Comput. Biol. Med. - Missing value imputation in DNA microarrays based on conjugate gradient method.

Tópicos

{ error(1145) method(1030) estim(1020) }
{ featur(3375) classif(2383) classifi(1994) }
{ algorithm(1844) comput(1787) effici(935) }
{ gene(2352) biolog(1181) express(1162) }
{ imag(2830) propos(1344) filter(1198) }
{ measur(2081) correl(1212) valu(896) }
{ data(3008) multipl(1320) sourc(1022) }
{ survey(1388) particip(1329) question(1065) }
{ problem(2511) optim(1539) algorithm(950) }
{ studi(1410) differ(1259) use(1210) }
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{ data(1737) use(1416) pattern(1282) }
{ imag(1057) registr(996) error(939) }
{ network(2748) neural(1063) input(814) }
{ learn(2355) train(1041) set(1003) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ method(984) reconstruct(947) comput(926) }
{ record(1888) medic(1808) patient(1693) }
{ group(2977) signific(1463) compar(1072) }
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Resumo

Analysis of gene expression profiles needs a complete matrix of gene array values; consequently, imputation methods have been suggested. In this paper, an algorithm that is based on conjugate gradient (CG) method is proposed to estimate missing values. k-nearest neighbors of the missed entry are first selected based on absolute values of their Pearson correlation coefficient. Then a subset of genes among the k-nearest neighbors is labeled as the best similar ones. CG algorithm with this subset as its input is then used to estimate the missing values. Our proposed CG based algorithm (CGimpute) is evaluated on different data sets. The results are compared with sequential local least squares (SLLSimpute), Bayesian principle component analysis (BPCAimpute), local least squares imputation (LLSimpute), iterated local least squares imputation (ILLSimpute) and adaptive k-nearest neighbors imputation (KNNKimpute) methods. The average of normalized root mean squares error (NRMSE) and relative NRMSE in different data sets with various missing rates shows CGimpute outperforms other methods.

Resumo Limpo

analysi gene express profil need complet matrix gene array valu consequ imput method suggest paper algorithm base conjug gradient cg method propos estim miss valu knearest neighbor miss entri first select base absolut valu pearson correl coeffici subset gene among knearest neighbor label best similar one cg algorithm subset input use estim miss valu propos cg base algorithm cgimput evalu differ data set result compar sequenti local least squar sllsimput bayesian principl compon analysi bpcaimput local least squar imput llsimput iter local least squar imput illsimput adapt knearest neighbor imput knnkimput method averag normal root mean squar error nrmse relat nrmse differ data set various miss rate show cgimput outperform method

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