{"id":37778,"date":"2022-10-10T16:06:19","date_gmt":"2022-10-10T08:06:19","guid":{"rendered":"https:\/\/swarma.org\/?p=37778"},"modified":"2022-10-10T16:06:19","modified_gmt":"2022-10-10T08:06:19","slug":"%e5%a4%8d%e6%9d%82%e7%b3%bb%e7%bb%9f%e7%9a%84%e9%80%86%e5%90%91%e5%b7%a5%e7%a8%8b-%e9%80%9a%e8%bf%87%e6%97%b6%e9%97%b4%e5%ba%8f%e5%88%97%e9%87%8d%e6%9e%84%e5%a4%8d%e6%9d%82%e7%bd%91","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=37778","title":{"rendered":"\u590d\u6742\u7cfb\u7edf\u7684\u9006\u5411\u5de5\u7a0b\u2014\u2014\u901a\u8fc7\u65f6\u95f4\u5e8f\u5217\u91cd\u6784\u590d\u6742\u7f51\u7edc\u548c\u52a8\u529b\u5b66"},"content":{"rendered":"
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\u5728\u8fd1\u65e5\u53d1\u8868\u4e8e Physical Review E <\/em>\u7684\u4e00\u9879\u6700\u65b0\u7814\u7a76\u4e2d\uff0c\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7cfb\u7edf\u79d1\u5b66\u5b66\u9662\u5f20\u6c5f\u56e2\u961f\u63d0\u51fa\u4e86\u4e00\u4e2a\u901a\u7528\u6846\u67b6\uff0c\u7528\u89c2\u5bdf\u5230\u7684\u8282\u70b9\u65f6\u95f4\u5e8f\u5217\u6570\u636e\u6765\u9006\u5411\u5de5\u7a0b\u590d\u6742\u7cfb\u7edf\u7684\u7f51\u7edc\u7ed3\u6784\u548c\u52a8\u529b\u5b66\u3002\u7814\u7a76\u4f7f\u7528\u53ef\u5fae\u5206\u4f2f\u52aa\u5229\u91c7\u6837\u8fc7\u7a0b\u6765\u751f\u6210\u5019\u9009\u7f51\u7edc\u7ed3\u6784\uff0c\u4f7f\u7528\u795e\u7ecf\u7f51\u7edc\u6765\u6a21\u62df\u57fa\u4e8e\u5019\u9009\u7f51\u7edc\u7684\u8282\u70b9\u52a8\u529b\u5b66\uff1b\u7136\u540e\u4f7f\u7528\u968f\u673a\u68af\u5ea6\u4e0b\u964d\u7b97\u6cd5\u8c03\u6574\u6240\u6709\u53c2\u6570\uff0c\u4ee5\u6700\u5927\u5316\u5728\u6570\u636e\u4e0a\u5b9a\u4e49\u7684\u4f3c\u7136\u51fd\u6570\u3002\u5b9e\u9a8c\u8868\u660e\uff0c\u8be5\u6a21\u578b\u53ef\u4ee5\u540c\u65f6\u9ad8\u7cbe\u5ea6\u5730\u6062\u590d\u5404\u79cd\u7f51\u7edc\u7ed3\u6784\u548c\u8282\u70b9\u52a8\u529b\u5b66\uff0c\u8fd8\u53ef\u4ee5\u5f88\u597d\u5730\u5904\u7406\u4e8c\u8fdb\u5236\u3001\u79bb\u6563\u548c\u8fde\u7eed\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u5e76\u4e14\u91cd\u5efa\u7ed3\u679c\u5bf9\u566a\u58f0\u548c\u4e22\u5931\u4fe1\u606f\u5177\u6709\u9c81\u68d2\u6027\u3002\u4e3a\u4e86\u9a8c\u8bc1\u7b97\u6cd5\u80fd\u591f\u5b66\u4e60\u5230\u6b63\u786e\u7684\u56e0\u679c\u5173\u7cfb\uff0c\u8be5\u7814\u7a76\u9996\u6b21\u63d0\u51fa\u5229\u7528\u63a7\u5236\u4efb\u52a1\u5bf9\u6bd4\u5b66\u4e60\u5230\u7684\u66ff\u4ee3\u7cfb\u7edf\u548c\u771f\u5b9e\u7cfb\u7edf\u3002\u4ee5\u4e0b\u662f\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\u3001\u5317\u4eac\u5e08\u8303\u5927\u5b66\u7cfb\u7edf\u79d1\u5b66\u5b66\u9662\u7855\u58eb\u751f\u5f20\u598d\u5bf9\u8be5\u7814\u7a76\u7684\u89e3\u8bfb\u3002<\/span><\/p>\n


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Universal framework for reconstructing complex networks and node dynamics from discrete or continuous dynamics data<\/span><\/p>\n

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https:\/\/journals.aps.org\/pre\/abstract\/10.1103\/PhysRevE.106.034315<\/p>\n<\/section>\n<\/section>\n<\/blockquote>\n


