Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks

Date

2011-01-01

Author

TAN, MEHMET
Alshalalfa, Mohammed
Alhajj, Reda
Polat, Faruk

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

101
views

0
downloads

Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks, which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior knowledge; one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets.

Subject Keywords

Biotechnology, Genetics, Applied Mathematics

URI

https://hdl.handle.net/11511/37851

Journal

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS

DOI

https://doi.org/10.1109/tcbb.2009.58

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

A Divide and Conquer Approach for Construction of Large-Scale Signaling Networks from PPI and RNAi Data Using Linear Programming Ozsoy, Oyku Eren; Can, Tolga (Institute of Electrical and Electronics Engineers (IEEE), 2013-07-01) Inference of topology of signaling networks from perturbation experiments is a challenging problem. Recently, the inference problem has been formulated as a reference network editing problem and it has been shown that finding the minimum number of edit operations on a reference network to comply with perturbation experiments is an NP-complete problem. In this paper, we propose an integer linear optimization (ILP) model for reconstruction of signaling networks from RNAi data and a reference network. The ILP ...
Effective optimization with weighted automata on decomposable trees Ravve, E. V.; Volkovich, Z.; Weber, Gerhard Wilhelm (Informa UK Limited, 2014-01-02) In this paper, we consider quantitative optimization problems on decomposable discrete systems. We restrict ourselves to labeled trees as the description of the systems and we use weighted automata on them as our computational model. We introduce a new kind of labeled decomposable trees, sum-like weighted labeled trees, and propose a method, which allows us to reduce the solution of an optimization problem, defined in a fragment of Weighted Monadic Second Order Logic, on such a tree to the solution of effec...
A tearing-based hybrid parallel sparse linear system solver NAUMOV, Maxim; Manguoğlu, Murat; SAMEH, Ahmed (Elsevier BV, 2010-09-15) We propose a hybrid sparse system solver for handling linear systems using algebraic domain decomposition-based techniques. The solver consists of several stages. The first stage uses a reordering scheme that brings as many of the largest matrix elements as possible closest to the main diagonal. This is followed by partitioning the coefficient matrix into a set of overlapped diagonal blocks that contain most of the largest elements of the coefficient matrix. The only constraint here is to minimize the size ...
Effects of sensitivity derivatives on aerodynamic design optimization Eyi, Sinan (Informa UK Limited, 2007-10-01) The effects of sensitivity derivatives on the performance of aerodynamic design optimization were evaluated. Sensitivity derivatives were obtained by both analytical and finite-difference approaches. A sensitivity code was developed to analytically obtain sensitivity derivatives for the two-dimensional Euler equations using the material derivative concept of continuum mechanics. Several inverse design optimizations were performed to evaluate the merits of the analytical approach in comparison with the finit...
Derivative free multilevel optimization methods Pekmen, Bengisen; Karasözen, Bülent; Department of Scientific Computing (2009) Derivative free optimization algorithms are implementations of trust region based derivative-free methods using multivariate polynomial interpolation. These are designed to minimize smooth functions whose derivatives are not available or costly to compute. The trust region based multilevel optimization algorithms for solving large scale unconstrained optimization problems resulting by discretization of partial differential equations (PDEs), make use of different discretization levels to reduce the computati...

Citation Formats

M. TAN, M. Alshalalfa, R. Alhajj, and F. Polat, “Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks,” IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, pp. 130–142, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37851.