Browse
Submit Data
Databases
API
Help

Dataset Information

16 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Information-theoretic equilibrium and observable thermalization.

ABSTRACT: A crucial point in statistical mechanics is the definition of the notion of thermal equilibrium, which can be given as the state that maximises the von Neumann entropy, under the validity of some constraints. Arguing that such a notion can never be experimentally probed, in this paper we propose a new notion of thermal equilibrium, focused on observables rather than on the full state of the quantum system. We characterise such notion of thermal equilibrium for an arbitrary observable via the maximisation of its Shannon entropy and we bring to light the thermal properties that it heralds. The relation with Gibbs ensembles is studied and understood. We apply such a notion of equilibrium to a closed quantum system and show that there is always a class of observables which exhibits thermal equilibrium properties and we give a recipe to explicitly construct them. Eventually, an intimate connection with the Eigenstate Thermalisation Hypothesis is brought to light.

SUBMITTER: Anza F

PROVIDER: S-EPMC5339777 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Information-theoretic equilibrium and observable thermalization.

Anzà F F Vedral V V

Scientific reports 20170307

A crucial point in statistical mechanics is the definition of the notion of thermal equilibrium, which can be given as the state that maximises the von Neumann entropy, under the validity of some constraints. Arguing that such a notion can never be experimentally probed, in this paper we propose a new notion of thermal equilibrium, focused on observables rather than on the full state of the quantum system. We characterise such notion of thermal equilibrium for an arbitrary observable via the max ...[more]

PMID: 28266646

Similar Datasets

Beyond microscopic reversibility: Are observable non-equilibrium processes precisely reversible?

Project description:Although the principle of microscopic reversibility has been studied for many decades, there remain ambiguities in its application to non-equilibrium processes of importance to chemistry, physics and biology. Examples include whether protein unfolding should follow the same pathways and in the same proportions as folding, and whether unbinding should likewise mirror binding. Using continuum-space calculations which extend previous kinetic analyses, we demonstrate formally that the precise symmetry of forward and reverse processes is expected only under certain special conditions. Approximate symmetry will be exhibited under a separate set of conditions. Exact, approximate, and broken symmetry scenarios are verified in several ways: using numerical calculations on toy and molecular systems; using exact calculations on kinetic models of induced fit in protein-ligand binding; and based on reported experimental results. The analysis highlights intrinsic challenges and ambiguities in the design and analysis of both experiments and simulations.

| S-EPMC3159166 | biostudies-literature

An information theoretic characterisation of auditory encoding.

Project description:The entropy metric derived from information theory provides a means to quantify the amount of information transmitted in acoustic streams like speech or music. By systematically varying the entropy of pitch sequences, we sought brain areas where neural activity and energetic demands increase as a function of entropy. Such a relationship is predicted to occur in an efficient encoding mechanism that uses less computational resource when less information is present in the signal: we specifically tested the hypothesis that such a relationship is present in the planum temporale (PT). In two convergent functional MRI studies, we demonstrated this relationship in PT for encoding, while furthermore showing that a distributed fronto-parietal network for retrieval of acoustic information is independent of entropy. The results establish PT as an efficient neural engine that demands less computational resource to encode redundant signals than those with high information content.

| S-EPMC2039771 | biostudies-literature

Information-theoretic evaluation of predicted ontological annotations.

Project description:MotivationThe development of effective methods for the prediction of ontological annotations is an important goal in computational biology, with protein function prediction and disease gene prioritization gaining wide recognition. Although various algorithms have been proposed for these tasks, evaluating their performance is difficult owing to problems caused both by the structure of biomedical ontologies and biased or incomplete experimental annotations of genes and gene products.ResultsWe propose an information-theoretic framework to evaluate the performance of computational protein function prediction. We use a Bayesian network, structured according to the underlying ontology, to model the prior probability of a protein's function. We then define two concepts, misinformation and remaining uncertainty, that can be seen as information-theoretic analogs of precision and recall. Finally, we propose a single statistic, referred to as semantic distance, that can be used to rank classification models. We evaluate our approach by analyzing the performance of three protein function predictors of Gene Ontology terms and provide evidence that it addresses several weaknesses of currently used metrics. We believe this framework provides useful insights into the performance of protein function prediction tools.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC3694662 | biostudies-literature

Information-theoretic principles in incremental language production.

