Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.
Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.
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有条件的相互包容信息能够准确量化基因调控网络中的关联
DOI：
10.1093/nar/gku1315
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发表时间：
2015-03-11
影响因子：
14.9
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Zhang X;Zhao J;Hao JK;Zhao XM;Chen L
互信息是描述两个随机变量之间非线性关系的一个量，已被广泛用于构建基因调控网络。尽管MI表现良好，但它不能将基因间的直接调节和间接调节分开。尽管条件互信息（CMI）能够识别直接调控，但它通常低估了调控强度，即在推断基因调控时可能导致假阴性。在这项工作中，为了克服这些问题，我们提出了一个新的概念，即条件互包含信息（CMI 2），来描述基因之间的规定。此外，与CMI 2，我们开发了一种新的方法，即CMI 2NI（基于CMI 2的网络推理），逆向工程GRNs。在CMI 2NI中，CMI 2被用来量化两个基因之间的互信息，给定第三个基因，通过计算包含和排除两个基因之间的边缘的假设分布之间的Kullback-Leibler分歧。在DREAM挑战的GRNs以及大肠杆菌中的SOS DNA修复网络上的基准测试结果证明了CMI 2NI的上级性能。具体而言，即使对于小样本量的基因表达数据，CMI 2NI不仅可以推断出调控网络的正确拓扑结构，还可以准确地量化基因之间的调控强度。作为一个案例研究，CMI 2NI也被用于使用来自癌症基因组图谱（TCGA）的基因表达数据重建癌症特异性GRNs。CMI 2NI可在http://www.comp-sysbio.org/cmi2ni上免费访问。
Mutual information (MI), a quantity describing the nonlinear dependence between two random variables, has been widely used to construct gene regulatory networks (GRNs). Despite its good performance, MI cannot separate the direct regulations from indirect ones among genes. Although the conditional mutual information (CMI) is able to identify the direct regulations, it generally underestimates the regulation strength, i.e. it may result in false negatives when inferring gene regulations. In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs. In CMI2NI, CMI2 is used to quantify the mutual information between two genes given a third one through calculating the Kullback–Leibler divergence between the postulated distributions of including and excluding the edge between the two genes. The benchmark results on the GRNs from DREAM challenge as well as the SOS DNA repair network in Escherichia coli demonstrate the superior performance of CMI2NI. Specifically, even for gene expression data with small sample size, CMI2NI can not only infer the correct topology of the regulation networks but also accurately quantify the regulation strength between genes. As a case study, CMI2NI was also used to reconstruct cancer-specific GRNs using gene expression data from The Cancer Genome Atlas (TCGA). CMI2NI is freely accessible at http://www.comp-sysbio.org/cmi2ni.
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影响因子：
5.8
作者：
Kueffner, Robert;Petri, Tobias;Zimmer, Ralf
通讯作者：
Zimmer, Ralf
影响因子：
14.9
作者：
Hong S;Chen X;Jin L;Xiong M
通讯作者：
Xiong M
影响因子：
64.8
作者：
Chin, L.;Meyerson, M.;Aldape, K.;Bigner, D.;Mikkelsen, T.;VandenBerg, S.;Kahn, A.;Penny, R.;Ferguson, M. L.;Gerhard, D. S.;Getz, G.;Brennan, C.;Taylor, B. S.;Winckler, W.;Park, P.;Ladanyi, M.;Hoadley, K. A.;Verhaak, R. G. W.;Hayes, D. N.;Spellman, Paul T.;Absher, D.;Weir, B. A.;Ding, L.;Wheeler, D.;Lawrence, M. S.;Cibulskis, K.;Mardis, E.;Zhang, Jinghui;Wilson, R. K.;Donehower, L.;Wheeler, D. A.;Purdom, E.;Wallis, J.;Laird, P. W.;Herman, J. G.;Schuebel, K. E.;Weisenberger, D. J.;Baylin, S. B.;Schultz, N.;Yao, Jun;Wiedemeyer, R.;Weinstein, J.;Sander, C.;Gibbs, R. A.;Gray, J.;Kucherlapati, R.;Lander, E. S.;Myers, R. M.;Perou, C. M.;McLendon, Roger;Friedman, Allan;Van