Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19.

Publication date: Jan 28, 2024

A novel virus emerged from Wuhan, China, at the end of 2019 and quickly evolved into a pandemic, significantly impacting various industries, especially healthcare. One critical lesson from COVID-19 is the importance of understanding and predicting underlying comorbidities to better prioritize care and pharmacological therapies. Factors like age, race, and comorbidity history are crucial in determining disease mortality. While clinical data from hospitals and cohorts have led to the identification of these comorbidities, traditional approaches often lack a mechanistic understanding of the connections between them. In response, we utilized a deep learning approach to integrate COVID-19 data with data from other diseases, aiming to detect comorbidities with mechanistic insights. Our modified algorithm in the mpDisNet package, based on word-embedding deep learning techniques, incorporates miRNA expression profiles from SARS-CoV-2 infected cell lines and their target transcription factors. This approach is aligned with the emerging field of network medicine, which seeks to define diseases based on distinct pathomechanisms rather than just phenotypes. The main aim is discovery of possible unknown comorbidities by connecting the diseases by their miRNA mediated regulatory interactions. The algorithm can predict the majority of COVID-19’s known comorbidities, as well as several diseases that have yet to be discovered to be comorbid with COVID-19. These potentially comorbid diseases should be investigated further to raise awareness and prevention, as well as informing the comorbidity research for the next possible outbreak.

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Concepts Keywords
China Algorithm
Learning Based
Pharmacological Comorbid
Race Comorbidities
Virus Comorbidity
Covid
Deep
Diseases
Factors
Learning
Mechanistic
Mirna
Network
Optimized
Understanding

Semantics

Type Source Name
disease MESH COVID-19
drug DRUGBANK Tropicamide
disease MESH comorbidity
disease IDO history
disease VO LACK
disease IDO algorithm
disease IDO cell
drug DRUGBANK Coenzyme M
drug DRUGBANK Pinaverium
disease MESH infection
disease IDO process
drug DRUGBANK Isoxaflutole
disease MESH complications
disease MESH heart disease
disease IDO blood
disease VO gene
disease MESH etiology
disease VO document
disease MESH cancer
drug DRUGBANK Saquinavir
pathway REACTOME Reproduction
disease VO frequency
disease MESH Carcinoma
disease MESH Leukemia
disease MESH Lymphoma
disease MESH Sarcoma
disease MESH Melanoma
pathway KEGG Melanoma
disease MESH Glioma
pathway KEGG Glioma
disease MESH ‘Mitochondrial Encephalomyopathies
disease MESH ‘Prostatic Diseases
disease MESH ‘Tauopathies
disease MESH ‘Cystadenocarcinoma
disease MESH ‘Anodontia
disease MESH ‘Chagas Disease
pathway KEGG Chagas disease
disease MESH ‘Frontotemporal Lobar Degeneration
disease MESH Syndrome
disease MESH Defects
disease MESH ‘Lymphedema
disease MESH ‘Myopia
disease MESH ‘Pemphigus
disease MESH Rheumatoid Arthritis
pathway KEGG Rheumatoid arthritis
disease MESH epilepsy
disease MESH chronic hepatitis
disease MESH Hypertension
disease MESH Hepatitis
drug DRUGBANK Sulodexide
disease MESH coronary artery disease
disease MESH aortic aneurysm
disease MESH Schizophrenia
drug DRUGBANK Gold
disease VO volume
disease VO effective
disease MESH genetic disorders
disease VO Hsp90
disease MESH chronic pancreatitis
disease MESH psoriasis
disease VO population

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