MIKB: A manually curated and comprehensive knowledge base for myocardial infarction

MIO: An ontology for annotating and integrating medical knowledge in myocardial infarction to enhance clinical decision making

As biotechnology and computer science continue to advance, there's a growing amount of biomedical data worldwide. However, standardizing and consolidating these data remains challenging, making analysis and comprehension more difficult. To enhance research on complex diseases like myocardial infarction (MI), an ontology is necessary to ensure consistent data labeling and knowledge representation. This will facilitate data management and the application of artificial intelligence techniques in this field, ultimately advancing precision medicine research for MI. This study introduced the MI Ontology (MIO), which was developed using Stanford's seven-step method and Protégé. MIO aims to support precision medicine research on MI by effectively modeling and representing MI-related concepts and relationships. The validation of the MIO model involved employing Ontology Web Language (OWL) reasoners and comparing it with other disease-specific ontologies. MIO is an ontology model comprising of 3090 classes, 14 object attributes, 3494 individuals, 9415 synonyms and 49263 axioms, which encompass knowledge related to MI such as anatomical entities, clinical findings, drugs, genes, influencing factors, pathogenesis, patients-related concepts, procedures, and disease types. Furthermore, MIO has passed logical consistency validation and exhibits a broader conceptual scope and deeper knowledge structure than other disease-specific ontologies. Additionally, clinical use scenarios for MIO were developed to help address specific clinical problems. This study constructed the first comprehensive disease-specific ontology in cardiovascular diseases, named MIO, to promote precision medicine research on MI. MIO integrates and standardizes medical data, addressing complexity and standardization challenges. This promotes the use of big data analysis, explainable AI, and deep phenotype research in precision medicine. Future efforts will focus on enhancing and expanding MIO's applicability and scalability for superior services in this field.

NDDRF 2.0: An update and expansion of risk factor knowledge base for personalized prevention of neurodegenerative diseases

INTRODUCTION

Neurodegenerative diseases (NDDs) are chronic diseases caused by brain neuron degeneration, requiring systematic integration of risk factors to address their heterogeneity. Established in 2021, Knowledgebase of Risk Factors for Neurodegenerative Diseases (NDDRF) was the first knowledge base to consolidate NDD risk factors. NDDRF 2.0 expands focus to modifiable lifestyle-related factors, enhancing utility for NDD prevention.

METHODS

Data from the past 4 years were comprehensively updated, while lifestyle factors were manually collected and filtered from 1975 to 2024. Each factor was embedded with International Classification of Diseases codes and clinical stage annotations, and then re-standardized, classified, and annotated in accordance with the Unified Medical Language System Semantic Network.

RESULTS

NDDRF 2.0 encompasses 1971 risk factors classified under 151 subcategories across 20 NDDs, including 536 lifestyle-related factors covering six major categories and is freely accessible at http://sysbio.org.cn/NDDRF/.

DISCUSSION

As the first lifestyle-specific and holistic knowledge base for NDDs, NDDRF 2.0 offers structured and deep phenotype information, enabling personalized prevention strategies and clinical decision support.

HIGHLIGHTS

An enhanced lifestyle-specific and holistic knowledge base (Knowledgebase of Risk Factors for Neurodegenerative Diseases [NDDRF] 2.0) was built for neurodegenerative diseases (NDDs). NDDRF 2.0 provides detailed categorization and deep phenotypes to support targeted NDD prevention. NDDRF 2.0 provides a knowledge-driven resource that facilitates personalized risk assessment and proactive health management. NDDRF 2.0 provides clinicians, researchers, and at-risk populations with knowledge to develop and implement effective risk prevention strategies. NDDRF 2.0 can be used to build chatbots by enhancing large language models in the future.

PPCRKB: a risk factor knowledge base of postoperative pulmonary complications

Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk factors frequently occurring after surgical interventions, resulting in significant financial burdens, prolonged hospitalization and elevated mortality rates. Despite the existence of multiple studies on PPCs, a comprehensive knowledge base that can effectively integrate and visualize the diverse risk factors associated with PPCs is currently lacking. This study aims to develop an online knowledge platform on risk factors for PPCs (Postoperative Pulmonary Complications Risk Factor Knowledge Base, PPCRKB) that categorizes and presents the risk and protective factors associated with PPCs, as well as to facilitate the development of individualized prevention and management strategies for PPCs based on the needs of each investigator. The PPCRKB is a novel knowledge base that encompasses all investigated potential risk factors linked to PPCs, offering users a web-based platform to access these risk factors. The PPCRKB contains 2673 entries, 915 risk factors that have been categorized into 11 distinct groups. These categories include habit and behavior, surgical factors, anesthetic factors, auxiliary examination, environmental factors, clinical status, medicines and treatment, demographic characteristics, psychosocial factors, genetic factors and miscellaneous factors. The PPCRKB holds significant value for PPC research. The inclusion of both quantitative and qualitative data in the PPCRKB enhances the ability to uncover new insights and solutions related to PPCs. It could provide clinicians with a more comprehensive perspective on research related to PPCs in future. Database URL: http://sysbio.org.cn/PPCs.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Myocardial infarction unveiled: Key miRNA players screened by a novel lncRNA-miRNA-mRNA network model

BACKGROUND

Myocardial infarction (MI) is a major contributor to global mortality, and microRNAs (miRNAs) are important in its pathogenesis. Identifying blood miRNAs with clinical application potential for the early detection and treatment of MI is crucial.

METHODS

We obtained MI-related miRNA and miRNA microarray datasets from MI Knowledge Base (MIKB) and Gene Expression Omnibus (GEO), respectively. A new feature called target regulatory score (TRS) was proposed to characterize the RNA interaction network. MI-related miRNAs were characterized using TRS, transcription factor (TF) gene proportion (TFP), and ageing-related gene (AG) proportion (AGP) via the lncRNA-miRNA-mRNA network. A bioinformatics model was then developed to predict MI-related miRNAs, which were verified by literature and pathway enrichment analysis.

RESULTS

The TRS-characterized model outperformed previous methods in identifying MI-related miRNAs. MI-related miRNAs had high TRS, TFP, and AGP values, and combining the three features improved prediction accuracy to 0.743. With this method, 31 candidate MI-related miRNAs were screened from the specific-MI lncRNA-miRNA-mRNA network, associated with key MI pathways like circulatory system processes, inflammatory response, and oxygen level adaptation. Most candidate miRNAs were directly associated with MI according to literature evidence, except hsa-miR-520c-3p and hsa-miR-190b-5p. Furthermore, CAV1, PPARA and VEGFA were identified as MI key genes, and were targeted by most of the candidate miRNAs.

CONCLUSIONS

This study proposed a novel bioinformatics model based on multivariate biomolecular network analysis to identify putative key miRNAs of MI, which deserve further experimental and clinical validation for translational applications.