Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity.

Statistical integration of two omics datasets using GO2PLS.

BACKGROUND: Nowadays, multiple omics data are measured on the same samples in the belief that these different omics datasets represent various aspects of the underlying biological systems. Integrating these omics datasets will facilitate the understanding of the systems. For this purpose, various methods have been proposed, such as Partial Least Squares (PLS), decomposing two datasets into joint and residual subspaces. Since omics data are heterogeneous, the joint components in PLS will contain variation specific to each dataset. To account for this, Two-way Orthogonal Partial Least Squares (O2PLS) captures the heterogeneity by introducing orthogonal subspaces and better estimates the joint subspaces. However, the latent components spanning the joint subspaces in O2PLS are linear combinations of all variables, while it might be of interest to identify a small subset relevant to the research question. To obtain sparsity, we extend O2PLS to Group Sparse O2PLS (GO2PLS) that utilizes biological information on group structures among variables and performs group selection in the joint subspace. RESULTS: The simulation study showed that introducing sparsity improved the feature selection performance. Furthermore, incorporating group structures increased robustness of the feature selection procedure. GO2PLS performed optimally in terms of accuracy of joint score estimation, joint loading estimation, and feature selection. We applied GO2PLS to datasets from two studies: TwinsUK (a population study) and CVON-DOSIS (a small case-control study). In the first, we incorporated biological information on the group structures of the methylation CpG sites when integrating the methylation dataset with the IgG glycomics data. The targeted genes of the selected methylation groups turned out to be relevant to the immune system, in which the IgG glycans play important roles. In the second, we selected regulatory regions and transcripts that explained the covariance between regulomics and transcriptomics data. The corresponding genes of the selected features appeared to be relevant to heart muscle disease. CONCLUSIONS: GO2PLS integrates two omics datasets to help understand the underlying system that involves both omics levels. It incorporates external group information and performs group selection, resulting in a small subset of features that best explain the relationship between two omics datasets for better interpretability.

Investigating the impact of Down syndrome on methylation and glycomics with two-stage PO2PLS.

Down syndrome (DS) is a condition that leads to precocious and accelerated aging in affected subjects. Several alterations in DS cases have been reported at a molecular level, particularly in methylation and glycosylation. Investigating the relation between methylation, glycomics and DS can lead to new insights underlying the atypical aging. We consider a data integration approach, where we investigate how DS affects the parts of glycomics and methylation which are correlated, and which CpG sites and glycans are relevant. Our motivating datasets consist of methylation and glycomics data, measured on 29 DS patients and their unaffected siblings and mothers. The family-based case-control design needs to be taken into account when studying the relationship between methylation, glycomics and DS. We propose a two-stage approach to first integrate methylation and glycomics data, and then link the joint information to Down syndrome. For the data integration step, we consider probabilistic two-way orthogonal partial least squares (PO2PLS). PO2PLS models two omics datasets in terms of low-dimensional joint and omic-specific latent components, and takes into account heterogeneity across the omics data. The relationship between the omics data can be statistically tested. The joint components represent the joint information in methylation and glycomics. In the second stage, we apply a linear mixed model to the relationship between DS and the joint methylation and glycomics components. For the components that are significantly as sociated with DS, we identify the most important CpG sites and glycans. A simulation study is conducted to evaluate the performance of our approach. The results showed that the effects of DS on the omics data can be detected in a large sample size, and the accuracy of the feature selection was high in both small and large sample sizes. Our approach is applied to the DS datasets, a significant effect of DS on the joint components is found. The identified CpG sites and glycans appeared to be related to DS. Our proposed method that jointly analyzes multiple omics data with an outcome variable may provide new insight into the molecular implications of DS at different omics levels.