e\u4e2d\u8bc6\u522b\u4e86\u4e0d\u540c\u914d\u4f53\u548c\u836f\u7269\u7684\u4f5c\u7528\u673a\u5236\u3002<\/span><\/p>\n


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\u56fe3. CausalPath <\/em>\u65b9\u6cd5\u7ed3\u5408\u6765\u81ea\u79d1\u5b66\u6587\u732e\u4e2d\u5df2\u6709\u7684\u5173\u4e8e\u751f\u7269\u901a\u8def\u7684\u8be6\u7ec6\u77e5\u8bc6\u4e0e\u6700\u65b0\u83b7\u5f97\u7684\u86cb\u767d\u8d28\u7ec4\u5b66\u548c\u5176\u4ed6\u5206\u5b50\u6d4b\u91cf\u6570\u636e\uff0c\u751f\u6210\u673a\u5236\u6a21\u578b\u6765\u89e3\u91ca\u89c2\u6d4b\u5230\u7684\u53d8\u5316\u5982\u4f55\u5f7c\u6b64\u5173\u8054\u3002<\/span><\/p>\n


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\u5f53\u4ed6\u4eec\u5c06\u57fa\u56e0\u8c03\u63a7\u7f51\u7edc\u4e0e\u86cb\u767d\u8d28\u7ec4\u5b66<\/strong>\u6570\u636e\u7ed3\u5408\u4f7f\u7528\u65f6\uff0c\u63a8\u65ad\u51fa\u7684\u56e0\u679c\u7f51\u7edc\u5728\u7edf\u8ba1\u5b66\u610f\u4e49\u4e0a\u5e76\u4e0d\u663e\u8457\uff0c\u800c\u5c06\u76f8\u540c\u7684\u5148\u9a8c\u77e5\u8bc6\u7f51\u7edc\u4e0e\u57fa\u56e0\u8868\u8fbe<\/strong>\u6570\u636e\u7ed3\u5408\u4f7f\u7528\uff0c\u4f1a\u5bfc\u81f4\u66f4\u52a0\u663e\u8457\u7684\u56e0\u679c\u5173\u8054\u3002\u8fd9\u4e9b\u7ed3\u679c\u8fd8\u7a81\u51fa\u4e86\u76ee\u524d\u7cfb\u7edf\u751f\u7269\u5b66\u5efa\u6a21\u7684\u4e00\u4e2a\u666e\u904d\u5b58\u5728\u7684\u95ee\u9898\uff1a\u9274\u4e8e\u8f6c\u5f55\u7ec4\u5b66\u6570\u636e\u96c6\u7684\u4e30\u5ea6\u66f4\u9ad8<\/span>\uff08\u4f8b\u5982\uff0c\u4e0e\u78f7\u9178\u86cb\u767d\u8d28\u7ec4\u5b66\u76f8\u6bd4\uff09<\/span>\uff0c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u66f4\u5e38\u7528\u4e8e\u5efa\u6a21\u7814\u7a76\u3002\u7136\u800c\uff0c\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\uff0c\u4f7f\u7528\u7684\u5148\u9a8c\u77e5\u8bc6\u7f51\u7edc\u662f\u5728\u86cb\u767d\u8d28\u6d3b\u52a8<\/strong>\u7684\u6c34\u5e73\u4e0a\u5b9a\u4e49\u7684\u3002\u7531\u4e8e\u57fa\u56e0\u8868\u8fbe\u4e0e\u86cb\u767d\u8d28\u4e30\u5ea6\u53ca\u6d3b\u6027\u4e4b\u95f4\u7684\u5173\u8054\u53ef\u80fd\u4e0d\u5927\uff0c\u56e0\u6b64\u5c06\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u4e0e\u901a\u8def\u7f51\u7edc\u7ed3\u5408\u4f7f\u7528\uff0c\u53ef\u80fd\u4f1a\u5bf9\u7ed3\u679c\u7ed9\u51fa\u9519\u8bef\u89e3\u91ca\u3002<\/span><\/p>\n


