Bayesian methods for elucidating genetic regulatory networks

bayesian methods for elucidating genetic regulatory networks-36
We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.

Within the last few years a number of sophisticated approaches for the reverse engineering of cellular networks (also called deconvolution) from gene expression data have emerged (reviewed in []).We also study the effects of misestimation of mutual information (MI) on network reconstruction, and show that algorithms based on MI ranking are resilient to estimation errors.The algorithm is general enough to deal with a variety of other network reconstruction problems in biological, social, and engineering fields.This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.Cellular phenotypes are determined by the dynamical activity of large networks of co-regulated genes.Compounding this constraint, there is no universally accepted definition of statistical dependencies in the multivariate setting [Math [email protected]@[email protected]@ =feaafiart1ev1aaat Cv AUf Ktt Learu Wr P9MDH5MBPb Iq V92Aae Xat Lx BI9g Baebbnrfif Hh DYfgasaac H8ak Y=wi Ff Yd H8Gipec8Eeeu0x Xdbba9fr Fj0=Oq Ffea0d Xdd9vqai=h Gu Q8kuc9pgc9s8qqaq=dirpe0xb9q8qi Ls Fr0=vr0=vr0dc8meaabaqaciaaca Gaaeqabaqabe Gadaaakeaacq WGqbaudaqadaqaamaacmaaba Gaem4za C2aa Sbaa Sqaaiabd Mga Pbqabaaakiaaw Uhaca GL9baaaiaaw Icaca GLPaaacq GH9aqpda Wcaaqaaiabigda Xaqaaiabd Qfa Abaacy GGLbqzcq GG4ba Ecq GGWba Cda Wadaqaaiabgk Hi Tmaaqahabaacci Gae8NXdy2aa Sbaa Sqaaiabd Mga Pbqaba Gcdaqadaqaaiabd Ega Nnaa Baaaleaacq WGPbq Aaeqaaa Gcca GLOa Gaayzkaa Gaey Oe I0Yaaab Caeaacq WFgp Gzda Wgaa Wcba Gaemy Aa KMaem OAa Ogabeaakmaabmaaba Gaem4za C2aa Sbaa Sqaaiabd Mga Pbqaba Gccq GGSaalcq WGNb Wzda Wgaa Wcba Gaem OAa Ogabeaaa OGaayjkaiaaw Mcaaiabgk Hi Tmaaqahaba Gae8NXdy2aa Sbaa Sqaaiabd Mga Pjabd Qga Qjabd Uga Rbqabaaaba Gaemy Aa KMaeiila WIaem OAa OMaeiila WIaem4Aa Sgaba Gaem Ota4eaniabgg Hi Ld Gcdaqadaqaaiabd Ega Nnaa Baaaleaacq WGPbq Aaeqaa OGaeiila WIaem4za C2aa Sbaa Sqaaiabd Qga Qbqaba Gccq GGSaalcq WGNb Wzda Wgaa Wcba Gaem4Aa Sgabeaaa OGaayjkaiaaw Mcaaiabgk Hi Tiabc6ca Uiabc6ca Uiabc6ca Ua Wcba Gaemy Aa KMaeiila WIaem OAa Ogaba Gaem Ota4eaniabgg Hi Ldaaleaacq WGPbq Aaeaacq WGob Gta0Gaeyye Iuoaa OGaay5waiaaw2faaiabgg Mi6Iqadiab vga Lnaa Caaaleqaba Gaey Oe I0ccbi Gae0hsa G0aae Waaeaada Gadaqaaiab9Dga Nnaa Baaameaacqq FPbq Aaeqaaa Wcca GL7b Gaayz Faaaaca GLOa Gaayzkaaaaa OGaa Czcaiaax Maadaqadaqaaiabigda Xa Gaayjkaiaaw [email protected]@) the single potential that depends exclusively on these variables is nonzero.ARACNE aims precisely at identifying which of these potentials are nonzero, and eliminating the others even though their corresponding marginal JPDs may not factorize.This precludes the use of methods that infer temporal associations and thus plausible causal relationships (reviewed in []).Only steady state statistical dependences can be studied, which are not obviously linked to the underlying physical dependency model.On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks.Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the c MYC proto-oncogene.

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