Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach.

ABSTRACT: The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, w

SUBMITTER: Dutta S 

PROVIDER: S-EPMC11503472 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature