CVSARRP: A framework to predict the risk of adverse to severe adverse reactions for 10855 diseases after COVID-19 vaccination

COVID-19 vaccines greatly reduce the risk of infection with SARS-CoV-2. However, some people have adverse reactions after vaccination, and these can sometimes be severe. Gender, age, vaccines, and especially certain diseases histories are related to severe adverse reactions following COVID-19 vaccination. However, there are thousands of diseases and only some are known to be related to these severe adverse reactions. The risk of severe adverse reactions with other diseases remains unknown. Therefore, there is a need for predictive studies to provide improved medical care and minimize risk. Herein, we analyzed the statistical results of existing COVID-19 vaccine adverse reaction data and proposed a COVID-19 vaccine severe adverse reaction risk prediction method, named CVSARRP. The performance of the CVSARRP method was tested using the leave-one-out cross-validation approach. The correlation coefficient between the predicted and real risk is greater than 0.86. The CVSARRP method predicts the risk from adverse reactions to severe adverse reactions after COVID-19 vaccination for 10855 diseases. People with certain diseases, such as central nervous system diseases, heart diseases, urinary system disease, anemia, cancer, and respiratory tract disease, among others, may potentially have increased of severe adverse reactions following vaccination against COVID-19 and experiencing adverse events.

From Association to Intervention: The Alzheimer's Disease-Associated Processes and Targets (ADAPT) Ontology

BACKGROUND

Many putative causes and risk factors have been associated with outcomes in Alzheimer's disease (AD) but all attempts at disease-modifying treatment have failed to be clinically significant. Efforts to address this "association-intervention" mismatch have tended to focus on the novel design of interventions.

OBJECTIVE

Here, we instead deal with the notion of association in depth. We introduce the concept of disease-associated process (DAP) as a flexible concept that can unite different areas of study of AD from genetics to epidemiology to identify disease-modifying targets.

METHODS

We sort DAPs using three properties: specificity for AD, frequency in patients, and pathogenic intensity for dementia before using a literature review to apply these properties in three ways. Firstly, we describe and visualize known DAPs. Secondly, we exemplify qualitative specificity analysis with the DAPs of tau protein pathology and autophagy to reveal their differential implication in AD. Finally, we use DAP properties to define the terms "risk factor," "cause," and "biomarker."

RESULTS

We show how DAPs fit into our collaborative disease ontology, the Alzheimer's Disease-Associated Processes and Targets (ADAPT) ontology. We argue that our theoretical system can serve as a democratic research forum, offering a more biologically adequate view of dementia than reductionist models.

CONCLUSION

The ADAPT ontology is a tool that could help to ground debates around priority setting using objective criteria for the identifying of targets in AD. Further efforts are needed to address issues of how biomedical research into AD is prioritized and funded.

A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction

Circular RNAs (circRNAs) are involved in the regulatory mechanisms of multiple complex diseases, and the identification of their associations is critical to the diagnosis and treatment of diseases. In recent years, many computational methods have been designed to predict circRNA-disease associations. However, most of the existing methods rely on single correlation data. Here, we propose a machine learning framework for circRNA-disease association prediction, called MLCDA, which effectively fuses multiple sources of heterogeneous information including circRNA sequences and disease ontology. Comprehensive evaluation in the gold standard dataset showed that MLCDA can successfully capture the complex relationships between circRNAs and diseases and accurately predict their potential associations. In addition, the results of case studies on real data show that MLCDA significantly outperforms other existing methods. MLCDA can serve as a useful tool for circRNA-disease association prediction, providing mechanistic insights for disease research and thus facilitating the progress of disease treatment.

'Doing' hypertension: Experiential knowledge and practice in the self-management of 'high blood' in the Philippines

Patients' embodied experiences do not always correspond to the biomedical concepts of particular diseases. Drawing from year-long fieldwork in the Philippines that involved semi-structured interviews, focus group discussions and digital diaries, we examine how individuals 'do' hypertension through their embodied experiences and the knowledge and practice that emerge from them. Drawing inspiration from Annemarie Mol's work on the notion of 'multiplicity' of disease, our analysis was informed by a commitment to privileging patients' embodied experiences and the multiple ontologies of hypertension. We find that for patients diagnosed with hypertension in the Philippines, symptoms enact illness; patients rely on their own embodied knowledge to define their illness' nature (e.g., diagnosis), experience (e.g., frequency of symptoms and non-chronicity) and praxis (e.g., self-care practices). We show how this knowledge gained from having embodied experiences of living with the disease interacts in various ways with biomedical knowledge, other diagnostic labels and clinical practices, to shape how hypertension manifests and is managed by patients. Beyond interrogating the relationship between what counts as a 'disease' and what is considered a 'symptom', our findings underscore the need to pay attention instead to the mutually co-constitutive processes of embodied experiences and disease categories in co-producing patient knowledge.

Getting Started with the IDG KMC Datasets and Tools

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.

