Author

A. Save

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Cancer Biology· 1 Pathology· 1

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Research Focus:cell type annotation· 2 Research Focus:clustering· 2 Approach:deep learning· 2 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:gene regulatory network inference· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

D. Boyett· 2 shared J. Li· 2 shared O. Al-Dalahmah· 2 shared P. Canoll· 2 shared A. Dovas· 1 shared A. Hawkins-Daarud· 1 shared A. Kannan· 1 shared A. Lassman· 1 shared

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2021-2024 · peak 1/yr

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10 top collaborators · 40 mutual co-author links

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2 shared papersCancer Biology

D. Boyett

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2 papers

Recent·Most cited

Biologically-informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post-treatment glioblastoma

H. Wang, M. G. Argenziano, H. Yoon, D. Boyett, A. Save, P. Petridis, W. Savage, P. Jackson, A. Hawkins-Daarud, N. Tran, L. Hu, O. Al-Dalahmah, J. N. Bruce, J. Grinband, K. R. Swanson, , J. Li

This paper develops BioNet, a biologically-informed deep learning model, to predict intratumoral heterogeneity in post-treatment glioblastoma using MRI and biopsy data, showing improved performance over existing methods.

npj Digital Medicine · Oct 19, 2024Peer-reviewedCancer Biology

Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic andtargetable tissue states

O. Al-Dalahmah, M. Argenziano, A. Kannan, A. Mahajan, J. Furnari, F. Paryani, D. Boyett, A. Save, N. Humala, F. Khan, J. Li, H. Lu, Y. Sun, J. Tuddenham, a. r. goldberg, , P. Canoll

The paper identifies three tissue states in glioblastoma microenvironments using snRNAseq and spatial transcriptomics, linking them to metabolic pathways and clinical outcomes.

bioRxiv · Jul 6, 2021Pathology