Project description:I apply a recently emerging perspective on the complexity of action selection, the rate-distortion theory of control, to provide a computational-level model of errors and difficulties in human language production, which is grounded in information theory and control theory. Language production is cast as the sequential selection of actions to achieve a communicative goal subject to a capacity constraint on cognitive control. In a series of calculations, simulations, corpus analyses, and comparisons to experimental data, I show that the model directly predicts some of the major known qualitative and quantitative phenomena in language production, including semantic interference and predictability effects in word choice; accessibility-based ("easy-first") production preferences in word order alternations; and the existence and distribution of disfluencies including filled pauses, corrections, and false starts. I connect the rate-distortion view to existing models of human language production, to probabilistic models of semantics and pragmatics, and to proposals for controlled language generation in the machine learning and reinforcement learning literature.

| S-EPMC10523564 | biostudies-literature

Out-of-equilibrium quantum magnetism and thermalization in a spin-3 many-body dipolar lattice system.

Project description:Understanding quantum thermalization through entanglement build up in isolated quantum systems addresses fundamental questions on how unitary dynamics connects to statistical physics. Spin systems made of long-range interacting atoms offer an ideal experimental platform to investigate this question. Here, we study the spin dynamics and approach towards local thermal equilibrium of a macroscopic ensemble of S = 3 chromium atoms pinned in a three dimensional optical lattice and prepared in a pure coherent spin state, under the effect of magnetic dipole-dipole interactions. Our isolated system thermalizes under its own dynamics, reaching a steady state consistent with a thermal ensemble with a temperature dictated from the system's energy. The build up of quantum correlations during the dynamics is supported by comparison with an improved numerical quantum phase-space method. Our observations are consistent with a scenario of quantum thermalization linked to the growth of entanglement entropy.

| S-EPMC6461643 | biostudies-literature

Information-theoretic metrics for visualizing gene-environment interactions.

Project description:The purpose of our work was to develop heuristics for visualizing and interpreting gene-environment interactions (GEIs) and to assess the dependence of candidate visualization metrics on biological and study-design factors. Two information-theoretic metrics, the k-way interaction information (KWII) and the total correlation information (TCI), were investigated. The effectiveness of the KWII and TCI to detect GEIs in a diverse range of simulated data sets and a Crohn disease data set was assessed. The sensitivity of the KWII and TCI spectra to biological and study-design variables was determined. Head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and the pedigree disequilibrium test (PDT) methods were obtained. The KWII and TCI spectra, which are graphical summaries of the KWII and TCI for each subset of environmental and genotype variables, were found to detect each known GEI in the simulated data sets. The patterns in the KWII and TCI spectra were informative for factors such as case-control misassignment, locus heterogeneity, allele frequencies, and linkage disequilibrium. The KWII and TCI spectra were found to have excellent sensitivity for identifying the key disease-associated genetic variations in the Crohn disease data set. In head-to-head comparisons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, the results from visual interpretation of the KWII and TCI spectra performed satisfactorily. The KWII and TCI are promising metrics for visualizing GEIs. They are capable of detecting interactions among numerous single-nucleotide polymorphisms and environmental variables for a diverse range of GEI models.

| S-EPMC2265645 | biostudies-literature

An information-theoretic derivation of min-cut-based clustering.

Project description:Min-cut clustering, based on minimizing one of two heuristic cost functions proposed by Shi and Malik nearly a decade ago, has spawned tremendous research, both analytic and algorithmic, in the graph partitioning and image segmentation communities over the last decade. It is, however, unclear if these heuristics can be derived from a more general principle, facilitating generalization to new problem settings. Motivated by an existing graph partitioning framework, we derive relationships between optimizing relevance information, as defined in the Information Bottleneck method, and the regularized cut in a K-partitioned graph. For fast-mixing graphs, we show that the cost functions introduced by Shi and Malik can be well approximated as the rate of loss of predictive information about the location of random walkers on the graph. For graphs drawn from a generative model designed to describe community structure, the optimal information-theoretic partition and the optimal min-cut partition are shown to be the same with high probability.

| S-EPMC3603148 | biostudies-literature

Sequence-based information-theoretic features for gene essentiality prediction.

