Combining 2D and 3D features to classify protein mutants in HeLa cells

Carlo Sansone, Vincenzo Paduano, Michele Ceccarelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

The field of high-throughput applications in biomedicine is an always enlarging field. This kind of applications, providing a huge amount of data, requires necessarily semi-automated or fully automated analysis systems. Such systems are typically represented by classifiers capable of discerning from the different types of data obtained (i.e. classes). In this work we present a methodology to improve classification accuracy in the field of 3D confocal microscopy. A set of 3D cellular images (z-stacks) were taken, each depicting HeLa cells with different mutations of the UCE protein ([Mannose-6-Phosphate] UnCovering Enzyme). This dataset was classified to obtain the mutation class from the z-stacks. 3D and 2D features were extracted, and classifications were carried out with cell by cell and z-stack by z-stack approaches, with 2D or 3D features. Also, a classification approach that combines 2D and 3D features is proposed, which showed interesting improvements in the classification accuracy.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages284-293
Number of pages10
Volume5997 LNCS
DOIs
Publication statusPublished - 14 May 2010
Externally publishedYes
Event9th International Workshop on Multiple Classifier Systems, MCS 2010 - Cairo, Egypt
Duration: 7 Apr 20109 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5997 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other9th International Workshop on Multiple Classifier Systems, MCS 2010
CountryEgypt
CityCairo
Period7/4/109/4/10

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Sansone, C., Paduano, V., & Ceccarelli, M. (2010). Combining 2D and 3D features to classify protein mutants in HeLa cells. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5997 LNCS, pp. 284-293). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5997 LNCS). https://doi.org/10.1007/978-3-642-12127-2-29