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Your search keyword '"PICCININI, FILIPPO"' showing total 13 results
Start Over Author "PICCININI, FILIPPO" Publisher elsevier bv
13 results on '"PICCININI, FILIPPO"'

8. Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data

Author

Piccinini, Filippo, primary, Balassa, Tamas, additional, Szkalisity, Abel, additional, Molnar, Csaba, additional, Paavolainen, Lassi, additional, Kujala, Kaisa, additional, Buzas, Krisztina, additional, Sarazova, Marie, additional, Pietiainen, Vilja, additional, Kutay, Ulrike, additional, Smith, Kevin, additional, and Horvath, Peter, additional

Published
2017
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13. Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Author

Hossein Azizpour, Kevin Smith, Krisztian Koos, Filippo Piccinini, Tivadar Danka, Tamas Balassa, Peter Horvath, Smith, Kevin, Piccinini, Filippo, Balassa, Tama, Koos, Krisztian, Danka, Tivadar, Azizpour, Hossein, and Horvath, Peter

Subjects
Big Data, 0301 basic medicine, Histology, Computer science, phenotypic image analysi, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, cell classification, Bioinformatik och systembiologi, high-content screening, Field (computer science), Pathology and Forensic Medicine, Machine Learning, 03 medical and health sciences, Software, Phenomics, Image Processing, Computer-Assisted, Animals, Humans, drug screening, single-cell analysi, Image analysis, Microscopy, Bioinformatics and Systems Biology, business.industry, Perspective (graphical), Cell Biology, Data science, High-Throughput Screening Assays, Phenotype, 030104 developmental biology, High-content screening, oncology, Key (cryptography), freely available tool, phenomic, business, Strengths and weaknesses
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. The goal of phenotypic image analysis is to recognize variations in cellular properties using image data—either measurements extracted by image analysis software or directly from the raw pixel values. In this review, we describe free and open-source software tools that are currently available for exploring and quantifying phenotypes in image-based cellular assays. We discuss some of the main challenges, current trends, and future research directions in phenotypic image analysis.

Published
2018

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