Tensor‐structured decomposition improves systems serology analysis

• Published in: Molecular systems biology Vol. 17; no. 9; pp. e10243 - n/a
• Main Authors: Tan, Zhixin Cyrillus, Murphy, Madeleine C, Alpay, Hakan S, Taylor, Scott D, Meyer, Aaron S
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.09.2021; EMBO Press; John Wiley and Sons Inc; Springer Nature
• Subjects: Antibodies; Antigens; Approximation; Binding; Data reduction; Datasets; Decomposition; Disease; effector function; EMBO10; EMBO19; EMBO23; Gene expression; HIV; Human immunodeficiency virus; Humoral immunity; Immune system; Infections; Mathematical analysis; Pattern recognition; Prediction models; Principal components analysis; Regulatory mechanisms (biology); SARS‐CoV‐2; Serology; Severe acute respiratory syndrome coronavirus 2; System effectiveness; systems serology; tensor decomposition; Tensors; effector function; systems serology; HIV; SARS‐CoV‐2; tensor decomposition

Systems serology provides a broad view of humoral immunity by profiling both the antigen‐binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease‐relevant antigen targets, alongside additional measurements made for single antigens or in an antige...