Author

L. Bentolila

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

Biology Disciplines

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Bioengineering· 1 Evolutionary Biology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Organism:mouse· 2 Research Focus:cancer & tumor microenvironment· 1 Approach:deep learning· 1 Research Focus:development & morphogenesis· 1 Research Focus:dimensionality reduction· 1 Research Focus:drug response / drug discovery· 1 Assay:fluorescence / live-cell microscopy· 1

Frequent Co-Authors

A. Krause· 1 shared A. Ozcan· 1 shared A. Ridley· 1 shared A. Sadier· 1 shared C. M. Storey· 1 shared C. Yuen· 1 shared D. Abou· 1 shared D. L. Thorek· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperEvolutionary Biology

A. Krause

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Development of a LRRC15-Targeted Radio-Immunotheranostic Approach to Deplete Pro-tumorigenic Mechanisms and Immunotherapy Resistance

C. M. Storey, M. Altai, K. M. Lueckerath, W. Zedan, H. Zhu, M. Trajkovic-Arsic, J. E. Park, N. Peekhaus, J. Siveke, H. Lilljebjorn, D. Abou, H. L. Marks, E. Ulmert, H. Lilja, A. Ridley, , D. Ulmert

This paper develops a radio-theranostic approach targeting LRRC15 to image and treat cancer, showing tumor reduction and reversal of immunotherapy resistance.

bioRxiv · Jan 30, 2024 · PreprintPharmacology & Toxicology

Bat teeth illuminate the diversification of mammalian tooth classes

A. Sadier, N. Anthwal, A. Krause, R. Dessalles, M. Lake, L. Bentolila, R. Haase, N. Nieves, S. Santana, K. E. Sears

The paper studies how tooth classes in bats evolved and diversified by analyzing developmental mechanisms and growth rate modulation.

bioRxiv · Dec 5, 2021Evolutionary Biology

Deep learning achieves super-resolution in fluorescence microscopy

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Gunaydin, L. Bentolila, A. Ozcan

This paper introduces a deep learning method that achieves super-resolution in fluorescence microscopy without requiring numerical models or parameter tuning, successfully resolving images with low numerical aperture lenses and diffraction-limited confocal microscopy.

bioRxiv · Apr 27, 2018Bioengineering