... target validation in situ in the intact cell from single-molecule localization super-resolution microscopy. Super resolution Clathrin Machine learning Small molecule Pitstop Dynasore Canadian Institutes of Health Research http://dx.doi.org/10.13039/501100000024 CIHR CPG – 163989...

... . Neurogastroenterol. Motil. 35 , e14678 . 10.1111/nmo.14678 Berg , S. , Kutra , D. , Kroeger , T. , Straehle , C. N. , Kausler , B. X. , Haubold , C. , Schiegg , M. , Ales , J. , Beier , T. , Rudy , M. et al. (2019). ilastik: interactive machine learning...

...Inês Cunha; Emma Latron; Sebastian Bauer; Daniel Sage; Juliette Griffié ABSTRACT Machine learning (ML) is transforming the field of image processing and analysis, from automation of laborious tasks to open-ended exploration of visual patterns. This has striking implications for image-driven life...

... upregulation compromised mitotic spindle integrity and induced aneuploidy via micronuclei formation. Chromosomal instability Aneuploidy Micronuclei KIF11 Mitosis CRISPR activation Confocal imaging Machine learning Deep learning Cancer Research UK http://dx.doi.org/10.13039/501100000289...

...-source Fiji plugin. We present a feature-based machine learning pipeline to recognize oocyte populations and determine morphological differences between them. We first demonstrate its potential to screen oocytes from different strains and automatically identify their morphological characteristics. Its...

.... J. , Grenier , J. K. , Castoreno , A. B. , Eggert , U. S. , Root , D. E. , Golland , P. et al. (2009). Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning . Proc. Natl. Acad. Sci. USA 106 , 1826 - 1831 . 10.1073...

... to deriving biological insight. Data science Deep learning Imaging Machine learning Microscopy Microscopy provides visual access to cell appearance, organization and behavior, enabling us to discover new biology by observing cells in their basal and perturbed states. The intricate beauty...

...-processing and machine learning-aided approach for the semi-automated analysis of MT organization. We designed a convolutional neural network-based approach for detecting centrosomes, and an automated pipeline for analyzing MT organization around centrosomes, encapsulated in a semi-automatic graphical tool...

... 2020 10 2 2020 © 2020. Published by The Company of Biologists Ltd 2020 http://www.biologists.com/user-licence-1-1/ Summary: A machine learning algorithm can predict the 3D mammalian cell volume to accurate quantify cell volume and cell growth trajectories in standard cell...

... transport to the nuclear pore complex. Adenovirus Fluorescence microscopy Virus entry Microtubule Intracellular transport CRM1 Machine learning Swiss National Science Foundation 10.13039/501100001711 310030B_160316 Swiss National Science Foundation 10.13039/501100001711...

... effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful...