Inferring COVID-19 Biological Pathways from Clinical Phenotypes Via Topological Analysis

Karisani, Negin; Platt, Daniel E.; Basu, Saugata; Parida, Laxmi

doi:10.1007/978-3-030-93080-6_12

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…First, we retrieved a representative cycle for each bar in Figure 7. Next, we selected the cycles based on two factors: (1) Cycles that are dominated by sparse clusters are weak for inferring clinical hypothesis, hence it is important to note the number of subjects. (2) With respect to the size of the clusters, cycles with low Jaccard distances have higher preference.…”

Section: Resultsmentioning

confidence: 99%

“…Concept extraction. We carry out the first phase in three steps: (1) We parse the clinical notes and map the biological terms to the concepts in a medical ontology. This step uniforms physicians' records and utilizes techniques in natural language processing (NLP), which are widely applied to analyze biomedical documents.…”

Section: Proposed Pipelinementioning

confidence: 99%

“…In this article, we propose an efficient pipeline to infer the existence of multiple alternative biological pathways from clinical phenotypes. The shorter version of the article was presented by Karisani et al 1 As used here, “Pathway” refers to physiologically connected processes, ranging from a cascade such as the clotting cascade, to looser systems such as the renin–aldesterone–angiotensin system (RAAS) that angiotensin-converting enzyme 2 (ACE2) is a part of. Our approach is to identify distinctive phenotypic clusters satisfying logical relationships (implication) and to seek possible connections to the underlying pathways.…”

Section: Introductionmentioning

confidence: 99%

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Topology and redescriptions detect multiple alternative biological pathways from clinical phenotypes

Karisani

Platt

Basu

et al. 2022

Exp Biol Med (Maywood)

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Biological pathways play a crucial role in the properties of diseases and are important in drug discovery. Identifying the logical relationships among distinctive phenotypic clusters could reveal possible connections to the underlying pathways. However, this process is challenging since clinical phenotypes are often available through unstructured electronic health records. Moreover, in the absence of a standardized questionnaire, there could be bias among physicians toward selecting certain medical terms. In this article, we develop an efficient pipeline to address these challenges and help practitioners to reveal the pathways associated with the disease. We use topological data analysis and redescriptions and propose a pipeline of four phases: (1) pre-processing the clinical notes to extract the salient concepts, (2) constructing a feature space of the patients to characterize the extracted concepts, (3) leveraging the topological properties to distill the available knowledge and visualize the extracted features, and finally, (4) investigating the bias in the clinical notes of the selected features and identify possible pathways. Our experiments on a publicly available dataset of COVID-19 clinical notes testify that our pipeline can indeed extract meaningful pathways.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Pipelinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Topology and redescriptions detect multiple alternative biological pathways from clinical phenotypes

Karisani

Platt

Basu

et al. 2022

Exp Biol Med (Maywood)

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“…In this study, we used a unique approach for redescription-based topological data analysis (RTDA) ( Karisani et al 2021 ) of clinical records to address critical epidemiological questions. We compared RTDA with standard epidemiological analysis techniques ( Platt et al 2016 ) on data from Explorys to explore the physiology of risk factors elucidated by C19 associations, through interactions with RAAS-targeted HT drugs ( Ferrari 2013 ) and HL interaction with other risk factors for severe C19.…”

Section: Introductionmentioning

confidence: 99%

Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions

Platt,

Bose,

Rhrissorrakrai

et al. 2024

Bioinformatics

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Motivation The emergence of COVID-19 (C19) created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. To address the challenges of discovering clinically relevant interactions, we employed a unique approach for epidemiological analysis powered by redescription-based topological data analysis (RTDA). Results Here, RTDA was applied to Explorys data to discover associations among severe C19 and metabolic syndrome. This approach was able to further explore the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with C19, as well as modification of risk factor impact by hyperlipidemia (HL) on severe C19. RTDA found higher-order relationships between RAAS pathway and severe C19 along with demographic variables of age, gender, and comorbidities such as obesity, statin prescriptions, HL, chronic kidney failure, and disproportionately affecting Black individuals. RTDA combined with CuNA (cumulant-based network analysis) yielded a higher-order interaction network derived from cumulants that furthered supported the central role that RAAS plays. TDA techniques can provide a novel outlook beyond typical logistic regressions in epidemiology. From an observational cohort of electronic medical records, it can find out how RAAS drugs interact with comorbidities, such as hypertension and HL, of patients with severe bouts of C19. Where single variable association tests with outcome can struggle, TDA’s higher-order interaction network between different variables enables the discovery of the comorbidities of a disease such as C19 work in concert. Availability and Implementation Code for performing TDA/RTDA is available in https://github.com/IBM/Matilda and code for CuNA can be found in https://github.com/BiomedSciAI/Geno4SD/. Supplementary Information Supplementary data are available at Bioinformatics online.

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“…In this study, we draw upon the Explorys 21 dataset using both standard and novel 22,23 epidemiological analysis techniques to explore the physiology of these risk factors elucidated by C19 associations, particularly through interactions with RAAS-targeted HT drugs, 7 and the interaction of HL with other risk factors for severe C19. To address this epidemiological question, we developed an approach for Redescription-based Topological Data Analysis (RTDA) of clinical records to test our specific hypotheses and that can be generalized to address other epidemiological questions.…”

Section: Introductionmentioning

confidence: 99%

Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions

Platt

Bose

Levovitz

et al. 2022

Preprint

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The emergence of COVID19 created incredible worldwide challenges but offers unique opportunities to understand the physiology of its risk factors and their interactions with complex disease conditions, such as metabolic syndrome. Epidemiological analysis powered by topological data analysis (TDA) is a novel approach to uncover these clinically relevant interactions. Here TDA utilized Explorys data to discover associations among severe COVID19 and metabolic syndrome, and it explored the probative value of drug prescriptions to capture the involvement of RAAS and hypertension with COVID19 as well as modification of risk factor impact by hyperlipidemia on severe COVID19.

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Inferring COVID-19 Biological Pathways from Clinical Phenotypes Via Topological Analysis

Cited by 4 publications

References 27 publications

Topology and redescriptions detect multiple alternative biological pathways from clinical phenotypes

Topology and redescriptions detect multiple alternative biological pathways from clinical phenotypes

Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions

Epidemiological topology data analysis links severe COVID-19 to RAAS and hyperlipidemia associated metabolic syndrome conditions

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