Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “mutual information”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
PubMed2006

Comparative analysis of transcription start sites using mutual information.

The transcription start site (TSS) region shows greater variability compared with other promoter elements. We are interested to search for its variability by using information content as a measure. We note in this study that the variability is significant in the block of 5 nucleotides (nt) surrounding the TSS region compared with the block of 15 nt. This suggests that the actual region that may be involved is in the range of 5-10 nt in size. For Escherichia coli, we note that the information content from dinucleotide substitution matrices clearly shows a better discrimination, suggesting the presence of some correlations. However, for human this effect is much less, and for mouse it is practically absent. We can conclude that the presence of short-range correlations within the TSS region is species-dependent and is not universal. We further observe that there are other variable regions in the mitochondrial control element apart from TSS. It is also noted that effective comparisons can only be made on blocks, while single nucleotide comparisons do not give us any detectable signals.

Animals↗
PubMed2005

Mutual information in protein multiple sequence alignments reveals two classes of coevolving positions.

Information theory was used to identify nonconserved coevolving positions in multiple sequence alignments from a variety of protein families. Coevolving positions in these alignments fall into two general categories. One set is composed of positions that coevolve with only one or two other positions. These positions often display direct amino acid side-chain interactions with their coevolving partner. The other set comprises positions that coevolve with many others and are frequently located in regions critical for protein function, such as active sites and surfaces involved in intermolecular interactions and recognition. We find that coevolving positions are more likely to change protein function when mutated than are positions showing little coevolution. These results imply that information theory may be applied generally to find coevolving, nonconserved positions that are part of functional sites in uncharacterized protein families. We propose that these coevolving positions compose an important subset of the positions in an alignment, and may be as important to the structure and function of the protein family as are highly conserved positions.

Algorithms↗
PubMed2003

A new approach to elastography using mutual information and finite elements.

Historically, increased mechanical stiffness during tissue palpation exams has been associated with assessing organ health as well as with detecting the growth of a potentially life-threatening cell mass. As such, techniques to image elasticity parameters (i.e., elastography) have recently become of great interest to scientists. In this work, a new method of elastography will be introduced within the context of mammographic imaging. The elastography method proposed represents a non-rigid iterative image registration algorithm that varies material properties within a finite element model to improve registration. More specifically, regional measures of image similarity are used within an objective function minimization framework to reconstruct elasticity images of tissue stiffness. Numerical simulations illustrate: (1) the encoding of stiffness information within the context of a regional image similarity criterion, (2) the methodology for an iterative elastographic imaging framework and (3) elasticity reconstruction simulations. The real strength in this approach is that images from any modality (e.g., magnetic resonance, computed tomography, ultrasound. etc) that have sufficient anatomically-based intensity heterogeneity and remain consistent from a pre- to a post-deformed state could be used in this paradigm.

Adipose Tissue↗
PubMed2001

Mutual information in a dilute, asymmetric neural network model.

Neural networks with asymmetric synaptic connections (w(ij) not equal to w(ji)) display a broad range of dynamical behavior including fixed point, periodic, and "chaotic" trajectories. Previous work has shown that such networks undergo an order-chaos phase transition as various network parameters, such as the connectivity or the degree of asymmetry, are changed. Here, using an information theoretic approach, we present results which suggest that neurons are able to communicate information to each other most effectively in networks that are near the order-chaos transition. We then extend the model to incorporate some biologically relevant features.

Animals↗
PubMed2003

Spike-timing-dependent plasticity and relevant mutual information maximization.

Synaptic plasticity was recently shown to depend on the relative timing of the pre- and postsynaptic spikes. This article analytically derives a spike-dependent learning rule based on the principle of information maximization for a single neuron with spiking inputs. This rule is then transformed into a biologically feasible rule, which is compared to the experimentally observed plasticity. This comparison reveals that the biological rule increases information to a near-optimal level and provides insights into the structure of biological plasticity. It shows that the time dependency of synaptic potentiation should be determined by the synaptic transfer function and membrane leak. Potentiation consists of weight-dependent and weight-independent components whose weights are of the same order of magnitude. It further suggests that synaptic depression should be triggered by rare and relevant inputs but at the same time serves to unlearn the baseline statistics of the network's inputs. The optimal depression curve is uniformly extended in time, but biological constraints that cause the cell to forget past events may lead to a different shape, which is not specified by our current model. The structure of the optimal rule thus suggests a computational account for several temporal characteristics of the biological spike-timing-dependent rules.

Action Potentials↗
PubMedDate not supplied

Estimating the mutual information of an EEG-based Brain-Computer Interface.

An EEG-based Brain-Computer Interface (BCI) could be used as an additional communication channel between human thoughts and the environment. The efficacy of such a BCI depends mainly on the transmitted information rate. Shannon's communication theory was used to quantify the information rate of BCI data. For this purpose, experimental EEG data from four BCI experiments was analyzed off-line. Subjects imaginated left and right hand movements during EEG recording from the sensorimotor area. Adaptive autoregressive (AAR) parameters were used as features of single trial EEG and classified with linear discriminant analysis. The intra-trial variation as well as the inter-trial variability, the signal-to-noise ratio, the entropy of information, and the information rate were estimated. The entropy difference was used as a measure of the separability of two classes of EEG patterns.

Attention↗
PubMed2005

Mutual information content of homologous DNA sequences.

The necessary information to reproduce and keep an organism is codified in acid nucleic molecules. Deepening the knowledge about how the information is stored in these bio-sequences can lead to more efficient methods of comparing genomic sequences. In the present study, we analyzed the quantity of information contained in a DNA sequence that can be useful to identify sequences homologous to it. To reach it, we used signal processing techniques, specially spectral analysis and information theory.

Base Sequence↗