Automated analysis of high-content microscopy data with deep learning.
Oren Kraus,Ben T. Grys,Jimmy Ba,Yolanda T. Chong,Brendan J. Frey,Charles Boone,Charles Boone,Brenda J. Andrews,Brenda J. Andrews +8 more
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TLDR
This study uses a deep convolutional neural network (DeepLoc) to analyze yeast cell images and shows improved performance over traditional approaches in the automated classification of protein subcellular localization.Abstract:
Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.read more
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Journal ArticleDOI
Opportunities and obstacles for deep learning in biology and medicine.
Travers Ching,Daniel Himmelstein,Brett K. Beaulieu-Jones,Alexandr A. Kalinin,Brian T. Do,Gregory P. Way,Enrico Ferrero,Paul-Michael Agapow,Michael Zietz,Michael M. Hoffman,Michael M. Hoffman,Wei Xie,Gail L. Rosen,Benjamin J. Lengerich,Johnny Israeli,Jack Lanchantin,Stephen Woloszynek,Anne E. Carpenter,Avanti Shrikumar,Jinbo Xu,Evan M. Cofer,Evan M. Cofer,Christopher A. Lavender,Srinivas C. Turaga,Amr Alexandari,Zhiyong Lu,David J. Harris,Dave DeCaprio,Yanjun Qi,Anshul Kundaje,Yifan Peng,Laura K. Wiley,Marwin H. S. Segler,Simina M. Boca,S. Joshua Swamidass,Austin Huang,Anthony Gitter,Anthony Gitter,Casey S. Greene +38 more
TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.
Journal ArticleDOI
DeepLoc: prediction of protein subcellular localization using deep learning.
Jose Juan Almagro Armenteros,Jose Juan Almagro Armenteros,Casper Kaae Sønderby,Søren Kaae Sønderby,Henrik Nielsen,Ole Winther,Ole Winther +6 more
TL;DR: This work presents a prediction algorithm using deep neural networks to predict protein subcellular localization relying only on sequence information, outperforming current state‐of‐the‐art algorithms, including those relying on homology information.
Journal ArticleDOI
Deep learning for cellular image analysis
TL;DR: The intersection between deep learning and cellular image analysis is reviewed and an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists are provided.
Journal ArticleDOI
Super-resolution microscopy demystified
Lothar Schermelleh,Alexia Ferrand,Thomas R Huser,Christian Eggeling,Markus Sauer,Oliver Biehlmaier,Drummen Gpc. +6 more
TL;DR: An overview of current super-resolution microscopy techniques is given and guidance on how best to use them to foster biological discovery is provided.
Journal ArticleDOI
Data-analysis strategies for image-based cell profiling
Juan C. Caicedo,Sam Cooper,Florian Heigwer,Scott Warchal,Peng Qiu,Csaba Molnar,Aliaksei Vasilevich,Joseph Barry,Harmanjit Singh Bansal,Oren Kraus,Mathias Wawer,Lassi Paavolainen,Markus D. Herrmann,Mohammad Hossein Rohban,Jane Hung,Jane Hung,Holger Hennig,John Concannon,Ian Smith,Paul A. Clemons,Shantanu Singh,Paul Rees,Paul Rees,Peter Horvath,Peter Horvath,Roger G. Linington,Anne E. Carpenter +26 more
TL;DR: The steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images are introduced and techniques that have proven useful in each stage of the data analysis process are recommended on the basis of the experience of 20 laboratories worldwide that are refining their image- based cell-profiling methodologies.
References
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.