Author

B. Sanderson

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

Biology Disciplines

Click to filter author papers

Bioengineering· 1 Cancer Biology· 1 Plant Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:development & morphogenesis· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Organism:human· 1 Assay:imaging· 1

Frequent Co-Authors

B. Byrom· 1 shared D. Gambhir· 1 shared F. Mowlem· 1 shared G. Feng· 1 shared H. Hale· 1 shared H. Li· 1 shared J. C. Rubinstein· 1 shared J. H. Chuang· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperBioengineering

B. Byrom

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Spatiotemporal profiling defines persistence and resistance dynamics during targeted treatment of melanoma

J. C. Rubinstein, S. Domanskyi, T. B. Sheridan, B. Sanderson, S. Park, J. Kaster, H. Li, O. Anczukow, M. Herlyn, J. H. Chuang

This study uses spatial transcriptomics and deep learning to analyze melanoma treatment resistance, identifying persister states and resistance mechanisms with spatiotemporal resolution.

bioRxiv · Feb 2, 2024Cancer Biology

Reproductive sexual dimorphisms in two willow species, Salix exigua Nutt. and S. nigra Marshall.

N. Hu, H. Hale, B. Sanderson, G. Feng, M. Guo, D. Gambhir, M. Olson

The paper examines reproductive sexual dimorphisms in two willow species, finding males produce more flowers per catkin and catkins per branch than females.

bioRxiv · May 18, 2023 · PreprintPlant Biology

Novel algorithms deriving clinical performance measures from smartphone sensor data collected under a walking test

M. A. Little, S. Volotinen, B. Sanderson, U. Huopaniemi, F. Mowlem, J. Olt, B. Byrom

This paper develops and tests smartphone app algorithms to measure gait metrics using sensor data from walking tests, achieving 2% error across metrics.

bioRxiv · Oct 21, 2021 · PreprintBioengineering