| scholarly article | Q13442814 |
| P2093 | author name string | Andrea De Martino | |
| Daniele De Martino | |||
| P2860 | cites work | Genetic network inference: from co-expression clustering to reverse engineering | Q34094471 |
| Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design | Q34334803 | ||
| Characterizing species abundance distributions across taxa and ecosystems using a simple maximum entropy model | Q34396221 | ||
| Systematic analysis of conservation relations in Escherichia coli genome-scale metabolic network reveals novel growth media | Q34481468 | ||
| Stimulus-dependent maximum entropy models of neural population codes. | Q34629073 | ||
| Learning and comparing functional connectomes across subjects | Q34667099 | ||
| A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. | Q34734403 | ||
| Protein signaling networks from single cell fluctuations and information theory profiling | Q34978109 | ||
| Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective | Q35013625 | ||
| Combining experiments and simulations using the maximum entropy principle | Q35105556 | ||
| Bacterial growth laws and their applications | Q35154086 | ||
| A general pairwise interaction model provides an accurate description of in vivo transcription factor binding sites | Q35187624 | ||
| Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns | Q35539757 | ||
| Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding | Q35596316 | ||
| Topology, structures, and energy landscapes of human chromosomes | Q35616170 | ||
| Comparative determination of biomass composition in differentially active metabolic States | Q35633328 | ||
| Markov processes follow from the principle of maximum caliber | Q35798350 | ||
| Statistical mechanics for natural flocks of birds | Q35882573 | ||
| Inferring Contacting Residues within and between Proteins: What Do the Probabilities Mean? | Q36015520 | ||
| The Maximum Entropy Fallacy Redux? | Q36015581 | ||
| Long-term growth data of Escherichia coli at a single-cell level. | Q36325196 | ||
| On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method | Q36688310 | ||
| Statistical mechanics of letters in words. | Q36828291 | ||
| Mapping the landscape of metabolic goals of a cell | Q36929776 | ||
| Identifying essential genes in Escherichia coli from a metabolic optimization principle | Q37085720 | ||
| Pairwise maximum entropy models for studying large biological systems: when they can work and when they can't | Q37172866 | ||
| Maximum-entropy network analysis reveals a role for tumor necrosis factor in peripheral nerve development and function | Q37282437 | ||
| Applications of the principle of maximum entropy: from physics to ecology | Q37851269 | ||
| The constructal law and the evolution of design in nature | Q37891329 | ||
| Constraint-based models predict metabolic and associated cellular functions. | Q38178893 | ||
| Combining experimental and simulation data of molecular processes via augmented Markov models. | Q38674854 | ||
| Random versus maximum entropy models of neural population activity | Q38785558 | ||
| Frontiers of high-throughput metabolomics | Q39073712 | ||
| Teaching the principles of statistical dynamics | Q39745514 | ||
| Bayesian flux balance analysis applied to a skeletal muscle metabolic model. | Q39776363 | ||
| An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) | Q21194867 | ||
| A protocol for generating a high-quality genome-scale metabolic reconstruction | Q24603373 | ||
| Robust growth of Escherichia coli | Q24611036 | ||
| Direct-coupling analysis of residue coevolution captures native contacts across many protein families | Q24627074 | ||
| Three-dimensional structures of membrane proteins from genomic sequencing | Q24630589 | ||
| The maximum entropy formalism and the idiosyncratic theory of biodiversity | Q24670539 | ||
| Weak pairwise correlations imply strongly correlated network states in a neural population | Q24680540 | ||
| Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models | Q26801539 | ||
| Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 | Q28246991 | ||
| What is flux balance analysis? | Q28275348 | ||
| Maximum entropy reconstructions of dynamic signaling networks from quantitative proteomics data | Q28476008 | ||
| A Scalable Algorithm to Explore the Gibbs Energy Landscape of Genome-Scale Metabolic Networks | Q28480577 | ||
| Shrinking the Metabolic Solution Space Using Experimental Datasets | Q28483028 | ||
| Identification of direct residue contacts in protein-protein interaction by message passing | Q29395268 | ||
| Predicting biological system objectives de novo from internal state measurements | Q30443523 | ||
| Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods | Q30489619 | ||
