Cardiovascular genomics: a biomarker identification pipeline

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Background

The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods

We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings

A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation

The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding

No funding received.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Feature Selection Using Approximate Conditional Entropy Based on Fuzzy Information Granule for Gene Expression Data Classification

Classification is widely used in gene expression data analysis. Feature selection is usually performed before classification because of the large number of genes and the small sample size in gene expression data. In this article, a novel feature selection algorithm using approximate conditional entropy based on fuzzy information granule is proposed, and the correctness of the method is proved by the monotonicity of entropy. Firstly, the fuzzy relation matrix is established by Laplacian kernel. Secondly, the approximately equal relation on fuzzy sets is defined. And then, the approximate conditional entropy based on fuzzy information granule and the importance of internal attributes are defined. Approximate conditional entropy can measure the uncertainty of knowledge from two different perspectives of information and algebra theory. Finally, the greedy algorithm based on the approximate conditional entropy is designed for feature selection. Experimental results for six large-scale gene datasets show that our algorithm not only greatly reduces the dimension of the gene datasets, but also is superior to five state-of-the-art algorithms in terms of classification accuracy.

Optimal Bayesian Filtering for Biomarker Discovery: Performance and Robustness

Optimal Bayesian feature filtering (OBF) is a fast and memory-efficient algorithm that optimally identifies markers with distributional differences between treatment groups under Gaussian models. Here, we study the performance and robustness of OBF for biomarker discovery. Our contributions are twofold: (1) we examine how OBF performs on data that violates modeling assumptions, and (2) we provide guidelines on how to set input parameters for robust performance. Contribution (1) addresses an important, relevant, and commonplace problem in computational biology, where it is often impossible to validate an algorithm's core assumptions. To accomplish both tasks, we present a battery of simulations that implement OBF with different inputs and challenge each assumption made by OBF. In particular, we examine the robustness of OBF with respect to incorrect input parameters, false independence, imbalanced sample size, and we address the Gaussianity assumption by considering performance on an extensive family of non-Gaussian distributions. We address advantages and disadvantages between different priors and optimization criteria throughout. Finally, we evaluate the utility of OBF in biomarker discovery using acute myeloid leukemia (AML) and colon cancer microarray datasets, and show that OBF is successful at identifying well-known biomarkers for these diseases that rank low under moderated t-test.

Microarray Technologies in Fungal Diagnostics

Microarray technologies have been a major research tool in the last decades. In addition they have been introduced into several fields of diagnostics including diagnostics of infectious diseases. Microarrays are highly parallelized assay systems that initially were developed for multiparametric nucleic acid detection. From there on they rapidly developed towards a tool for the detection of all kind of biological compounds (DNA, RNA, proteins, cells, nucleic acids, carbohydrates, etc.) or their modifications (methylation, phosphorylation, etc.). The combination of closed-tube systems and lab on chip devices with microarrays further enabled a higher automation degree with a reduced contamination risk. Microarray-based diagnostic applications currently complement and may in the future replace classical methods in clinical microbiology like blood cultures, resistance determination, microscopic and metabolic analyses as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel nucleic acid based biomarkers. Here I focus an microarray technologies in diagnostics and as research tools, based on nucleic acid-based arrays.

Feature selection methods for big data bioinformatics: A survey from the search perspective

This paper surveys main principles of feature selection and their recent applications in big data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and embedded approaches to feature selection, we formulate feature selection as a combinatorial optimization or search problem and categorize feature selection methods into exhaustive search, heuristic search, and hybrid methods, where heuristic search methods may further be categorized into those with or without data-distilled feature ranking measures.

Systemic Transcriptional Alterations of Innate and Adaptive Immune Signaling Pathways in Atherosclerosis, Ischemia Stroke, and Myocardial Infarction

Background

Transcriptional profiles are available for a variety of cardiovascular-related diseases. The goal of this study was to compare blood transcriptional profiles of the Toll-like receptor (TLR), T-cell receptor (TCR), and B-cell receptor (BCR) signaling pathways in asymptomatic atherosclerosis, acute ischemic stroke, and myocardial infarction patients to identify common mechanisms of immune regulation and their association with epigenetic regulation.

