Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

Kartchner, David; McCoy, Kevin; Dubey, Janhvi; Zhang, Dongyu; Zheng, Kevin; Umrani, Rushda; Kim, James J.; Mitchell, Cassie S.

doi:10.3390/biology12091269

Cited by 4 publications

(7 citation statements)

References 158 publications

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“…A total of 20 nodes were specified as “hub nodes” for making the hub node networks. Briefly, hub networks enable improved cross-domain analysis by functionally increasing the search depth in areas of the knowledge graph of chief interest [8, 14]. Ten pairwise simulation trials were performed, equating to 200 simulations.…”

Section: Methodsmentioning

confidence: 99%

“…The unsupervised learning ranking algorithm within SemNet 2.0 examines relationship patterns in the literature to rank cross-domain concepts with respect to the user-defined concept(s) [9]. SemNet 2.0 has been used for drug repurposing of COVID-19 [12] and Parkinson’s disease [13], to identify unknown disease mechanisms of resistant hypertension following COVID-19 infection [14], and to predict adverse events from chronic tyrosine kinase inhibitor therapy in chronic myeloid leukemia [8].…”

Section: Introductionmentioning

confidence: 99%

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Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Patidar,

Deng,

Mitchell

et al. 2024

Preprint

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Diabetic kidney disease (DKD) remains a significant burden on the healthcare system and is the leading cause of end-stage renal disease worldwide. The pathophysiology of DKD is multifactorial and characterized by various early signs of metabolic impairment, inflammatory biomarkers, and complex pathways that lead to progressive kidney damage. New treatment prospects rely on a comprehensive understanding of disease pathology. The study aimed to identify signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via cross-domain text mining with SemNet 2.0. The open-source literature-based discovery approach, SemNet 2.0, leverages the power of text mining 33+ million PubMed articles to provide integrative insight into multiscalar and multifactorial pathophysiology. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersecting three domains\textemdash DKD, immune response, and glomerular endothelial cells\textemdash were analyzed. The top 10$\%$ of ranked concepts mapped to the following biological functions: angiotensin, apoptosis, cell-cell function, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, nitric oxide response, oxidative stress, cytokine response, macrophage signaling, NF$\upkappa$B factor activity, TLR signaling, glucose metabolism, inflammatory response, ERK/MAPK signaling, JAK/STAT signaling, T-cell mediated response, WNT signaling, renin angiotensin system, and NADPH response. High-ranking genes and proteins were used to generate a protein-protein interaction network. This comprehensive analysis identified testable hypotheses for interactions or molecules involved with dysregulated signaling in DKD, which can be further studied through biochemical network models.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Patidar,

Deng,

Mitchell

et al. 2024

Preprint

Self Cite

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“…SemNet 2.0 uses the Unified Medical Language System (UMLS) as its ontology to specify concept types, such as pharmacological substances (PHSU); diseases or syndromes (DSYN) or biologically active substances (BAC); amino acids, peptides, and proteins (AAPP); genes or genomes (GNGM), etc. LBD with SemNet has been very useful for drug repurposing in COVID-19 [ 40 ], ascribing mechanisms of resistant hypertension after COVID-19 infection [ 41 ], assessing the long-term effects of tyrosine kinase inhibitors in chronic myeloid leukemia [ 42 ], and drug repurposing for Parkinson’s disease [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Al-Hussaini,

White,

Varmeziar

et al. 2024

JCM

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Background: Datasets on rare diseases, like pediatric acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), have small sample sizes that hinder machine learning (ML). The objective was to develop an interpretable ML framework to elucidate actionable insights from small tabular rare disease datasets. Methods: The comprehensive framework employed optimized data imputation and sampling, supervised and unsupervised learning, and literature-based discovery (LBD). The framework was deployed to assess treatment-related infection in pediatric AML and ALL. Results: An interpretable decision tree classified the risk of infection as either “high risk” or “low risk” in pediatric ALL (n = 580) and AML (n = 132) with accuracy of ∼79%. Interpretable regression models predicted the discrete number of developed infections with a mean absolute error (MAE) of 2.26 for bacterial infections and an MAE of 1.29 for viral infections. Features that best explained the development of infection were the chemotherapy regimen, cancer cells in the central nervous system at initial diagnosis, chemotherapy course, leukemia type, Down syndrome, race, and National Cancer Institute risk classification. Finally, SemNet 2.0, an open-source LBD software that links relationships from 33+ million PubMed articles, identified additional features for the prediction of infection, like glucose, iron, neutropenia-reducing growth factors, and systemic lupus erythematosus (SLE). Conclusions: The developed ML framework enabled state-of-the-art, interpretable predictions using rare disease tabular datasets. ML model performance baselines were successfully produced to predict infection in pediatric AML and ALL.

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“…The unsupervised learning ranking algorithm within SemNet 2.0 examines relationship patterns in the literature to rank cross-domain concepts with respect to the user-defined concept(s) [31]. SemNet 2.0 has been used for drug repurposing for COVID-19 [34] and Parkinson's disease [35], identifying unknown disease mechanisms of resistant hypertension following COVID-19 infection [36], predicting adverse events from chronic tyrosine kinase inhibitor therapy in chronic myeloid leukemia [29], and identifying clinical features by which to better stratify chemotherapy-related infection risk in pediatric acute leukemia [37].…”

Section: Introductionmentioning

confidence: 99%

“…Ephrin receptors make up the largest subgroup of the receptor tyrosine kinase family, which have a key role in vascular regulation. SemNet 2.0 has previously highlighted the role of tyrosine kinase pathways in resistant hypertension [36].…”

mentioning

confidence: 99%

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Patidar,

Deng,

Mitchell

et al. 2024

IJMS

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Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease worldwide. This study’s goal was to identify the signaling drivers and pathways that modulate glomerular endothelial dysfunction in DKD via artificial intelligence-enabled literature-based discovery. Cross-domain text mining of 33+ million PubMed articles was performed with SemNet 2.0 to identify and rank multi-scalar and multi-factorial pathophysiological concepts related to DKD. A set of identified relevant genes and proteins that regulate different pathological events associated with DKD were analyzed and ranked using normalized mean HeteSim scores. High-ranking genes and proteins intersected three domains—DKD, the immune response, and glomerular endothelial cells. The top 10% of ranked concepts were mapped to the following biological functions: angiogenesis, apoptotic processes, cell adhesion, chemotaxis, growth factor signaling, vascular permeability, the nitric oxide response, oxidative stress, the cytokine response, macrophage signaling, NFκB factor activity, the TLR pathway, glucose metabolism, the inflammatory response, the ERK/MAPK signaling response, the JAK/STAT pathway, the T-cell-mediated response, the WNT/β-catenin pathway, the renin–angiotensin system, and NADPH oxidase activity. High-ranking genes and proteins were used to generate a protein–protein interaction network. The study results prioritized interactions or molecules involved in dysregulated signaling in DKD, which can be further assessed through biochemical network models or experiments.

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Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

Cited by 4 publications

References 158 publications

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

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