J. Comput. Biol. - Using iterative ridge regression to explore associations between conditioned variables.

Tópicos

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{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
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{ drug(1928) target(777) effect(648) }
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Resumo

We address a specific case of joint probability mapping, where the information presented is the probabilistic associations of random variables under a certain condition variable (conditioned associations). Bayesian and dependency networks graphically map the joint probabilities of random variables, though both networks may identify associations that are independent of the condition (background associations). Since the background associations have the same topological features as conditioned associations, it is difficult to discriminate between conditioned and non-conditioned associations, which results in a major increase in the search space. We introduce a modification of the dependency network method, which produces a directed graph, containing only condition-related associations. The graph nodes represent the random variables and the graph edges represent the associations that arise under the condition variable. This method is based on ridge-regression, where one can utilize a numerically robust and computationally efficient algorithm implementation. We illustrate the method's efficiency in the context of a medically relevant process, the emergence of drug-resistant variants of human immunodeficiency virus (HIV) in drug-treated, HIV-infected people. Our mapping was used to discover associations between variants that are conditioned by the initiation of a particular drug treatment regimen. We have demonstrated that our method can recover known associations of such treatment with selected resistance mutations as well as documented associations between different mutations. Moreover, our method revealed novel associations that are statistically significant and biologically plausible.

Resumo Limpo

address specif case joint probabl map inform present probabilist associ random variabl certain condit variabl condit associ bayesian depend network graphic map joint probabl random variabl though network may identifi associ independ condit background associ sinc background associ topolog featur condit associ difficult discrimin condit noncondit associ result major increas search space introduc modif depend network method produc direct graph contain conditionrel associ graph node repres random variabl graph edg repres associ aris condit variabl method base ridgeregress one can util numer robust comput effici algorithm implement illustr method effici context medic relev process emerg drugresist variant human immunodefici virus hiv drugtreat hivinfect peopl map use discov associ variant condit initi particular drug treatment regimen demonstr method can recov known associ treatment select resist mutat well document associ differ mutat moreov method reveal novel associ statist signific biolog plausibl

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