541. In Silico Modeling by Causal Inference for Identifying Biomarkers of Sepsis
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ABSTRACT: Background
“Every 3 seconds, someone in the world dies of sepsis” (https://sepsistrust.org/about/). We used causal inference theory as a in silico method to identify biomarkers of sepsis. Causal Inference is a theory in Machine Learning that seeks for the root causes of an event. Methods
In the study it was used transcription profile data downloaded from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12624. This dataset has 70 samples, being 34 sepsis and 36 non-sepsis samples. Data set contains 8,519 attributes: 7,672 genes obtained after preprocessing of the mRNA expression profile data. The method applied in the dataset was a modification in the HEISA, a local learner of two stages algorithm (Figure 1). In the first stage HEISA identifies variables that compounds the set of parents, children, parents of parents and children of children of a target. During the second stage is calculated the causal effect using do-calculus method, of the selected variables of the first stage in the target. At end of the second stage, features with causal effect greater than 0.2 is selected. After selecting the features mRNA expression, it was applied two algorithms of classification, Random Forest and K-means, in order to evaluate the ability of the selected variables of identifying the occurrence of sepsis.
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PROVIDER: S-EPMC9752052 | biostudies-literature | 2022 Dec
REPOSITORIES: biostudies-literature
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