Methods and results

Peripheral blood gene expression profiles from human atherosclerosis-related diseases and healthy controls were downloaded from Gene Expression Omnibus (GEO). Genes in the TLR, TCR, and BCR pathways were retrieved from the NCBI BioSystems database. Significance of gene enrichment and concordance of expression changes in each pathway was compared between studies. Gene expression was significantly correlated across the three disease conditions (p<10⁻¹⁵) and the proportion of significant genes was high (30~60%, p<0.001). Hub genes identified by weighted gene co-expression network analysis (WGCNA) in the TCR/BCR sub-network, including CD81 and TCR-CD3ζ, were significantly down-regulated and highly correlated with DNA (cytosine-5-)-methyltransferase 1 (DNMT1).

Conclusion

Common biologically relevant networks associated with immune regulation in stroke, atherosclerosis, and myocardial infarction were discovered. Given the high correlation of DNMT1 with these immune signaling pathways, epigenetic regulation may contribute to the coordination of innate and adaptive immune response in all CVD disease states. Down-regulation of the TCR-BCR axis in the adaptive immune system offers critical information for the investigation of the functional mechanisms underlying chronic inflammation-induced immune suppression in cardiovascular disease and stroke.

Microarrays as Research Tools and Diagnostic Devices

Molecular diagnostics comprises a main analytical division in clinical laboratory diagnostics. The analysis of RNA or DNA helps to diagnose infectious diseases and identify genetic determined disorders or even cancer. Starting from mono-parametric tests within the last years, technologies have evolved that allow for the detection of many parameters in parallel, e.g., by using multiplex nucleic acid amplification techniques, microarrays, or next-generation sequencing technologies. The introduction of closed-tube systems as well as lab-on-a-chip devices further resulted in a higher automation degree with a reduced contamination risk. These applications complement or even stepwise replace classical methods in clinical microbiology like virus cultures, resistance determination, microscopic and metabolic analyses, as well as biochemical or immunohistochemical assays. In addition, novel diagnostic markers appear, like noncoding RNAs and miRNAs providing additional room for novel biomarkers.

This article provides an overview of microarrays as diagnostics devices and research tools. Introduced in 1995 for transcription analysis, microarrays are used today to detect several different biomolecules like DNA, RNA, miRNA, and proteins among others. Mainly used in research, some microarrays also found their way to clinical diagnostics. Further, closed lab-on-a-chip devices that use DNA microarrays as detection tools are discussed, and additionally, an outlook toward applications of next-generation sequencing tools in diagnostics will be given.

Cardiovascular health informatics: risk screening and intervention

Despite enormous efforts to prevent cardiovascular disease (CVD) in the past, it remains the leading cause of death in most countries worldwide. Around two-thirds of these deaths are due to acute events, which frequently occur suddenly and are often fatal before medical care can be given. New strategies for screening and early intervening CVD, in addition to the conventional methods, are therefore needed in order to provide personalized and pervasive healthcare. In this special issue, selected emerging technologies in health informatics for screening and intervening CVDs are reported. These papers include reviews or original contributions on 1) new potential genetic biomarkers for screening CVD outcomes and high-throughput techniques for mining genomic data; 2) new imaging techniques for obtaining faster and higher resolution images of cardiovascular imaging biomarkers such as the cardiac chambers and atherosclerotic plaques in coronary arteries, as well as possible automatic segmentation, identification, or fusion algorithms; 3) new physiological biomarkers and novel wearable and home healthcare technologies for monitoring them in daily lives; 4) new personalized prediction models of plaque formation and progression or CVD outcomes; and 5) quantifiable indices and wearable systems to measure them for early intervention of CVD through lifestyle changes. It is hoped that the proposed technologies and systems covered in this special issue can result in improved CVD management and treatment at the point of need, offering a better quality of life to the patient.