Project description:Identification of essential genes is not only useful for our understanding of the minimal gene set required for cellular life but also aids the identification of novel drug targets in pathogens. In this work, we present a simple and effective gene essentiality prediction method using information-theoretic features that are derived exclusively from the gene sequences.We developed a Random Forest classifier and performed an extensive model performance evaluation among and within 15 selected bacteria. In intra-organism predictions, where training and testing sets are taken from the same organism, AUC (Area Under the Curve) scores ranging from 0.73 to 0.90, 0.84 on average, were obtained. Cross-organism predictions using 5-fold cross-validation, pairwise, leave-one-species-out, leave-one-taxon-out, and cross-taxon yielded average AUC scores of 0.88, 0.75, 0.80, 0.82, and 0.78, respectively. To further show the applicability of our method in other domains of life, we predicted the essential genes of the yeast Schizosaccharomyces pombe and obtained a similar accuracy (AUC 0.84).The proposed method enables a simple and reliable identification of essential genes without searching in databases for orthologs and demanding further experimental data such as network topology and gene-expression.

| S-EPMC5679510 | biostudies-literature

Distinguishing prognostic and predictive biomarkers: an information theoretic approach.

Project description:Motivation:The identification of biomarkers to support decision-making is central to personalized medicine, in both clinical and research scenarios. The challenge can be seen in two halves: identifying predictive markers, which guide the development/use of tailored therapies; and identifying prognostic markers, which guide other aspects of care and clinical trial planning, i.e. prognostic markers can be considered as covariates for stratification. Mistakenly assuming a biomarker to be predictive, when it is in fact largely prognostic (and vice-versa) is highly undesirable, and can result in financial, ethical and personal consequences. We present a framework for data-driven ranking of biomarkers on their prognostic/predictive strength, using a novel information theoretic method. This approach provides a natural algebra to discuss and quantify the individual predictive and prognostic strength, in a self-consistent mathematical framework. Results:Our contribution is a novel procedure, INFO+, which naturally distinguishes the prognostic versus predictive role of each biomarker and handles higher order interactions. In a comprehensive empirical evaluation INFO+ outperforms more complex methods, most notably when noise factors dominate, and biomarkers are likely to be falsely identified as predictive, when in fact they are just strongly prognostic. Furthermore, we show that our methods can be 1-3 orders of magnitude faster than competitors, making it useful for biomarker discovery in 'big data' scenarios. Finally, we apply our methods to identify predictive biomarkers on two real clinical trials, and introduce a new graphical representation that provides greater insight into the prognostic and predictive strength of each biomarker. Availability and implementation:R implementations of the suggested methods are available at https://github.com/sechidis. Supplementary information:Supplementary data are available at Bioinformatics online.

| S-EPMC6157098 | biostudies-literature

Information-theoretic analysis of multivariate single-cell signaling responses.

Project description:Mathematical methods of information theory appear to provide a useful language to describe how stimuli are encoded in activities of signaling effectors. Exploring the information-theoretic perspective, however, remains conceptually, experimentally and computationally challenging. Specifically, existing computational tools enable efficient analysis of relatively simple systems, usually with one input and output only. Moreover, their robust and readily applicable implementations are missing. Here, we propose a novel algorithm, SLEMI-statistical learning based estimation of mutual information, to analyze signaling systems with high-dimensional outputs and a large number of input values. Our approach is efficient in terms of computational time as well as sample size needed for accurate estimation. Analysis of the NF-κB single-cell signaling responses to TNF-α reveals that NF-κB signaling dynamics improves discrimination of high concentrations of TNF-α with a relatively modest impact on discrimination of low concentrations. Provided R-package allows the approach to be used by computational biologists with only elementary knowledge of information theory.

| S-EPMC6655862 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data