Author

Francisco Jose Grisanti Canozo

Baylor College of Medicine

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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Systems Biology· 2

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Md. Abul Hassan SameeiD· 2 shared James F. MartiniD· 1 shared Minjun ParkiD· 1 shared Salvi Singh· 1 shared Zhen C. ZuoiD· 1 shared

Activity by Year

2020-2020 · peak 2/yr

Co-Author Collaboration Network

5 top collaborators · 6 mutual co-author links

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2 shared papersSystems Biology

Md. Abul Hassan SameeORCID

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2 papers

Recent·Most cited

Systems BiologybioRxivPreprintSep 9, 2020

Integrative Analysis of Spatial and Single-Cell Transcriptomics Reveals Principles of Tissue Organization and Intercellular Communication in Mouse Olfactory Bulb

Francisco Jose Grisanti Canozo, Zhen C. Zuo, James F. Martin, Md. Abul Hassan Samee

The paper develops STANN, a neural network model to integrate spatial transcriptomics and scRNA-seq data, revealing spatial organization and intercellular communication principles in mouse olfactory bulb.

DOI 10.1101/2020.09.09.290064

mousespatial transcriptomics

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Systems BiologybioRxivPreprintJul 28, 2020

Multinomial Convolutions for Joint Modeling of Sequence Motifs and Enhancer Activities

Minjun Park, Salvi Singh, Francisco Jose Grisanti Canozo, Md. Abul Hassan Samee

The paper introduces MuSeAM, a convolutional neural network that models sequence-specific motifs and enhancer activities from MPRAs, achieving high accuracy and generalizing to related tasks.

2 citations

DNA methylationdeep learning

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