Journal ArticleDOI
bLARS: An Algorithm to Infer Gene Regulatory Networks
TLDR
This work proposes a new regression based method named bLARS that permits a variety of regulatory interactions from a predefined but otherwise arbitrary family of functions and offers the best performance among currently available similar algorithms.Abstract:
Inferring gene regulatory networks (GRNs) from high-throughput gene-expression data is an important and challenging problem in systems biology. Several existing algorithms formulate GRN inference as a regression problem. The available regression based algorithms are based on the assumption that all regulatory interactions are linear. However, nonlinear transcription regulation mechanisms are common in biology. In this work, we propose a new regression based method named bLARS that permits a variety of regulatory interactions from a predefined but otherwise arbitrary family of functions. On three DREAM benchmark datasets, namely gene expression data from E. coli, Yeast, and a synthetic data set, bLARS outperforms state-of-the-art algorithms in the terms of the overall score . On the individual networks, bLARS offers the best performance among currently available similar algorithms, namely algorithms that do not use perturbation information and are not meta-algorithms. Moreover, the presented approach can also be utilized for general feature selection problems in domains other than biology, provided they are of a similar structure.read more
Citations
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Journal ArticleDOI
Network inference in systems biology: recent developments, challenges, and applications
TL;DR: This paper reviews the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more, and discusses unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data.
Journal ArticleDOI
Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
Priyanka Dhingra,Alexander Martinez-Fundichely,Adeline Berger,Franklin W. Huang,Franklin W. Huang,Andre Neil Forbes,Eric Minwei Liu,Deli Liu,Andrea Sboner,Andrea Sboner,Pablo Tamayo,Pablo Tamayo,David S. Rickman,David S. Rickman,Mark A. Rubin,Mark A. Rubin,Ekta Khurana +16 more
TL;DR: A novel computational method is reported to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network.
Journal ArticleDOI
A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints
TL;DR: Given the intrinsic interdisciplinary nature of gene regulatory network inference, this work presents a review on the currently available approaches, their challenges and limitations and proposes guidelines to select the most appropriate method considering the underlying assumptions and fundamental biological and data constraints.
Journal ArticleDOI
Gene regulatory networks on transfer entropy (GRNTE): a novel approach to reconstruct gene regulatory interactions applied to a case study for the plant pathogen Phytophthora infestans.
Juan Camilo Castro,I.D. Valdes,Laura Natalia González-García,Giovanna Danies,Silvia Cañas,Flavia Vischi Winck,Carlos Eduardo Ñústez,Silvia Restrepo,Diego Mauricio Riaño-Pachón +8 more
TL;DR: The results suggest that GRNTE is comparable with the state-of-the-art methods when the parameters for edge detection are properly tuned, and applications of the methodology showed that it could reliably predict where to place edges in the transcriptional networks and sub-networks.
References
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Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Journal ArticleDOI
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
Journal ArticleDOI
WGCNA: an R package for weighted correlation network analysis.
Peter Langfelder,Steve Horvath +1 more
TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
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