logo
ResearchBunny Logo
Topaz-Denoise: general deep denoising models for cryoEM and cryoET

Biology

Topaz-Denoise: general deep denoising models for cryoEM and cryoET

T. Bepler, K. Kelley, et al.

Discover Topaz-Denoise, a revolutionary deep learning method designed by Tristan Bepler, Kotaro Kelley, Alex J. Noble, and Bonnie Berger, that enhances the signal-to-noise ratio of cryoEM images, allowing for clearer micrograph interpretation and accelerated data collection. This innovative approach makes it possible to solve complex 3D structures with ease!

00:00
00:00
Playback language: English
Citation Metrics
Citations
0
Influential Citations
0
Reference Count
0

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

Related Publications

Explore these studies to deepen your understanding of the subject.

A general and transferable deep learning framework for predicting phase formation in materials
Engineering and Technology

A general and transferable deep learning framework for predicting phase formation in materials

S. Feng, H. Fu, et al.

Shared computational principles for language processing in humans and deep language models
Psychology

Shared computational principles for language processing in humans and deep language models

A. Goldstein, Z. Zada, et al.

Neural general circulation models for weather and climate
Earth Sciences

Neural general circulation models for weather and climate

D. Kochkov, J. Yuval, et al.

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery
Medicine and Health

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery

C. Boissin, L. Laflamme, et al.

Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny