Machine learning in cell biology – teaching computers to recognize phenotypes

By Christoph Sommer, Daniel Gerlich. 2013

326 citations recorded by the source corpus.

Summary Recent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome 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 application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Computer vision for high content screening (2016, methodology): “Comprehensive and modular software has been developed for segmenting and quantifying microscopy images, reviewed here (Eliceiri et al ., 2012; Shamir et al ., 2010; Sommer & Gerlich, 2013).”
Microscopy Image Browser: A Platform for Segmentation and Analysis of Multidimensional Datasets (2016, methodology): “Latest developments in image segmentation aim to minimize manual work by implementing automatic approaches of machine learning [25].”
Applications in image-based profiling of perturbations. (2016, methodology): “This requires flexible software tools for extracting measurements from images and robust computational models for subsequent data analytics [33], whether a single morphological feature is the basis of the screen, or whether machine learning is used to combine multiple morphological features in order”
Machine learning applications in cell image analysis (2017, methodology): “Basic clustering algorithms, such as k-means and hierarchical clustering, have been frequently covered, 4,5 and pitfalls of high-dimensional clustering in biological research have been discussed by Ronan et al. 21”