Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns

By Timothy R. Lezon, Jayanth R. Banavar, Marek Cieplak, Amos Maritan, Nina V. Fedoroff. 2006

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In Molecular Biology

Citations in the corpus

Protein 3D Structure Computed from Evolutionary Sequence Variation (2011, neutral): “approximation [80].”
The Fitness Landscape of HIV-1 Gag: Advanced Modeling Approaches and Validation of Model Predictions by In Vitro Testing (2014, background): “Inference of the parameters of the Ising models, commonly referred to as the inverse Ising problem, is a canonical inverse problem lacking an analytical solution that may be tackled in many ways [16,17,19–21,28,29].”
Principles of maximum entropy and maximum caliber in statistical physics (2013, background): “…al., 2001; Elowitz et al., 2002; Yang and Xie, 2002; Yang et al., 2003; Rhoades et al., 2004; Witkoskie and Cao, 2004, 2008; Cecconi et al., 2005; Lezon et al., 2006; Schneidman et al., 2006; Huang et al., 2007; Vergassola, Villermaux, and Shraiman, 2007; Sanchez and Kondev, 2008; Shahrezaei and…”
3D Protein Structure Predicted from Sequence (2011, unknown): “, for the engineering of stable and fast-‐folding proteins [37], for the analysis of correlated network states in neural populations [38], regulatory gene network modeling from transcript profiles [39], t”
Inferring interaction partners from protein sequences (2016, background): “[21] Lezon TR, Banavar JR, Cieplak M, Maritan A, Fedoroff NV (2006) Using the principle of entropy maximization”
From Principal Component to Direct Coupling Analysis of Coevolution in Proteins: Low-Eigenvalue Modes are Needed for Structure Prediction (2012, unknown): “MaxEnt has been used to analyze many types of biological data, ranging from multi-electrode recording of neural activities [25,26], gene concentrations in genetic networks [27], bird flocking [28] etc.”
Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model. (2018, unknown): “…were used to study the collective behavior of neurons (41,42), infer gene-interaction networks from experimentally observed transcription proﬁles (43), predict residue-residue contacts from multiple sequence alignments (37,44,45), study protein ﬁtness landscapes (46,47), and infer epistatic…”
A Functional Taxonomy of Music Generation Systems (2017, methodology): “The reorchestrations are based on max-entropy models, which are often used in fields such as physics and biology to model probability distributions with observed pairwise correlations (Lezon et al. 2006).”
Control Principles of Complex Networks (2015, background): “The state variable xi(t) can denote the amount of traffic that passes through a node i on a communication network (Pastor-Satorras and Vespignani, 2004), or transcription factor concentration in a gene regulatory network (Lezon et al., 2006).”
Information processing in complex networks: Graph entropy and information functionals (2008, background): “Particularly, the application of information-theoretical methods to analyze graph-based systems is currently of considerable interest [6,7,9,14,22].”
Inverse statistical problems: from the inverse Ising problem to data science (2017, methodology): “For a statistical description of gene expression levels, [126] applied a model with pairwise couplings”
Maximum Entropy Reconstructions of Dynamic Signaling Networks from Quantitative Proteomics Data (2009, methodology): “NwM ), we obtain the interaction matrix by inverting Cij in the space of non-zero eigenvalues[16].”
A Small World of Neuronal Synchrony (2008, methodology): “A similar parameter was used to estimate gene interactions (Lezon et al. 2006).”