CytoBERT: A Foundation Model for Cytometry Data
Cytometry measures the complex characteristics of single cells (e.g., counts and protein expression of immune cells) and is widely used across immunological research and clinical settings. However, cytometry data is highly heterogeneous and unstandardized due to experimental protocols and the choice of measured features. While machine learning methods hold the potential to gain deeper insights into cell biology, these challenges make them difficult to apply and transfer across studies. Recent advances in foundation models can alleviate these issues, but corresponding approaches are still scarc
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- PossiblePossibly related (embedding) · 50%Explainable machine learning to predict immunotherapy outcomes in metastatic renal cell carcinoma - Meet-URO 15-AI study - Nature →
- PossiblePossibly related (embedding) · 46%Machine learning for drug metabolism prediction: CYPs, Phase II enzymes, and metabolite ID - Drug Discovery News →
- PossiblePossibly related (embedding) · 48%Using data to predict how cancer cells respond to various drugs - The Florida Times-Union →
- LinkedLinked via arxiv author · 85%Syed Abdul Haseeb Qadri →
“CytoBERT: A Foundation Model for Cytometry Data”
- LinkedLinked via arxiv author · 85%Bjarne C. Hiller →
“CytoBERT: A Foundation Model for Cytometry Data”
- LinkedLinked via arxiv author · 85%Felix Blanke →
“CytoBERT: A Foundation Model for Cytometry Data”
- LinkedLinked via arxiv author · 85%Vanja Sophie Cangalovic →
“CytoBERT: A Foundation Model for Cytometry Data”
- LinkedLinked via arxiv author · 85%Kutalmış Coşkun →
“CytoBERT: A Foundation Model for Cytometry Data”