Sharing Data With Shared Benefits: Artificial Intelligence Perspective.

Artificial intelligence (AI) and data sharing go hand in hand. In order to develop powerful AI models for medical and health applications, data need to be collected and brought together over multiple centers. However, due to various reasons, including data privacy, not all data can be made publicly available or shared with other parties. Federated and swarm learning can help in these scenarios. However, in the private sector, such as between companies, the incentive is limited, as the resulting AI models would be available for all partners irrespective of their individual contribution, including the amount of data provided by each party. Here, we explore a potential solution to this challenge as a viewpoint, aiming to establish a fairer approach that encourages companies to engage in collaborative data analysis and AI modeling. Within the proposed approach, each individual participant could gain a model commensurate with their respective data contribution, ultimately leading to better diagnostic tools for all participants in a fair manner.

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder.

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks' time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models' parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal's application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain's right to the left hemisphere is different between ASD and controls.

Learning genetic and environmental graphical models from family data.

Many challenging problems in biomedical research rely on understanding how variables are associated with each other and influenced by genetic and environmental factors. Probabilistic graphical models (PGMs) are widely acknowledged as a very natural and formal language to describe relationships among variables and have been extensively used for studying complex diseases and traits. In this work, we propose methods that leverage observational Gaussian family data for learning a decomposition of undirected and directed acyclic PGMs according to the influence of genetic and environmental factors. Many structure learning algorithms are strongly based on a conditional independence test. For independent measurements of normally distributed variables, conditional independence can be tested through standard tests for zero partial correlation. In family data, the assumption of independent measurements does not hold since related individuals are correlated due to mainly genetic factors. Based on univariate polygenic linear mixed models, we propose tests that account for the familial dependence structure and allow us to assess the significance of the partial correlation due to genetic (between-family) factors and due to other factors, denoted here as environmental (within-family) factors, separately. Then, we extend standard structure learning algorithms, including the IC/PC and the really fast causal inference (RFCI) algorithms, to Gaussian family data. The algorithms learn the most likely PGM and its decomposition into two components, one explained by genetic factors and the other by environmental factors. The proposed methods are evaluated by simulation studies and applied to the Genetic Analysis Workshop 13 simulated dataset, which captures significant features of the Framingham Heart Study.

Missense genetic variants in major bitter taste receptors are associated with diet quality and food intake in a highly admixed underrepresented population.

BACKGROUND AND AIMS: To investigate associations between Single Nucleotide Polymorphisms (SNPs) in the TAS1R and TAS2R taste receptors and diet quality, intake of alcohol, added sugar, and fat, using linear regression and machine learning techniques in a highly admixed population. METHODS: In the ISA-Capital health survey, 901 individuals were interviewed and had socioeconomic, demographic, health characteristics, along with dietary information obtained through two 24-h recalls. Data on 12 components related to food groups, nutrients, and calories was combined into a diet quality score (BHEI-R). BHEI-R, SoFAAs (calories from added sugar, saturated fat, and alcohol) and Alcohol use were tested for associations with 255 TAS2R SNPs and 73 TAS1R SNPs for 637 individuals with regression analysis and Random Forest. Significant SNPs were combined into Genetic taste scores (GTSs). RESULTS: Among 23 SNPs significantly associated either by stepwise linear/logistic regression or random forest with any possible biological functionality, the missense variants rs149217752 in TAS2R40, for SoFAAs, and rs2233997 in TAS2R4, were associated with both BHEI-R (under 4% increase in Mean Squared Error) and SoFAAs. GTSs increased the variance explanation of quantitative phenotypes and there was a moderately high AUC for alcohol use. CONCLUSIONS: The study provides insights into the genetic basis of human taste perception through the identification of missense variants in the TAS2R gene family. These findings may contribute to future strategies in precision nutrition aimed at improving food quality by reducing added sugar, saturated fat, and alcohol intake.

Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments.

Faced with the lack of reliability and reproducibility in omics studies, more careful and robust methods are needed to overcome the existing challenges in the multi-omics analysis. In conventional omics data analysis, signal intensity values (denoted by M and values) are estimated neglecting pixel-level uncertainties, which may reflect noise and systematic artifacts. For example, intensity values from two-color microarray data are estimated by taking the mean or median of the pixel intensities within the spot and then subjected to a within-slide normalization by LOWESS. Thus, focusing on estimation and normalization of gene expression profiles, we propose a spot quantification method that takes into account pixel-level variability. Also, to preserve relevant variation that may be removed in LOWESS normalization with poorly chosen parameters, we propose a parameter selection method that is parsimonious and considers intrinsic characteristics of microarray data, such as heteroskedasticity. The usefulness of the proposed methods is illustrated by an application to real intestinal metaplasia data. Compared with the conventional approaches, the analysis is more robust and conservative, identifying fewer but more reliable differentially expressed genes. Also, the variability preservation allowed the identification of new differentially expressed genes. Using the proposed approach, we have identified differentially expressed genes involved in pathways in cancer and confirmed some molecular markers already reported in the literature.

Association Between Short-Term Systolic Blood Pressure Variability and Carotid Intima-Media Thickness in ELSA-Brasil Baseline.

BACKGROUND: Blood pressure (BP) is associated with carotid intima-media thickness (CIMT), but few studies have explored the association between BP variability and CIMT. We aimed to investigate this association in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) baseline. METHODS: We analyzed data from 7,215 participants (56.0% women) without overt cardiovascular disease (CVD) or antihypertensive use. We included 10 BP readings in varying positions during a 6-hour visit. We defined BP variability as the SD of these readings. We performed a 2-step analysis. We first linearly regressed the CIMT values on main and all-order interaction effects of the variables age, sex, body mass index, race, diabetes diagnosis, dyslipidemia diagnosis, family history of premature CVD, smoking status, and ELSA-Brasil site, and calculated the residuals (residual CIMT). We used partial least square path analysis to investigate whether residual CIMT was associated with BP central tendency and BP variability. RESULTS: Systolic BP (SBP) variability was significantly associated with residual CIMT in models including the entire sample (path coefficient [PC]: 0.046; P < 0.001), and in women (PC: 0.046; P = 0.007) but not in men (PC: 0.037; P = 0.09). This loss of significance was probably due to the smaller subsample size, as PCs were not significantly different according to sex. CONCLUSIONS: We found a small but significant association between SBP variability and CIMT values. This was additive to the association between SBP central tendency and CIMT values, supporting a role for high short-term SBP variability in atherosclerosis.