Second order optimization for the inference of gene regulatory pathways

Das Mouli (), Murthy Chivukula A. and K. De Rajat
Additional contact information
Das Mouli: Machine Intelligence Unit (MIU), Indian Statistical Institute (I.S.I), 203, B. T. Road, Kolkata 700 108, India
Murthy Chivukula A.: Machine Intelligence Unit (MIU), Indian Statistical Institute (I.S.I), 203, B. T. Road, Kolkata 700 108, India
K. De Rajat: Machine Intelligence Unit (MIU), Indian Statistical Institute (I.S.I), 203, B. T. Road, Kolkata 700 108, India

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 1, 19-33

Abstract: With the increasing availability of experimental data on gene interactions, modeling of gene regulatory pathways has gained special attention. Gradient descent algorithms have been widely used for regression and classification applications. Unfortunately, results obtained after training a model by gradient descent are often highly variable. In this paper, we present a new second order learning rule based on the Newton’s method for inferring optimal gene regulatory pathways. Unlike the gradient descent method, the proposed optimization rule is independent of the learning parameter. The flow vectors are estimated based on biomass conservation. A set of constraints is formulated incorporating weighting coefficients. The method calculates the maximal expression of the target gene starting from a given initial gene through these weighting coefficients. Our algorithm has been benchmarked and validated on certain types of functions and on some gene regulatory networks, gathered from literature. The proposed method has been found to perform better than the gradient descent learning. Extensive performance comparison with the extreme pathway analysis method has underlined the effectiveness of our proposed methodology.

Keywords: flow vector; morphogenesis; node-edge incidence matrix; segment polarity gene; transcription factor (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2012-0021 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:13:y:2014:i:1:p:19-33:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2012-0021

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().