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Ranking and 1-Dimensional Projection of Cell Development Transcription Profiles

  • Lan Zagar
  • Francesca Mulas
  • Riccardo Bellazzi
  • Blaz Zupan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6747)

Abstract

Genome-scale transcription profile is known to be a good reporter of the state of the cell. Much of the early predictive modelling and cell-type clustering relied on this relation and has experimentally confirmed it. We have examined if this also holds for prediction of cell’s staging, and focused on the inference of stage prediction models for stem cell development. We show that the problem relates to rank learning and, from the user’s point of view, to projection of transcription profile data to a single dimension. Our comparison of several state-of-the-art algorithms on 10 data sets from Gene Expression Omnibus shows that rank-learning can be successfully applied to developmental cell staging, and that relatively simple techniques can perform surprisingly well.

Keywords

cell development staging temporal ordering ranking projection regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lan Zagar
    • 1
  • Francesca Mulas
    • 2
  • Riccardo Bellazzi
    • 2
  • Blaz Zupan
    • 1
  1. 1.University of LjubljanaLjubljanaSlovenia
  2. 2.University of PaviaPaviaItaly

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