| Literature DB >> 29953863 |
Kevin Smith1, Filippo Piccinini2, Tamas Balassa3, Krisztian Koos3, Tivadar Danka3, Hossein Azizpour1, Peter Horvath4.
Abstract
Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.Keywords: cell classification; drug screening; freely available tools; high-content screening; machine learning; microscopy; oncology; phenomics; phenotypic image analysis; single-cell analysis
Mesh:
Year: 2018 PMID: 29953863 DOI: 10.1016/j.cels.2018.06.001
Source DB: PubMed Journal: Cell Syst ISSN: 2405-4712 Impact factor: 10.304