Representing bioinformatics causality

Representing bioinformatics causality

This paper reviews a variety of different graphical notations currently in active use for modelling dynamic processes in bioinformatics and biotechnology, and crystallises from these notations a set of properties essential to any proposal for a modelling language seeking to provide an adequate systemic description of biological processes.

Representing bioinformatics datatypes using the OntoDT ontology

Data processing is at the heart of science. Hence, the problem of data typing is an important problem that has been addressed from different aspects and in different forms. For example, the Research Data Alliance1 (RDA), whose major goal is to speed up the international data-driven innovation and discovery by facilitating research data sharing and exchange, has identified that the problem of da...

Partial Orders for Representing Uncertainty, Causality and Decision Making: General Properties, Operations, and Algorithms

One of the main objectives of science and engineering is to help people select the most beneficial decisions. To make these decisions, we must know people’s preferences, we must have the information about different possible consequences of different decisions. Since information is never absolutely accurate and precise, we must also have information about the degree of certainty of different par...

Causality

Causality, Conditional Exogeneity, and Granger Causality

We relate notions of structural causality and conditional exogeneity as de…ned in White and Chalak (2006) (WC) to the concept of Granger causality. We show that given conditional exogeneity, structural non-causality implies retrospective Granger non-causality, an extension of the classical Granger (1969) non-causality condition. We also provide a form of converse to this result and tests for re...