Signaling effectors regulated by the identified differentially expressed miRs r

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 Signaling effectors regulated by the identified differentially expressed miRs r Empty Signaling effectors regulated by the identified differentially expressed miRs r

Post  wangqian on Thu Apr 03, 2014 6:51 am

The above assumption of direct correlation for all successful drugs is obviously an extremely restrictive assumption and will be unable to produce high accu racy predictions. Thus, the binarization scheme has to be modified to incorporate the following three AP24534 臨床試験 factors: noises in varying magnitude will be present in the drug screen data generated by our collaborators. The noise is unavoidable, and as such, needs to be accounted for. In addition, despite the high accuracy of the drug protein interaction data procured from literature, we should still account for possible errors in the EC50 values for the numerous drugs. the restrictive assumption considers that effective drugs operate on single points of failure within the patients signaling pathway.

In reality, high sensitivity to a drug is often attributed to a family of related kinases or several independent kinases working synergistically over one or multiple pathways to induce tumor death. This cooperative multivariate behavior needs to be taken into account while binarizing a drug to its multiple possible targets. despite the high level of currently available knowledge supplier AT7519 on the biological effects of numerous targeted drugs, there remains the possibility of a drug having high sensitivity while having no known mechanisms explaining its sensitivity. Therefore, we must consider the situation where there are latent mechanisms not considered within the dataset that are proving to be effective in some combination of treatment. This point does not necessarily eliminate the possibility of kinase mechanisms being an important factor.

We address all three concerns as follows: By consid ering the log scaled EC50 values for each target and the log scaled IC50 value for each drug, we convert the mul tiplicative noise to additive noise. In addition, we employ scalable bounds reversible Akt 阻害剤 around the IC50 s to determine binariza tion values of the numerous kinase targets for each drug. The bounds can be scaled to allow targets that may have EC50 s higher than the IC50 to be considered as a possi ble treatment mechanism. We extend the bounds to low EC50 levels, and often down to 0, to incorporate the possibility of target collaboration at various different EC50 levels. While a high IC50 indicates the likelihood of drug side targets as therapeutic mechanisms, it does not pre clude the possibility of a joint relationship between a high EC50 target and a low EC50 target.

Hence, to incorporate the numerous possible effective combinations implied by the IC50 of an effective drug, the binarization range of tar gets for a drug is the range log log B log where 0 B. For reliability and validity of the target set that we aim to construct, it is important to keep B in a reasonable range, B should be a smaller constant such as 3 or 4. For the situation where the above bounds do not result in at least one binarized target, the immediate option is to eliminate the drug from the data set before target selection. This prevents incom plete information from affecting the desired target set. As information concerning the drug screen agents gradually becomes complete with respect to other forms of data, such as gene interaction data, additional mechanisms for unexplained targets can be explored and incorporated more readily into the predictive model.


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