A Novel Pathway Network Analytics Method Based on Graph Theory

A biological pathway is an ordered set of interactions between intracellular molecules having collective activity that impacts cellular function, for example, by controlling metabolite synthesis or by regulating the expression of sets of genes. They play a key role in advanced studies of genomics. However, existing pathway analytics methods are inadequate to extract meaningful biological structure underneath the network of pathways. They also lack automation. Given these circumstances, we have come up with a novel graph theoretic method to analyze disease-related genes through weighted network of biological pathways. The method automatically extracts biological structures, such as clusters of pathways and their relevance, significance of each pathway and gene, and so forth hidden in the complex network. We have demonstrated the effectiveness of the proposed method on a set of genes associated with coronavirus disease 2019.

A Birds-Eye (Re)View of Acid-Suppression Drugs, COVID-19, and the Highly Variable Literature

This Perspective examines a recent surge of information regarding the potential benefits of acid-suppression drugs in the context of COVID-19, with a particular eye on the great variability (and, thus, confusion) that has arisen across the reported findings, at least as regards the popular antacid famotidine. The degree of inconsistency and discordance reflects contradictory conclusions from independent, clinical-based studies that took roughly similar approaches, in terms of both experimental design (retrospective, observational, cohort-based, etc.) and statistical analysis workflows (propensity-score matching and stratification into sub-cohorts, etc.). The contradictions and potential confusion have ramifications for clinicians faced with choosing therapeutically optimal courses of intervention: e.g., do any potential benefits of famotidine suggest its use in a particular COVID-19 case? (If so, what administration route, dosage regimen, duration, etc. are likely optimal?) As succinctly put this March in Freedberg et al. (2021), "…several retrospective studies show relationships between famotidine and outcomes in COVID-19 and several do not." Beyond the pressing issue of possible therapeutic indications, the conflicting data and conclusions related to famotidine must be resolved before its inclusion/integration in ontological and knowledge graph (KG)-based frameworks, which in turn are useful for drug discovery and repurposing. As a broader methodological issue, note that reconciling inconsistencies would bolster the validity of meta-analyses which draw upon the relevant data-sources. And, perhaps most broadly, developing a system for treating inconsistencies would stand to improve the qualities of both 1) real world evidence-based studies (retrospective), on the one hand, and 2) placebo-controlled, randomized multi-center clinical trials (prospective), on the other hand. In other words, a systematic approach to reconciling the two types of studies would inherently improve the quality and utility of each type of study individually.

Identify Inflammatory Bowel Disease-Related Genes Based on Machine Learning

The patients of Inflammatory bowel disease (IBD) are increasing worldwide. IBD has the characteristics of recurring and difficult to cure, and it is also one of the high-risk factors for colorectal cancer (CRC). The occurrence of IBD is closely related to genetic factors, which prompted us to identify IBD-related genes. Based on the hypothesis that similar diseases are related to similar genes, we purposed a SVM-based method to identify IBD-related genes by disease similarities and gene interactions. One hundred thirty-five diseases which have similarities with IBD and their related genes were obtained. These genes are considered as the candidates of IBD-related genes. We extracted features of each gene and implemented SVM to identify the probability that it is related to IBD. Ten-cross validation was applied to verify the effectiveness of our method. The AUC is 0.93 and AUPR is 0.97, which are the best among four methods. We prioritized the candidate genes and did case studies on top five genes.

An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations

Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations.

Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches.

Disease relevant modifications of the methylome and transcriptome by particulate matter (PM2.5) from biomass combustion

Exposure to particulate matter (PM) is recognized as a major health hazard, but molecular responses are still insufficiently described. We analyzed the epigenetic impact of ambient PM_2.5 from biomass combustion on the methylome of primary human bronchial epithelial BEAS-2B cells using the Illumina HumanMethylation450 BeadChip. The transcriptome was determined by the Affymetrix HG-U133 Plus 2.0 Array. PM_2.5 induced genome wide alterations of the DNA methylation pattern, including differentially methylated CpGs in the promoter region associated with CpG islands. Gene ontology analysis revealed that differentially methylated genes were significantly clustered in pathways associated with the extracellular matrix, cellular adhesion, function of GTPases, and responses to extracellular stimuli, or were involved in ion binding and shuttling. Differential methylations also affected tandem repeats. Additionally, 45 different miRNA CpG loci showed differential DNA methylation, most of them proximal to their promoter. These miRNAs are functionally relevant for lung cancer, inflammation, asthma, and other PM-associated diseases. Correlation of the methylome and transcriptome demonstrated a clear bias toward transcriptional activation by hypomethylation. Genes that exhibited both differential methylation and expression were functionally linked to cytokine and immune responses, cellular motility, angiogenesis, inflammation, wound healing, cell growth, differentiation and development, or responses to exogenous matter. Disease ontology of differentially methylated and expressed genes indicated their prominent role in lung cancer and their participation in dominant cancer related signaling pathways. Thus, in lung epithelial cells, PM_2.5 alters the methylome of genes and noncoding transcripts or elements that might be relevant for PM- and lung-associated diseases.

iCTNet2: integrating heterogeneous biological interactions to understand complex traits

iCTNet (integrated Complex Traits Networks) version 2 is a Cytoscape app and database that allows researchers to build heterogeneous networks by integrating a variety of biological interactions, thus offering a systems-level view of human complex traits. iCTNet2 is built from a variety of large-scale biological datasets, collected from public repositories to facilitate the building, visualization and analysis of heterogeneous biological networks in a comprehensive fashion via the Cytoscape platform. iCTNet2 is freely available at the Cytoscape app store.