Dataset Information

Predicting the Effects of Interventions: A Tutorial on the Disequilibrium Model.

ABSTRACT: The disequilibrium approach to reinforcement and punishment, derived from the probability-differential hypothesis and response deprivation hypothesis, provides a number of potentially useful mathematical models for practitioners. The disequilibrium approach and its accompanying models have proven effective in the prediction and control of behavior, yet they have not been fully espoused and integrated into clinical practice. The purpose of this tutorial is to detail the disequilibrium approach and adapt its mathematical models for use as a tool in applied settings. The disequilibrium models specify how to arrange contingencies and predict the effects of those contingencies. We aggregate these models, and provide them as a single tool, in the form of a Microsoft Excel® spreadsheet that calculates the direction and magnitude of behavior change based on baseline measures and a practitioner's choice of intervention parameters. How practitioners take baseline measures and select intervention parameters in accordance with disequilibrium models is explicated. The proposed tool can be accessed and downloaded for use at https://osf.io/knf7x/.

SUBMITTER: Jacobs KW

PROVIDER: S-EPMC5459771 | biostudies-other | 2017 Jun

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:Despite scientific advances it remains difficult to predict the risk and benefit balance of immune interventions. Since a few years, network models have been built based on comprehensive datasets at multiple molecular/cellular levels (genes, gene products, metabolic intermediates, macromolecules, cells) to illuminate functional and structural relationships. Here we used a systems biology approach to identify key immune pathways involved in immune health endpoints and rank crucial candidate biomarkers to predict adverse and beneficial effects of nutritional immune interventions. First, a literature search was performed to select the molecular and cellular dynamics involved in hypersensitivity, autoimmunity and resistance to infection and cancer. Thereafter, molecular interaction between molecules and immune health endpoints was defined by connecting their relations by using database information. MeSH terms related to the immune health endpoints were selected resulting in the following selection: hypersensitivity (D006967: 184 genes), autoimmunity (D001327: 564 genes), infection (parasitic, bacterial, fungal and viral: 357 genes), and cancer (D009369: 3173 genes). In addition, a sequence of key processes was determined using Gene Ontology which drives the development of immune health disturbances resulting in the following selection: hypersensitivity (164 processes), autoimmunity (203 processes), infection (187 processes), and cancer (309 processes). Finally, an evaluation of the genes for each of the immune health endpoints was performed, which indicated that many genes played a role in multiple immune health endpoints, but also unique genes were observed for each immune health endpoint. This approach helps to build a screening/prediction tool which indicates the interaction of chemicals or food substances with immune health endpoint-related genes and suggests candidate biomarkers to evaluate risks and benefits. Several anti-cancer drugs and omega 3 fatty acids were evaluated as in silico test cases. To conclude, here we provide a systems biology approach to identify genes/molecules and their interaction with immune related disorders. Our examples illustrate that the prediction with our systems biology approach is promising and can be used to find both negatively and positively correlated interactions. This enables identification of candidate biomarkers to monitor safety and efficacy of therapeutic immune interventions.

Dataset Information

Predicting the Effects of Interventions: A Tutorial on the Disequilibrium Model.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure