An Exploratory Study of Machine Learning-Based Open-Angle Glaucoma Detection Using Specific Autoantibodies

<b>Objectives:</b> Previously, we identified four open-angle glaucoma (OAG)-associated autoantibodies (anti-ETNK1, anti-VMAC, anti-NEXN, and anti-SUN1) using proteome-wide autoantibody screening by wet protein arrays. The objective of this exploratory study was to evaluate the diagnostic performance...

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Bibliografski detalji

Glavni autori:	Naoko Takada, Makoto Ishikawa, Takahiro Ninomiya, Yukitoshi Izumi, Kota Sato, Hiroshi Kunikata, Yu Yokoyama, Satoru Tsuda, Eriko Fukuda, Kei Yamaguchi, Chihiro Ono, Tomoko Kirihara, Chie Shintani, Akiko Hanyuda, Naoki Goshima, Charles F. Zorumski, Toru Nakazawa
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2025-12-01
Serija:	Biomedicines
Teme:	autoantibody open-angle glaucoma diagnosis machine learning
Online pristup:	https://www.mdpi.com/2227-9059/13/12/3031
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