| Sparse low-order interaction network underlies a highly correlated and learnable neural population code | Q30500975 | ||
| Inference of structure ensembles of flexible biomolecules from sparse, averaged data | Q30697040 | ||
| Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes | Q30746334 | ||
| From plant traits to plant communities: a statistical mechanistic approach to biodiversity | Q33259505 | ||
| Statistical mechanics unifies different ecological patterns | Q33317679 | ||
| Maximum entropy and the state-variable approach to macroecology | Q33380355 | ||
| Inferring species interactions in tropical forests | Q33492048 | ||
| Social interactions dominate speed control in poising natural flocks near criticality | Q33665528 | ||
| CHRR: coordinate hit-and-run with rounding for uniform sampling of constraint-based models. | Q33735418 | ||
| Maximum entropy models for antibody diversity | Q33777827 | ||
| Maximal entropy inference of oncogenicity from phosphorylation signaling. | Q33778389 | ||
| Information-theoretic analysis of phenotype changes in early stages of carcinogenesis | Q33933428 | ||
| The fitness landscape of HIV-1 gag: advanced modeling approaches and validation of model predictions by in vitro testing | Q34015177 | ||
| Optimal population coding by noisy spiking neurons | Q34069477 | ||
| Identifying relevant positions in proteins by Critical Variable Selection | Q39922520 | ||
| Individuality and universality in the growth-division laws of single E. coli cells. | Q40002020 | ||
| Reconstructing Boolean Models of Signaling | Q41493698 | ||
| A novel methodology to estimate metabolic flux distributions in constraint-based models | Q41889906 | ||
| Modeling Stochastic Dynamics in Biochemical Systems with Feedback Using Maximum Caliber | Q41959735 | ||
| Sparse coding and high-order correlations in fine-scale cortical networks | Q41961452 | ||
| An analytic approximation of the feasible space of metabolic networks | Q42194199 | ||
| Variable habitat conditions drive species covariation in the human microbiota | Q42290355 | ||
| Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models | Q42685087 | ||
| Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models | Q42687387 | ||
| A derivation of the master equation from path entropy maximization | Q42745519 | ||
| Dynamical maximum entropy approach to flocking | Q44548478 | ||
| On the Use of Experimental Observations to Bias Simulated Ensembles | Q44810418 | ||
| Use of maximum entropy principle with Lagrange multipliers extends the feasibility of elementary mode analysis | Q46155149 | ||
| The structure of multi-neuron firing patterns in primate retina. | Q46464814 | ||
| Cell-size maintenance: universal strategy revealed | Q46540421 | ||
| Invariance of Initiation Mass and Predictability of Cell Size in Escherichia coli | Q46828403 | ||
| Interactions between species introduce spurious associations in microbiome studies. | Q47862341 | ||
| Optimization-based framework for inferring and testing hypothesized metabolic objective functions | Q47995846 | ||
| A maximum-entropy model for predicting chromatin contacts | Q48105797 | ||
| Bayesian-based selection of metabolic objective functions | Q48421839 | ||
| Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals | Q48531006 | ||
| Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes | Q49959922 | ||
| Quantifying the entropic cost of cellular growth control. | Q49961274 | ||
| Prediction of spatiotemporal patterns of neural activity from pairwise correlations. | Q50202285 | ||
| Bayesian approach to inverse statistical mechanics | Q51018995 | ||
| Maximum entropy decomposition of flux distribution at steady state to elementary modes | Q51782351 | ||
| The Synchronization of Replication and Division Cycles in Individual E. coli Cells | Q52857634 | ||
| Stochasticity of metabolism and growth at the single-cell level | Q53053028 | ||
| Maximum Entropy Approach for Deducing Amino Acid Interactions in Proteins | Q58033003 | ||
| Molecular dynamics simulations with replica-averaged structural restraints generate structural ensembles according to the maximum entropy principle | Q86365791 | ||
| Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli | Q89299585 | ||
| P433 | issue | 4 | |
| P304 | page(s) | e00596 | |
| P577 | publication date | 2018-04-13 | |
| P1433 | published in | Heliyon | Q27727019 |
| P1476 | title | An introduction to the maximum entropy approach and its application to inference problems in biology. | |
| P478 | volume | 4 |
| Q59812602 | State-of-the-art in artificial neural network applications: A survey | cites work | P2860 |
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