35. DOUBLY PENALIZED LASSO FOR RECONSTRUCTION OF BIOLOGICAL NETWORKS

Department: Bioengineering
Faculty Advisor(s): Shankar Subramaniam | Daniel Tartakovsky

Primary Student
Name: Behrang Asadi
Email: basadi@ucsd.edu
Phone: 858-822-0960
Grad Year: 2013

Student Collaborators
Mano R. Maurya, mano@sdsc.edu

Abstract
Data-driven network reconstruction of biological systems is an essential step towards extracting information from large volumes of biological data. There are several methods developed recently to reconstruct biological networks. However, to the best of our knowledge, few systematic application-specific algorithms and methods have been developed to reconstruct the networks for dynamic biological systems. Different data properties such as level and types of the noise, level of correlation/collinearity, size of the dataset, and portion of missing data on one hand, and existing knowledge such as partially-known signaling pathways, data pattern, and known time-scales of dynamic datasets on the other hand, provide a good motivation to develop a new method for reconstruction of dynamic biological networks based on both data and system properties. In this work, we have developed a new method called Doubly Panelized Linear Absolute Shrinkage and Selection Operator (DP-LASSO) for reconstruction of dynamic biological networks. In this method, we have implemented partial least squares as a supervisory level filter to extract the most informative components of the network from the dataset. In the lower level reconstruction engine, we apply LASSO with extra weights on the parameters with smaller-values in partial least squares analysis in the first layer to maintain the principal components and nullify the remaining small coefficients. Simulation results show fair improvements in accuracy and sensitivity of the algorithm in reconstruction of artificial networks. Application of DP-LASSO algorithm to experimental datasets for yeast cell cycle also demonstrates a good fidelity of this method in network reconstruction.

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