J Integr Bioinform - Transcription network analysis by a sparse binary factor analysis algorithm.

Tópicos

{ network(2748) neural(1063) input(814) }
{ gene(2352) biolog(1181) express(1162) }
{ method(1557) propos(1049) approach(1037) }
{ analysi(2126) use(1163) compon(1037) }
{ studi(1119) effect(1106) posit(819) }
{ imag(1947) propos(1133) code(1026) }
{ data(1714) softwar(1251) tool(1186) }
{ control(1307) perform(991) simul(935) }
{ learn(2355) train(1041) set(1003) }
{ motion(1329) object(1292) video(1091) }
{ method(984) reconstruct(947) comput(926) }
{ studi(1410) differ(1259) use(1210) }
{ import(1318) role(1303) understand(862) }
{ compound(1573) activ(1297) structur(1058) }
{ sampl(1606) size(1419) use(1276) }
{ structur(1116) can(940) graph(676) }
{ activ(1452) weight(1219) physic(1104) }
{ model(3404) distribut(989) bayesian(671) }
{ system(1976) rule(880) can(841) }
{ sequenc(1873) structur(1644) protein(1328) }
{ take(945) account(800) differ(722) }
{ studi(2440) review(1878) systemat(933) }
{ algorithm(1844) comput(1787) effici(935) }
{ howev(809) still(633) remain(590) }
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{ state(1844) use(1261) util(961) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Transcription factor activities (TFAs), rather than expression levels, control gene expression and provide valuable information for investigating TF-gene regulations. The underlying bimodal or switch-like patterns of TFAs may play important roles in gene regulation. Network Component Analysis (NCA) is a popular method to deduce TFAs and TF-gene control strengths from microarray data. However, it does not directly examine the bimodality of TFAs and it needs TF-gene connection topology a priori known. In this paper, we modify NCA to model gene expression regulation by Binary Factor Analysis (BFA), which directly captures switch-like patterns of TFAs. Moreover, sparse technique is employed on the mixing matrix of BFA, and thus the proposed sparse BYY-BFA algorithm, developed under Bayesian Ying-Yang (BYY) learning framework, can not only uncover the latent TFA profile’s switch-like patterns, but also be capable of automatically shutting off the unnecessary connections. Simulation study demonstrates the effectiveness of BYY-BFA, and a preliminary application to Saccharomyces cerevisiae cell cycle data and Escherichia coli carbon source transition data shows that the reconstructed binary patterns of TFAs by BYY-BFA are consistent with the ups and downs of TFAs by NCA, and that BYY-BFA also works well when the network topology is unknown.

Resumo Limpo

transcript factor activ tfas rather express level control gene express provid valuabl inform investig tfgene regul under bimod switchlik pattern tfas may play import role gene regul network compon analysi nca popular method deduc tfas tfgene control strength microarray data howev direct examin bimod tfas need tfgene connect topolog priori known paper modifi nca model gene express regul binari factor analysi bfa direct captur switchlik pattern tfas moreov spars techniqu employ mix matrix bfa thus propos spars byybfa algorithm develop bayesian yingyang byy learn framework can uncov latent tfa profilersquo switchlik pattern also capabl automat shut unnecessari connect simul studi demonstr effect byybfa preliminari applic saccharomyc cerevisia cell cycl data escherichia coli carbon sourc transit data show reconstruct binari pattern tfas byybfa consist up down tfas nca byybfa also work well network topolog unknown

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