Approximate Computational Intelligence Models and Causality in Bioinformatics

Lawrence J. Mazlack


causality, causal graphs, causal imprecision, cognitive maps


The target of many studies in bioinformatics is the discovery of cause-effect relationships among observed variables of interest, for example: treatments, exposures, preconditions, and outcomes. Causal modeling and causal discovery are implicitly central to medical science. The goal of this paper is to supply an overview of modeling imprecise causal complexes in the bioinformatics domain. In order to algorithmically consider causal relations, the relations must be placed into a representation that supports manipulation. Representations must support the fact that knowledge of at least some causal effects is imprcise. The most widespread causal representation is directed acyclic graphs (DAGs). However, DAGs are severely limited in what portion of the common sense world they can repreĀ¬sent as they require that Markov conditions hold. Another causal network methodology holds promise: Fuzzy Cognitive Maps (FCMs). This paper considers the needs of commonsense causality, imprecise causality, provides an overview of causal graph modeling, and suggests Fuzzy Cognitive Maps as a possibly useful methodology when Markov conditions do not hold.

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