A Closed-Form Solution for Transcription Factor Activity Estimation Using Network Component Analysis

  • Amina Noor
  • Aitzaz Ahmad
  • Bilal Wajid
  • Erchin Serpedin
  • Mohamed Nounou
  • Hazem Nounou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8542)


Non-iterative network component analysis (NINCA), proposed by Jacklin, employs convex optimization methods to estimate the transcription factor control strengths and transcription factor activities. While NINCA provides good estimation accuracy and higher consistency, the costly optimization routine used therein renders a high computational complexity. This correspondence presents a closed form solution to estimate the connectivity matrix which is tens of times faster, and provides similar accuracy and consistency, thus making the closed form NINCA (CFNINCA) algorithm useful for large data sets encountered in practice. The proposed solution is assessed for accuracy and consistency using synthetic and yeast cell cycle data sets by comparing with the existing state-of-the-art algorithms. The robustness of the algorithm to the possible inaccuracies in prior information is also analyzed and it is observed that CFNINCA and NINCA are much more robust to erroneous prior information as compared to FastNCA.


Gene Regulatory Network transcription factor activity convex optimization 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Amina Noor
    • 1
  • Aitzaz Ahmad
    • 2
  • Bilal Wajid
    • 1
  • Erchin Serpedin
    • 1
  • Mohamed Nounou
    • 3
  • Hazem Nounou
    • 4
  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Corporate Research & DevelopmentQualcomm Technologies Inc.San DiegoUSA
  3. 3.Department of Chemical EngineeringTexas A&M University at QatarDohaQatar
  4. 4.Department of Electrical EngineeringTexas A&M University at QatarDohaQatar

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