PuSH - Publikationsserver des Helmholtz Zentrums München

Engelmann, J.P. ; Palma, A. ; Tomczak, J.M.* ; Theis, F.J. ; Casale, F.P.

Mixed models with multiple instance learning.

In: (Proceedings of Machine Learning Research). 2024. 3664-3672 (Proceedings of Machine Learning Research ; 238)
Predicting patient features from single-cell data can help identify cellular states implicated in health and disease. Linear models and average cell type expressions are typically favored for this task for their efficiency and robustness, but they overlook the rich cell heterogeneity inherent in single-cell data. To address this gap, we introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL), upholding the advantages of linear models while modeling cell state heterogeneity. By leveraging predefined cell embeddings, MixMIL enhances computational efficiency and aligns with recent advancements in single-cell representation learning. Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets, uncovering new associations and elucidating biological mechanisms across different domains.
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Konferenztitel Proceedings of Machine Learning Research
Quellenangaben Band: 238, Heft: , Seiten: 3664-3672 Artikelnummer: , Supplement: ,
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Institute of Computational Biology (ICB)