FIREWORKS incorporates a novel bias reduction approach to reduce false discoveries, enables restriction of coessentiality analyses to custom subsets of cell lines, and integrates multiomic and drug–gene interaction datasets to investigate and target contextual gene essentiality. We created FIREWORKS, a method and interactive tool for the construction and statistical analysis of coessentiality networks centered around gene(s) provided by the user. However, widespread implementation of coessentiality to study individual genes and pathways is limited by systematic biases in existing coessentiality approaches and accessibility barriers for investigators without computational expertise. Introduction Since reasoning in multi-dimensional domains tends to be infeasible in the domains as a whole-and the more so, if uncertainty and/or imprecision are involved-decomposition techniques, that reduce the reasoning process to computations in lo.Genetic coessentiality analysis, a computational approach which identifies genes sharing a common effect on cell fitness across large-scale screening datasets, has emerged as a powerful tool to identify functional relationships between human genes. Keywords: Decomposition, uncertain reasoning, probabilistic networks, possibilistic networks, learning from data. how to determine from a database of sample cases an appropriate decomposition of the underlying probability or possibility distribution. we also discuss how to learn probabilistic and possibilistic networks from a data, i.e. Since constructing probabilistic and possibilistic networks by hand can be tedious and time-consuming. : In this paper we explain in a tutorial manner the technique of reasoning in probabilistic and possibilistic network structures, which is based on the idea to decompose a multi-dimensional probability or possibility distribution and to draw inferences using only the parts of the decomposition.

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