Meir, Erwin G;Brat, Daniel J;Mastrogianakis, Gena Marie;Olson, Jeffrey J;Lehman, Norman;Yung, W. K. Alfred;Bogler, Oliver;Berger, Mitchel;Prados, Michael;Muzny, Donna;Morgan, Margaret;Scherer, Steve;Sabo, Aniko;Nazareth, Lynn;Lewis, Lora;Hall, Otis;Zhu, Yiming;Ren, Yanru;Alvi, Omar;Yao, Jiqiang;Hawes, Alicia;Jhangiani, Shalini;Fowler, Gerald;San Lucas, Anthony;Kovar, Christie;Cree, Andrew;Dinh, Huyen;Santibanez, Jireh;Joshi, Vandita;Gonzalez-Garay, Manuel L.;Miller, Christopher A.;Milosavljevic, Aleksandar;Sougnez, Carrie;Fennell, Tim;Mahan, Scott;Wilkinson, Jane;Ziaugra, Liuda;Onofrio, Robert;Bloom, Toby;Nicol, Rob;Ardlie, Kristin;Baldwin, Jennifer;Gabriel, Stacey;Fulton, Robert S.;McLellan, Michael D.;Larson, David E.;Shi, Xiaoqi;Abbott, Rachel;Fulton, Lucinda;Chen, Ken;Koboldt, Daniel C.;Wendl, Michael C.;Meyer, Rick;Tang, Yuzhu;Lin, Ling;Osborne, John R.;Dunford-Shore, Brian H.;Miner, Tracie L.;Delehaunty, Kim;Markovic, Chris;Swift, Gary;Courtney, William;Pohl, Craig;Abbott, Scott;Hawkins, Amy;Leong, Shin;Haipek, Carrie;Schmidt, Heather;Wiechert, Maddy;Vickery, Tammi;Scott, Sacha;Dooling, David J.;Chinwalla, Asif;Weinstock, George M.;O'Kelly, Michael;Robinson, Jim;Alexe, Gabriele;Beroukhim, Rameen;Carter, Scott;Chiang, Derek;Gould, Josh;Gupta, Supriya;Korn, Josh;Mermel, Craig;Mesirov, Jill;Monti, Stefano;Nguyen, Huy;Parkin, Melissa;Reich, Michael;Stransky, Nicolas;Garraway, Levi;Golub, Todd;Protopopov, Alexei;Perna, Ilana;Aronson, Sandy;Sathiamoorthy, Narayan;Ren, Georgia;Kim, Hyunsoo;Kong, Sek Won;Xiao, Yonghong;Kohane, Isaac S.;Seidman, Jon;Cope, Leslie;Pan, Fei;Van Den Berg, David;Van Neste, Leander;Yi, Joo Mi;Li, Jun Z.;Southwick, Audrey;Brady, Shannon;Aggarwal, Amita;Chung, Tisha;Sherlock, Gavin;Brooks, James D.;Jakkula, Lakshmi R.;Lapuk, Anna V.;Marr, Henry;Dorton, Shannon;Choi, Yoon Gi;Han, Ju;Ray, Amrita;Wang, Victoria;Durinck, Steffen;Robinson, Mark;Wang, Nicholas J.;Vranizan, Karen;Peng, Vivian;Van Name, Eric;Fontenay, Gerald V.;Ngai, John;Conboy, John G.;Parvin, Bahram;Feiler, Heidi S.;Speed, Terence P.;Socci, Nicholas D.;Olshen, Adam;Lash, Alex;Reva, Boris;Antipin, Yevgeniy;Stukalov, Alexey;Gross, Benjamin;Cerami, Ethan;Wang, Wei Qing;Qin, Li-Xuan;Seshan, Venkatraman E.;Villafania, Liliana;Cavatore, Magali;Borsu, Laetitia;Viale, Agnes;Gerald, William;Topal, Michael D.;Qi, Yuan;Balu, Sai;Shi, Yan;Wu, George;Bittner, Michael;Shelton, Troy;Lenkiewicz, Elizabeth;Morris, Scott;Beasley, Debbie;Sanders, Sheri;Sfeir, Robert;Chen, Jessica;Nassau, David;Feng, Larry;Hickey, Erin;Schaefer, Carl;Madhavan, Subha;Buetow, Ken;Barker, Anna;Vockley, Joseph;Compton, Carolyn;Vaught, Jim;Fielding, Peter;Collins, Francis;Good, Peter;Guyer, Mark;Ozenberger, Brad;Peterson, Jane;Thomson, Elizabeth
通讯作者：
Thomson, Elizabeth
影响因子：
64.5
作者：
Chen JC;Alvarez MJ;Talos F;Dhruv H;Rieckhof GE;Iyer A;Diefes KL;Aldape K;Berens M;Shen MM;Califano A
通讯作者：
Califano A
影响因子：
5.8
作者：
Brunel, Helena;Gallardo-Chacon, Joan-Josep;Perera, Alexandre
通讯作者：
Perera, Alexandre