Bryan D. Bryson
Molecular Biology · 2017–2020
A researcher in the Science corpus: 3 papers; trusts 10 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 46 citations into it; #14,781 of 17,773 by trust here, #7,124 by citations
- Immunology — 14 citations into it; #2,138 of 2,160 by trust here, #1,575 by citations
- Artificial Intelligence — 10 citations into it
Whom Bryan D. Bryson trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Moses Charikar
- F. Alexander Wolf
- Oliver Stegle
- Andrew W. Butler
- Laleh Haghverdi
- Dominic Grün
- Sean R. Eddy
- Rahul Satija
- Philipp Angerer
Who trusts Bryan D. Bryson overall
Papers from Bryan D. Bryson's perspective
Ordered by how strongly the researchers Bryan D. Bryson trusts endorse each paper. Same corpus, a different order for every researcher.
- Computational and analytical challenges in single-cell transcriptomics (Nature reviews genetics)
- Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. (Cell)
- Massively parallel digital transcriptional profiling of single cells
- Scikit-learn: Machine Learning in Python
- SCANPY: large-scale single-cell gene expression data analysis
- Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes (Cell Metabolism)
- Single cell RNA Seq reveals dynamic paracrine control of cellular variation (Nature)
- A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. (Blood)
- Integrating single-cell transcriptomic data across different conditions, technologies, and species
- Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (Nature Biotechnology)
Their papers
- Efficient integration of heterogeneous single-cell transcriptomes using Scanorama (2019) — 798 citations
- Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples (2017) — 702 citations
- Learning the language of viral evolution and escape (2020) — 317 citations