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Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).ORCID iD: 0000-0002-6163-191X
IRCCS, Ist Sci Romagnolo Studio & Cura Tumori IRST, Via P Maroncelli 40, I-47014 Meldola, FC, Italy..
Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
Hungarian Acad Sci, Synthet & Syst Biol Unit, BRC, Temesvari Krt 62, H-6726 Szeged, Hungary..
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2018 (English)In: CELL SYSTEMS, ISSN 2405-4712, Vol. 6, no 6, p. 636-653Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
CELL PRESS , 2018. Vol. 6, no 6, p. 636-653
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-232249DOI: 10.1016/j.cels.2018.06.001ISI: 000436877800002PubMedID: 29953863Scopus ID: 2-s2.0-85048445198OAI: oai:DiVA.org:kth-232249DiVA, id: diva2:1233964
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180720

Available from: 2018-07-20 Created: 2018-07-20 Last updated: 2018-07-20Bibliographically approved

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Smith, KevinAzizpour, Hossein

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Science for Life Laboratory, SciLifeLabComputational Science and Technology (CST)School of Electrical Engineering and Computer Science (EECS)
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