J. Comput. Biol. - Prediction of siRNA potency using sparse logistic regression.

Tópicos

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Resumo

RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.

Resumo Limpo

rna interfer rnai can modul gene express posttranscript well transcript level short interf rna sirna serv trigger rnai gene inhibit mechan therefor crucial intermedi step rnai extens studi identifi sequenc characterist potent sirna one studi built linear model use lasso least absolut shrinkag select oper measur contribut sirna sequenc featur model simpl interpret requir larg number nonzero weight introduc novel techniqu spars logist regress build linear model use singleposit specif nucleotid composit predict accuraci linear model base lasso weight new model share general trend previous model nonzero weight total weight reduct compar previous model contrari linear model base lasso model suggest posit influenti efficaci sirna end seed region sirna sequenc also employ spars logist regress build linear model use dualposit specif nucleotid composit task lasso abl accomplish well due high dimension natur result demonstr superior spars logist regress techniqu featur select regress lasso context sirna design

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