Results 1 - 10
of
18
Structure and dynamics of molecular networks: A novel paradigm of drug discovery -- A . . .
- PHARMACOLOGY THERAPEUTICS
, 2013
"... ..."
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods
, 2011
"... Motivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differenti ..."
Abstract
-
Cited by 38 (0 self)
- Add to MetaCart
Motivation: Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Results: Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). Availability: GNW is available at
An Overview of the Statistical Methods Used for Inferring Gene Regulatory
- Networks and Protein-Protein Interaction Networks. Advances in Bioinformatics 2013: Article ID 953814
, 2013
"... The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling o ..."
Abstract
-
Cited by 1 (1 self)
- Add to MetaCart
(Show Context)
The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.
Category TRANSWESD: inferring cellular networks with transitive reduction
"... Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facili-tates detection and removal of false positive edges. Transitive re-duction is one approach for eliminating edges reflecting indirect effects but its use in re ..."
Abstract
- Add to MetaCart
(Show Context)
Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facili-tates detection and removal of false positive edges. Transitive re-duction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic. Results: We present TRANSWESD, an elaborated variant of TRANSitive reduction for WEighted Signed Digraphs that over-comes conceptual problems of existing versions. Major changes and improvements concern (i) new statistical approaches for generating high-quality perturbation graphs from systematic perturbation ex-periments; (ii) the use of edge weights (association strengths) for recognizing true redundant structures; (iii) causal interpretation of cycles; (iv) relaxed definition of transitive reduction; (v) approxima-tion algorithms for large networks. Using standardized benchmark tests we demonstrate that our method outperforms existing variants of transitive reduction and is, despite its conceptual simplicity, highly competitive with other reverse engineering methods. Contact:
Systems biology Advance Access publication July 6, 2010 TRANSWESD
, 2010
"... inferring cellular networks with transitive reduction ..."
A Novel Hybrid Framework for Reconstructing Gene Regulatory Networks
"... Much effect has been devoted over the past decade to inference of gene regulatory networks (GRNs). However, the previous methods infer GRNs containing large amount of false positive edges, which could result in awful influence on biological analysis. In this study, we present a novel hybrid framewor ..."
Abstract
- Add to MetaCart
(Show Context)
Much effect has been devoted over the past decade to inference of gene regulatory networks (GRNs). However, the previous methods infer GRNs containing large amount of false positive edges, which could result in awful influence on biological analysis. In this study, we present a novel hybrid framework to improve the accuracy of GRN inference. In our method, network topologies from linear and nonlinear ordinary differential equation (ODE) models are integrated. The additive tree models are proposed for identification of linear/nonlinear models. We also propose a new criterion function that sparse and relevant terms are considered while inferring linear and nonlinear models. Benchmark datasets from Dialogue for Reverse Engineering Assessments and Methods challenge and real biological dataset from SOS DNA repair network in Escherichia coli are used to test the validity of our method. Results reveal that our proposed method can improve the prediction accuracy of GRN inference effectively and performs better than other popular methods.
Integrative random forest for gene regulatory network inference
"... *To whom correspondence should be addressed. Motivation: Gene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usu-ally provide complementary information regarding the underlying ..."
Abstract
- Add to MetaCart
*To whom correspondence should be addressed. Motivation: Gene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usu-ally provide complementary information regarding the underlying GRN, a model that integrates big data of diverse types is expected to increase both the power and accuracy of GRN inference. Towards this goal, we propose a novel algorithm named iRafNet: integrative random forest for gene regulatory network inference. Results: iRafNet is a flexible, unified integrative framework that allows information from heteroge-neous data, such as protein–protein interactions, transcription factor (TF)-DNA-binding, gene knock-down, to be jointly considered for GRN inference. Using test data from the DREAM4 and DREAM5 challenges, we demonstrate that iRafNet outperforms the original random forest based network inference algorithm (GENIE3), and is highly comparable to the community learning ap-proach. We apply iRafNet to construct GRN in Saccharomyces cerevisiae and demonstrate that it improves the performance in predicting TF-target gene regulations and provides additional func-tional insights to the predicted gene regulations. Availability and implementation: The R code of iRafNet implementation and a tutorial are available
unknown title
"... OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks ..."
Abstract
- Add to MetaCart
OKVAR-Boost: a novel boosting algorithm to infer nonlinear dynamics and interactions in gene regulatory networks
