Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery

The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox...

A Network Approach to the Five-Facet Model of Mindfulness

Despite the extensive dissemination of mindfulness-based interventions, debates persist about the very definition of mindfulness. For decades, the ontological discourse on mindfulness has mainly been confined to the development of operational definitions. To date, the dominant paradigm is the five-facet approach that suggests that mindfulness includes five facets (i.e., Observing, Describing, Nonjudging, Nonreactivity, and Acting with Awareness). However, uncertainty still abounds regarding the potential interplay between the facets. In this preregistered study, we investigated the five-facet approach via network analysis in an unselected sample (N=1,704). To do so, we used two distinct computational network approaches: a graphical Gaussian model (GGM) and a directed acyclic graph (DAG). Each model estimates edges (i.e., the relations between the facets) and the importance of nodes (i.e., the facets) in different ways. Our results indicate that the five facets can be conceptualized as a single, coherent network system of interacting elements. Moreover, both GGM and DAG pointed to the acting with awareness facet as playing an especially potent role in the network system. Altogether, our findings offer viable data-driven heuristics for the field's larger quest to ascertain the foundations of mindfulness.

A novel estimator of the interaction matrix in Graphical Gaussian Model of omics data using the entropy of non-equilibrium systems

Motivation Inferring the direct relationships between biomolecules from omics datasets is essential for the understanding of biological and disease mechanisms. Gaussian Graphical Model (GGM) provides a fairly simple and accurate representation of these interactions. However, estimation of the associated interaction matrix using data is challenging due to a high number of measured molecules and a low number of samples. Results In this article, we use the thermodynamic entropy of the non-equilibrium system of molecules and the data-driven constraints among their expressions to derive an analytic formula for the interaction matrix of Gaussian models. Through a data simulation, we show that our method returns an improved estimation of the interaction matrix. Also, using the developed method, we estimate the interaction matrix associated with plasma proteome and construct the corresponding GGM and show that known NAFLD-related proteins like ADIPOQ, APOC, APOE, DPP4, CAT, GC, HP, CETP, SERPINA1, COLA1, PIGR, IGHD, SAA1 and FCGBP are among the top 15% most interacting proteins of the dataset. Availability and implementation The supplementary materials can be found in the following URL: http://dynamic-proteome.utmb.edu/PrecisionMatrixEstimater/PrecisionMatrixEstimater.aspx. Supplementary information Supplementary data are available at Bioinformatics online.

A Network Approach to Parental Burnout

Background: The use of network analyses in psychology has increasingly gained traction in the last few years. A network perspective views psychological constructs as dynamic systems of interacting elements. Objective: We present the first study to apply network analyses to examine how the hallmark features of parental burnout — i.e., exhaustion related to the parental role, emotional distancing from children, and a sense of ineffectiveness in the parental role — interact with one another and with maladaptive behaviors related to the partner and the child(ren), when these variables are conceptualized as a network system. Participants and setting: In a preregistered fashion, we reanalyzed the data from a French-speaking sample (n = 1551; previously published in Mikolajczak, Brianda, Avalosse, & Roskam, 2018), focusing on seven specific variables: the three hallmark parental burnout features, partner conflict, partner estrangement, neglectful behavior toward children, and violent behavior toward children. Methods: We computed two types of network models, a graphical Gaussian model to examine network structure, potential communities, and influential nodes, and a directed acyclic graph to examine the probabilistic dependencies among the different variables. Results: Both network models pointed to emotional distance as an especially potent mechanism in activating all other nodes. Conclusions: These results suggest emotional distance as critical to the maintenance of the parental burnout network and a prime candidate for future interventions, while affirming that network analysis can successfully expose the structure and relationship of variables related to parental burnout and its consequences related to the partner and the child(ren).

Gene modules associated with human diseases revealed by network analysis

ABSTRACTDespite many genes associated with human diseases have been identified, disease mechanisms often remain elusive due to the lack of understanding how disease genes are connected functionally at pathways level. Within biological networks, disease genes likely map to modules whose identification facilitates etiology studies but remains challenging. We describe a systematic approach to identify disease-associated gene modules. A gene co-expression network based on the graphical Gaussian model (GGM) was constructed using the GTEx dataset and assembled into 652 gene modules. Screening these modules identified those with disease genes enrichment for obesity, cardiomyopathy, hypertension, and autism, which illuminated the molecular pathways underlying their pathogenesis. Using mammalian phenotypes derived from mouse models, potential disease candidate genes were identified from these modules. Also analyzed were epilepsy, schizophrenia, bipolar disorder, and depressive disorder, revealing shared and distinct disease modules among brain disorders. Thus, disease genes converge on modules within our GGM gene co-expression network, which provides a general framework to dissect genetic architecture of human diseases.