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\u56fe2. \u6a21\u578b\u6846\u67b6\u7684\u6784\u9020\uff1a\u7f51\u7edc\u751f\u6210\u5668\uff08Network Generator\uff09\u4f7f\u7528 Gumbel softmax \u6280\u672f\u751f\u6210\u90bb\u63a5\u77e9\u9635\uff0c\u52a8\u529b\u5b66\u5b66\u4e60\u5668\uff08Dynamics Learner\uff09\u7531\u8282\u70b9\u548c\u8fde\u8fb9\u4e4b\u95f4\u7684\u56db\u5c42\u6620\u5c04\u7ec4\u6210\uff0c\u901a\u8fc7\u90bb\u63a5\u77e9\u9635\u628a\u6240\u6709\u8282\u70b9\u7684\u72b6\u6001\u7531 \ud835\udc61 \u65f6\u523b\u6620\u5c04\u5230 \ud835\udc61 + 1 \u65f6\u523b\u3002<\/span><\/section>\n


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3. \u7f51\u7edc\u91cd\u6784\u548c\u8282\u70b9\u52a8\u529b\u5b66\u9884\u6d4b<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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<\/p>\n

\u56fe3. \u9c81\u68d2\u6027\u63a2\u7a76\uff1a\u90e8\u5206\u8282\u70b9\u7f3a\u5931\u7684\u7f51\u7edc\u4e0a\u7684\u5b9e\u9a8c<\/span><\/p>\n


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\u4e3a\u4e86\u9a8c\u8bc1\u8be5\u7b97\u6cd5\u662f\u5426\u53ef\u4ee5\u5e94\u7528\u4e8e\u5b9e\u9645\u573a\u666f\uff0c\u7814\u7a76\u8005\u5c1d\u8bd5\u6839\u636e GeneNetWeaver \u751f\u6210\u7684 mRNA \u6d53\u5ea6\u7684\u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff0c\u4ece\u5df2\u77e5\u7684\u917f\u9152\u9175\u6bcd\u8f6c\u5f55\u7f51\u7edc\u4e2d\u63a8\u65ad\u51fa\u771f\u5b9e\u7684\u5b50\u7f51\u7edc\u7ed3\u6784\u3002\u5728\u5b9e\u9a8c\u4e2d\uff0c\u7814\u7a76\u8005\u4f7f\u7528100\u4e2a\u8282\u70b9\u7684\u9175\u6bcd S. cerevisiae<\/em> \u57fa\u56e0\u7f51\u7edc\u4f5c\u4e3a\u57fa\u51c6\u57fa\u56e0\u7f51\u7edc\uff0c\u5e76\u4f7f\u7528 GeneNetWeaver \u8f6f\u4ef6\u4e2d DREAM4_In-Silico \u7684\u9ed8\u8ba4\u53c2\u6570\u751f\u6210\u6570\u636e\u3002\u5bf9\u4e8e\u52a8\u6001\u6a21\u62df\u5668\uff0c\u4e3a\u6bcf\u4e2a\u8282\u70b9\u4f7f\u7528\u4e0d\u540c\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u56e0\u4e3a\u8282\u70b9\u52a8\u6001\u7684\u5f02\u8d28\u6027\u4ee5\u53ca\u6f5c\u5728\u53d8\u91cf\u3001\u566a\u58f0\u548c\u6270\u52a8\u7684\u5b58\u5728\u3002<\/span><\/p>\n


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4. \u603b\u7ed3\u548c\u5c55\u671b<\/span><\/strong><\/strong><\/p>\n<\/section>\n

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\u53c2\u8003\u6587\u732e<\/span><\/strong><\/span><\/h1>\n
1. S. L\u00f6we, D. Madras, R. Zemel, and M. Welling, Amortized causal discovery: Learning to infer causal graphs from time- series data, Proc. Mach. Learn. Res. 177, 509 (2022).<\/span><\/section>\n
2. J. Casadiego, M. Nitzan, S. Hallerberg, and M. Timme, Model-free inference of direct network interactions from nonlinear collective dynamics, Nat. Commun. 8, 2192 (2017).<\/span><\/section>\n
3. T. Kipf, E. Fetaya, K.-C. Wang, M. Welling, and R. Zemel, Neu- ral relational inference for interacting systems, in Proceedings of the International Conference on Machine Learning (PMLR, 2018), pp. 2688\u20132697.<\/span><\/section>\n
4. A. Sanchez-Gonzalez, N. Heess, J. T. Springenberg, J. Merel, M. Riedmiller, R. Hadsell, and P. Battaglia, Graph networks as learnable physics engines for inference and control, in Pro- ceedings of the International Conference on Machine Learning (PMLR, 2018), pp. 4470\u20134479.<\/span><\/section>\n
5. T. Schaffter, D. Marbach, and D. Floreano, Genenetweaver: In silico benchmark generation and performance profiling of network inference methods, Bioinformatics 27, 2263 (2011).<\/span><\/section>\n
6. M. Chen, J. Zhang, Z. Zhang, L. Du, Q. Hu, S. Wang, and J. Zhu, Inferring network structure with unobservable nodes from time series data, Chaos: An Interdis, J. Nonlin. Sci. 32, 013126 (2022).<\/span><\/section>\n

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