A Machine Learning-Based Analytic Pipeline Applied to Clinical and Serum IgG Immunoproteome Data To Predict Chlamydia trachomatis Genital Tract Ascension and Incident Infection in Women

ABSTRACT We developed a reusable and open-source machine learning (ML) pipeline that can provide an analytical framework for rigorous biomarker discovery. We implemented the ML pipeline to determine the predictive potential of clinical and immunoproteome antibody data for outcomes associated with Ch...

Popoln opis

Bibliografske podrobnosti

Principais autores:	Chuwen Liu, Neha Vivek Mokashi, Toni Darville, Xuejun Sun, Catherine M. O’Connell, Katrin Hufnagel, Tim Waterboer, Xiaojing Zheng
Format:	Artigo
Jezik:	Inglês
Izdano:	American Society for Microbiology 2023-08-01
Serija:	Microbiology Spectrum
Teme:	ascension biomarker Chlamydia genital tract infection incident infection machine learning pipeline
Online dostop:	https://journals.asm.org/doi/10.1128/spectrum.04689-22
Oznake:	Označite Brez oznak, prvi označite!