Network component analysis: Reconstruction of regulatory signals in biological systems

By James C. Liao, Riccardo Boscolo, Young-Lyeol Yang, L. Tran, Chiara Sabatti, Vwani P. Roychowdhury. Proceedings of the National Academy of Sciences of the United States of America, 2003

645 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/87921fa58d2c96b39a0e45132e03e98b288f8fb1

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Citations in the corpus

Modelling transcriptional regulation using Gaussian Processes (2006, background): “This line of research was started in [3] and then extended further to include gene-specific effects in [10, 11].”
COBRApy: COnstraints-Based Reconstruction and Analysis for Python (2013, methodology): “Constraint based modeling approaches have been widely applied in the field of microbial metabolic engineering [1,2] and have been employed in the analysis [3-5] and, to a lesser extent, modeling of transcriptional [6-8] and signaling [9] networks.”
Inference of complex biological networks: distinguishability issues and optimization-based solutions (2011, background): “The importance of identifiability has been recognized previously in systems biology, too [14,61-64].”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “Pioneering efforts to identify regulatory interactions on a genome scale have used machine-learning algorithms to identify cis-regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genomewide location analysis chromatin immunoprecipitation (ChIP-Chip) ”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, methodology): “Other methods which were developed recently for inverse problems in biological systems are discussed in [139,205–210].”
ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells (2009, background): “Most models are based on linear regression, but there are also extensions that include network component analysis (9), multivariate adaptive regression splines (10, 11), Bayesian error analysis model (12), boosting and Bayesian additive regression trees (13), partial least squares (14), motif expres”
Computational and analytical challenges in single-cell transcriptomics (2015, methodology): “To improve the robustness of the inference of co-regulation, methods that group genes into regulatory modules 77 or that account for unmeasured regulators 78 have been considered.”
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities (2006, background): “This idea has been pursued by a number of research groups (Liao et al., 2003; Gao et al., 2004; Boulesteix and Strimmer, 2005).”
The mathematics of tanning (2009, neutral): “Assuming that TFs with similar TFA profiles might function together [12], it is interesting to check for possible regulatory associations among these factors targeting the M phase-specific clusSPCC320.”
Advantages and limitations of current network inference methods (2010, methodology): “Liao et al.(112) constrain A using the known network, whereas Sabbatti et al.”
Studying and modelling dynamic biological processes using time-series gene expression data (2012, background): “By solving a large-scale optimization problem (in which several genes are assumed to be regulated by a small number of transcription factors) researchers aim to identify this (hidden) activity profile and to determine the set of interactions at the various time points 71,83,84 .”
Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks (2005, background): “Table 2 Pathways and associated genes targeted by drug compounds Drug Known pathway Known target Significant GO ontology (rank, P-value) Ranked pathway genes (rank) Terbinafine Ergosterol biosynthesis41 Erg1 Steroid metabolism (1, 10–14) ERG7 (4), ERG1 (5), ERG8 (11), ERG26 (13), UPC2 (17), ERG28 (1”