Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease was written by Kehoe, Eric R.;Fitzgerald, Bryna L.;Graham, Barbara;Islam, M. Nurul;Sharma, Kartikay;Wormser, Gary P.;Belisle, John T.;Kirby, Michael J.. And the article was included in Scientific Reports in 2022.Application of 94-62-2 This article mentions the following:
We provide a pipeline for data preprocessing, biomarker selection, and classification of liquid chromatog.-mass spectrometry (LCMS) serum samples to generate a prospective diagnostic test for Lyme disease. We utilize tools of machine learning (ML), e.g., sparse support vector machines (SSVM), iterative feature removal (IFR), and k-fold feature ranking to select several biomarkers and build a discriminant model for Lyme disease. We report a 98.13% test balanced success rate (BSR) of our model based on a sequestered test set of LCMS serum samples. The methodol. employed is general and can be readily adapted to other LCMS, or metabolomics, data sets. In the experiment, the researchers used many compounds, for example, (2E,4E)-5-(Benzo[d][1,3]dioxol-5-yl)-1-(piperidin-1-yl)penta-2,4-dien-1-one (cas: 94-62-2Application of 94-62-2).
(2E,4E)-5-(Benzo[d][1,3]dioxol-5-yl)-1-(piperidin-1-yl)penta-2,4-dien-1-one (cas: 94-62-2) belongs to piperidine derivatives. Piperidine has a role as a reagent, a protic solvent, a base, a catalyst, a plant metabolite, a human metabolite and a non-polar solvent. Some chemotherapeutic agents have piperidine moiety within their structure, foremost among them, vinblastine and raloxifene.Application of 94-62-2
Referemce:
Piperidine – Wikipedia,
Piperidine | C5H11N – PubChem