Connectionist Approaches for Predicting Mouse Gene Function from Gene Expression

  • Emad Andrews Shenouda
  • Quaid Morris
  • Anthony J. Bonner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4232)


Identifying gene function has many useful applications especially in Gene Therapy. Identifying gene function based on gene expression data is much easier in prokaryotes than eukaryotes due to the relatively simple structure of prokaryotes. That is why tissue-specific expression is the primary tool for identifying gene function in eukaryotes. However, recent studies have shown that there is a strong learnable correlation between gene function and gene expression. This paper outlines a new approach for gene function prediction in mouse. The prediction mechanism depends on using Artificial Neural Networks (NN) to predict gene function based on quantitative analysis of gene co-expression. Our results show that neural networks can be extremely useful in this area. Also, we explore clustering of gene functions as a preprocessing step for predicting gene function.


Support Vector Machine Mean Square Error Extreme Learn Machine Binary Classifier Gene Function Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Emad Andrews Shenouda
    • 1
  • Quaid Morris
    • 1
  • Anthony J. Bonner
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoToronto

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