Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

Exploiting the theory of state space models, we derive the exact expressions of the information transfer, as well as redundant and synergistic transfer, for coupled Gaussian processes observed at multiple temporal scales. All of the terms, constituting the frameworks known as interaction information decomposition and partial information decomposition, can thus be analytically obtained for different time scales from the parameters of the VAR model that fits the processes. We report the application of the proposed methodology firstly to benchmark Gaussian systems, showing that this class of systems may generate patterns of information decomposition characterized by mainly redundant or synergistic information transfer persisting across multiple time scales or even by the alternating prevalence of redundant and synergistic source interaction depending on the time scale. Then, we apply our method to an important topic in neuroscience, i.e., the detection of causal interactions in human epilepsy networks, for which we show the relevance of partial information decomposition to the detection of multiscale information transfer spreading from the seizure onset zone

Synergistic warm inflation

We consider an alternative warm inflationary scenario in which $n$ scalar fields coupled to a dissipative matter fluid cooperate to produce power--law inflation. The scalar fields are driven by an exponential potential and the bulk dissipative pressure coefficient is linear in the expansion rate. We find that the entropy of the fluid attains its asymptotic value in a characteristic time proportional to the square of the number of fields. This scenario remains nearly isothermal along the inflationary stage. The perturbations in energy density and entropy are studied in the long--wavelength regime and seen to grow roughly as the square of the scale factor. They are shown to be compatible with COBE measurements of the fluctuations in temperature of the CMB.Comment: 13 pages, Revtex 3 To be published in Physical Review

Development Through Synergistic Reform

Several studies suggest that production of high-quality output is a precondition for firms in less developed countries to participate in the export market. Institutional deficiencies that raise the costs of entry into high-quality production therefore limit the positive impact that trade liberalization can have on income or growth. Institutional reform that reduces the costs of entry into high-quality production and trade reform therefore have synergistic effects on income and, possibly, growth. In contrast, institutional reform that reduces the costs of entry into low-quality production (e.g., reforms targeted at small businesses) interferes with the impact of trade reform. The model that yields these results is also used to analyze impacts of foreign direct investment and of subsidies to entrepreneurship in the presence of unemployment.

Synergistic drug combinations from electronic health records and gene expression.

ObjectiveUsing electronic health records (EHRs) and biomolecular data, we sought to discover drug pairs with synergistic repurposing potential. EHRs provide real-world treatment and outcome patterns, while complementary biomolecular data, including disease-specific gene expression and drug-protein interactions, provide mechanistic understanding.MethodWe applied Group Lasso INTERaction NETwork (glinternet), an overlap group lasso penalty on a logistic regression model, with pairwise interactions to identify variables and interacting drug pairs associated with reduced 5-year mortality using EHRs of 9945 breast cancer patients. We identified differentially expressed genes from 14 case-control human breast cancer gene expression datasets and integrated them with drug-protein networks. Drugs in the network were scored according to their association with breast cancer individually or in pairs. Lastly, we determined whether synergistic drug pairs found in the EHRs were enriched among synergistic drug pairs from gene-expression data using a method similar to gene set enrichment analysis.ResultsFrom EHRs, we discovered 3 drug-class pairs associated with lower mortality: anti-inflammatories and hormone antagonists, anti-inflammatories and lipid modifiers, and lipid modifiers and obstructive airway drugs. The first 2 pairs were also enriched among pairs discovered using gene expression data and are supported by molecular interactions in drug-protein networks and preclinical and epidemiologic evidence.ConclusionsThis is a proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing