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Here we tackle this multiple-condition scenario, where the reconstructed subnetwork should explain in a coherent manner multiple experiments driven by the same set of proteins (referred to here as anchor proteins) while producing different sets of affected proteins, or terminals.
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We tackle this challenge by employing and comparing several statistical methods, which we used to test assumptions about how multiple aspects of immune function are related at different organizational levels.
How do we tackle this social recession?
We tackle this challenge in two ways.
We tackle this question using computational experiments.
In this paper, we tackle this open problem.
However, we tackle this problem by using finite difference linearization. .
In this paper, we tackle this problem by using principles from AI.
We tackle this issue by framing the DRT problem within a Bayesian statistical framework.
Here we tackle this issue using the Z curve method.
Here, we tackle this issue by using Hox proteins as a case study.
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