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\u5f53\u7136\uff0c\u968f\u7740\u8d8a\u6765\u8d8a\u591a\u7684\u76f8\u5173\u5de5\u5177\u88ab\u5f00\u53d1\u51fa\u6765\uff0c\u6267\u884c\u65e0\u504f\u89c1\u3001\u72ec\u7acb\u7684\u57fa\u51c6\u6d4b\u8bd5\u81f3\u5173\u91cd\u8981\u3002\u57fa\u51c6\u6d4b\u8bd5\u7684\u4e00\u4e2a\u91cd\u8981\u74f6\u9888\u662f\u56e0\u679c\u5173\u7cfb\u5df2\u77e5\u7684\u9ad8\u8d28\u91cf\u6570\u636e\u96c6\u3002\u4e3a\u6b64\uff0c\u6270\u52a8\u6570\u636e\u96c6<\/span>\uff08\u53d8\u5316\u7684\u4e00\u822c\u539f\u56e0\u5df2\u7531\u4f7f\u7528\u7684\u6270\u52a8\u6240\u7ed9\u51fa\uff0c\u4f8b\u5982\u836f\u7269\u3001\u57fa\u56e0\u64cd\u4f5c\u7b49\uff09<\/span>\u770b\u8d77\u6765\u6700\u4e3a\u5408\u9002\uff0c\u4f46\u6270\u52a8\u7684\u504f\u79bb\u76ee\u6807\u6548\u5e94\u4f1a\u4f7f\u65b9\u6cd5\u7684\u8bc4\u4f30\u590d\u6742\u5316\u3002\u5c3d\u7ba1\u5982\u6b64\uff0c\u8bf8\u5982DREAM\u6311\u6218<\/span>\uff08Dialogue on Reverse Engineering Assessment and Methods Challenges\uff09<\/span>\u7b49\u5927\u578b\u57fa\u51c6\u6d4b\u8bd5\u9879\u76ee\uff0c\u53ef\u4ee5\u4fc3\u8fdb\u672a\u6765\u56e0\u679c\u63a8\u7406\u7cfb\u7edf\u751f\u7269\u5b66\u5de5\u5177\u7684\u5f00\u53d1\u4e0e\u8bc4\u4f30\u3002<\/span><\/p>\n


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1. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., and Mesirov, J.P. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545\u201315550.<\/span><\/p>\n

2. Eid, F.-E., Elmarakeby, H.A., Chan, Y.A., Fornelos, N., ElHefnawi, M., Van Allen, E.M., Heath, L.S., and Lage, K. (2021). Systematic auditing is essential to debiasing machine learning in biology. Commun Biol 4, 183.<\/span><\/p>\n

3. Bradley, G., and Barrett, S.J. (2017). CausalR: extracting mechanistic sense from genome scale data. Bioinformatics 33, 3670\u20133672.<\/span><\/p>\n

4. Liu, A., Trairatphisan, P., Gjerga, E., Didangelos, A., Barratt, J., and Saez- Rodriguez, J. (2019). From expression foot-prints to causal pathways: contextualizing large signaling networks with CARNIVAL. NPJ Syst. Biol. Appl. 5, 40.<\/span><\/p>\n

5. Paull, E.O., Carlin, D.E., Niepel, M., Sorger, P.K., Haussler, D., and Stuart, J.M. (2013). Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE). Bioinformatics 29, 2757\u20132764.<\/span><\/p>\n

6. Babur, O \u0308 ., Luna, A., Korkut, A., Durupinar, F., Siper, M.C., Dogrusoz, U., Vaca Jacome, A.S., Peckner, R., Christianson, K.E., Jaffe, et al. (2021). Causal interactions from proteomic profiles: Molecular data meet pathway knowledge. Patterns 2. https:\/\/doi.org\/10. 1016\/j.patter.2021.100257.<\/span><\/p>\n

7. Szalai, B., and Saez-Rodriguez, J. (2020). Why do pathway methods work better than they should? FEBS Lett. 594, 4189\u20134200.
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8. Dugourd, A., Kuppe, C., Sciacovelli, M., Gjerga, E., Gabor, A., Emdal, K.B., Vieira, V., Bekker-Jensen, D.B., Kranz, J., Bindels, E.M.J., et al. (2021). Causal integration of multiomics data with prior knowledge to generate mechanistic hypotheses. Mol. Syst. Biol. 17, e9730.<\/span><\/p>\n

9. Keenan, A.B., Jenkins, S.L., Jagodnik, K.M., Koplev, S., He, E., Torre, D., Wang, Z., Dohlman, A.B., Silverstein, M.C., Lachmann, A., et al. (2018). The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Syst. 6, 13\u201324.<\/span><\/p>\n

10. Gabor, A.,Tanevski, J., Guo, B., Cao, W., Shen, H., Yu, T., Chung, V., Signaling, S.C., et al. (2021). Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd. bioRxiv. https:\/\/doi.org\/10.1101\/ 2021.03.23.436603.<\/span><\/p>\n


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\u539f\u6587\u94fe\u63a5\uff1a<\/span><\/section>\n
https:\/\/www.cell.com\/patterns\/fulltext\/S2666-3899(21)00113-6<\/span><\/section>\n


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\u56e0\u679c\u79d1\u5b66\u8bfb\u4e66\u4f1a\u7b2c\u4e09\u5b63\u542f\u52a8<\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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\u8be6\u60c5\u8bf7\u89c1\uff1a<\/span><\/p>\n

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\u63a8\u8350\u9605\u8bfb<\/span><\/strong><\/strong><\/p>\n