We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enable simultaneous prediction of multiple PTMs with high performance and computation efficiency. MIND-S also features an interpretation module, which provides the relevance of each amino acid for making the predictions and is validated with known motifs. The interpretation module also captures PTM patterns without any supervision. Furthermore, MIND-S enables examination of mutation effects on PTMs. We document a workflow, its applications to 26 types of PTMs of two datasets consisting of ∼50,000 proteins, and an example of MIND-S identifying a PTM-interrupting SNP with validation from biological data. We also include use case analyses of targeted proteins. Taken together, we have demonstrated that MIND-S is accurate, interpretable, and efficient to elucidate PTM-relevant biological processes in health and diseases.

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. Using a novel bioinformatics text-mining tool, we studied six categories of cardiovascular disease (CVD), namely, ischemic heart disease, cardiomyopathies, cerebrovascular accident, congenital heart disease, arrhythmias, and valve disease, anticipating novel ECM protein-disease and protein-protein relationships hidden within vast quantities of textual data. We conducted a phrase-mining analysis, delineating the relationships of 709 ECM proteins with the 6 groups of CVDs reported in 1,099,254 abstracts. The technology pipeline known as Context-Aware Semantic Online Analytical Processing was applied to semantically rank the association of proteins to each CVD and all six CVDs, performing analyses to quantify each protein-disease relationship. We performed principal component analysis and hierarchical clustering of the data, where each protein was visualized as a six-dimensional vector. We found that ECM proteins display variable degrees of association with the six CVDs; certain CVDs share groups of associated proteins, whereas others have divergent protein associations. We identified 82 ECM proteins sharing associations with all 6 CVDs. Our bioinformatics analysis ascribed distinct ECM pathways (via Reactome) from this subset of proteins, namely, insulin-like growth factor regulation and interleukin-4 and interleukin-13 signaling, suggesting their contribution to the pathogenesis of all six CVDs. Finally, we performed hierarchical clustering analysis and identified protein clusters predominantly associated with a targeted CVD; analyses of these proteins revealed unexpected insights underlying the key ECM-related molecular pathogenesis of each CVD, including virus assembly and release in arrhythmias. NEW & NOTEWORTHY The present study is the first application of a text-mining algorithm to characterize the relationships of 709 extracellular matrix-related proteins with 6 categories of cardiovascular disease described in 1,099,254 abstracts. Our analysis informed unexpected extracellular matrix functions, pathways, and molecular relationships implicated in the six cardiovascular diseases.

The search for new strategies for better understanding cardiovascular (CV) disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in CV biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and CV medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to CV biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of CV Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of CV diseases and unification of CV knowledge.

During cardiovascular disease progression, molecular systems of myocardium (e.g., a proteome) undergo diverse and distinct changes. Dynamic, temporally-regulated alterations of individual molecules underlie the collective response of the heart to pathological drivers and the ultimate development of pathogenesis. Advances in high-throughput omics technologies have enabled cost-effective, temporal profiling of targeted systems in animal models of human diseases. However, computational analysis of temporal patterns from omics data remains challenging. In particular, bioinformatic pipelines involving unsupervised statistical approaches to support cardiovascular investigations are lacking, which hinders one's ability to extract biomedical insights from these complex datasets. We developed a non-parametric data analysis platform to resolve computational challenges unique to temporal omics datasets. Our platform consists of three modules. Module I preprocesses the temporal data using either cubic splines or principal component analysis (PCA), and it simultaneously accomplishes the tasks on missing data imputation and denoising. Module II performs an unsupervised classification by K-means or hierarchical clustering. Module III evaluates and identifies biological entities (e.g., molecular events) that exhibit strong associations to specific temporal patterns. The jackstraw method for cluster membership has been applied to estimate p-values and posterior inclusion probabilities (PIPs), both of which guided feature selection. To demonstrate the utility of the analysis platform, we employed a temporal proteomics dataset that captured the proteome-wide dynamics of oxidative stress induced post-translational modifications (O-PTMs) in mouse hearts undergoing isoproterenol (ISO)-induced hypertrophy. We have created a platform, CV.Signature.TCP, to identify distinct temporal clusters in omics datasets. We presented a cardiovascular use case to demonstrate its utility in unveiling biological insights underlying O-PTM regulations in cardiac remodeling. This platform is implemented in an open source R package (https://github.com/UCLA-BD2K/CV.Signature.TCP).

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications. We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm. Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents. CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

We consider SU(N) gauge theories on a two-dimensional torus with finite area, $A$. Let $T_{\mu }(A)$ denote the Polyakov loop operator in the $\mu$ direction. Starting from the lattice gauge theory on the torus, we derive a formula for the continuum limit of $⟨g_1(T_1(A))g_2(T_2(A))⟩$ as a function of the area of the torus where $g_1$ and $g_2$ are class functions. We show that there exists a class function ${\xi }_0$ for SU(2) such that $⟨{\xi }_0(T_1(A)){\xi }_0(T_2(A))⟩>1$ for all finite area of the torus with the limit being unity as the area of the torus goes to infinity. Only the trivial representation contributes to ${\xi }_0$ as $A\to \infty$ whereas all representations become equally important as $A\to 0$.