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Results for “Network inference”

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At least 415 records · Page 23Linked to original sources

PubMed2006

Inferring protein interactions from experimental data by association probabilistic method.

To elucidate protein interaction networks is one of the major goals of functional genomics for whole organisms. So far, various computational methods have been proposed for inference of protein-protein interactions. Based on the association method by Sprinzak et al., we propose an association probabilistic method in this short communication to infer protein interactions directly from the experimental data, which outperformed other existing methods in terms of both accuracy and efficiency despite its simple form. Specifically, we show that the association probabilistic method achieves the highest accuracy among the existing approaches for the measures of root-mean-square error and the Pearson correlation coefficient, and also runs much faster than the LP-based method, by experimental dataset in Yeast. Software is available from the authors upon request.

Databases, Protein↗

PubMed1998

Extended confocal microscopy of myocardial laminae and collagen network.

Ventricular myocardium has a complex three-dimensional structure which has previously been inferred from two-dimensional images. We describe a technique for imaging the 3D organization of myocytes in conjunction with the collagen network in extended blocks of myocardium. Rat hearts were fixed with Bouin's solution and perfusion-stained with picrosirius red. Transmural blocks from the left ventricular free wall were embedded in Agar 100 resin and mounted securely in an ultramicrotome chuck. Confocal fluorescence laser scanning microscopy was used to obtain 3D images to a depth of 60 microns in a contiguous mosaic across the surface. Approximately 50 microns was then cut off the surface of the block with an ultramicrotome. This sequence was repeated 20 times. Images were assembled and registered in 3D to form an extended volume 3800 x 800 x 800 microns 3 spanning the heart wall from epicardium to endocardium. Examples are given of how digital reslicing and volume rendering methods can be applied to the resulting dataset to provide quantitative structural information about the 3D organization of myocytes, extracellular collagen matrix and blood vessel network of the heart.

Animals↗

PubMed2005

A systems biology perspective on protein structural dynamics and signal transduction.

The functional dynamics of signal transduction through protein interaction networks are determined both by network topology and by the signal processing properties of component proteins. In order to understand the emergent properties of signal transduction networks in terms of information processing, storage and decision making, we not only need to map the so-called 'interactome' but, perhaps more importantly, we also have to understand how the structural dynamics of constituent proteins shape non-linear responses through cooperativity and allostery. Several in silico methods have been developed to identify networks of cooperative residues in proteins and help infer their mode of action. Applying this type of analysis to important classes of modular signal transduction domains should, in principle, allow the function of these proteins to be abstracted in terms of their information processing characteristics, permitting better comprehension of the systemic properties of biological networks.

Binding Sites↗

PubMed2004

Coupling of functional gene diversity and geochemical data from environmental samples.

Genomic techniques commonly used for assessing distributions of microorganisms in the environment often produce small sample sizes. We investigated artificial neural networks for analyzing the distributions of nitrite reductase genes (nirS and nirK) and two sets of dissimilatory sulfite reductase genes (dsrAB1 and dsrAB2) in small sample sets. Data reduction (to reduce the number of input parameters), cross-validation (to measure the generalization error), weight decay (to adjust model parameters to reduce generalization error), and importance analysis (to determine which variables had the most influence) were useful in developing and interpreting neural network models that could be used to infer relationships between geochemistry and gene distributions. A robust relationship was observed between geochemistry and the frequencies of genes that were not closely related to known dissimilatory sulfite reductase genes (dsrAB2). Uranium and sulfate appeared to be the most related to distribution of two groups of these unusual dsrAB-related genes. For the other three groups, the distributions appeared to be related to pH, nickel, nonpurgeable organic carbon, and total organic carbon. The models relating the geochemical parameters to the distributions of the nirS, nirK, and dsrAB1 genes did not generalize as well as the models for dsrAB2. The data also illustrate the danger (generating a model that has a high generalization error) of not using a validation approach in evaluating the meaningfulness of the fit of linear or nonlinear models to such small sample sizes.

Bacteria↗

PubMed1998

Network analyses of Y-chromosomal types in Europe, northern Africa, and western Asia reveal specific patterns of geographic distribution.

In a study of 908 males from Europe, northern Africa, and western Asia, the variation of four Y-linked dinucleotide microsatellites was analyzed within three "frames" that are defined by mutations that are nonrecurrent, or nearly so. The rapid generation and extinction of new dinucleotide length variants causes the haplotypes within each lineage to diverge from one another. We constructed networks of "adjacent" haplotypes within each frame, by assuming changes of a single dinucleotide unit. Two small and six large networks were obtained, the latter including 94.9% of the sampled Y chromosomes. We show that the phenetic relationships among haplotypes, represented as a network, result largely from common descent and subsequent molecular radiation. The grouping of haplotypes of the same network thus fits an evolutionarily relevant criterion. Notably, this method allows the total diversity within a sample to be partitioned. Networks can be considered optimal markers for population studies, because reliable frequency estimates can be obtained in small samples. We present synthetic maps describing the incidence of different Y-chromosomal lineages in the extant human populations of the surveyed areas. Dinucleotide diversity also was used to infer time intervals for the coalescence of each network.

Africa, Northern↗

PubMed2001

Neural networks and logical reasoning systems: a translation table.

A correspondence is established between the basic elements of logic reasoning systems (knowledge bases, rules, inference and queries) and the structure and dynamical evolution laws of neural networks. The correspondence is pictured as a translation dictionary which might allow to go back and forth between symbolic and network formulations, a desirable step in learning-oriented systems and multicomputer networks. In the framework of Horn clause logics, it is found that atomic propositions with n arguments correspond to nodes with nth order synapses, rules to synaptic intensity constraints, forward chaining to synaptic dynamics and queries either to simple node activation or to a query tensor dynamics.

Logic↗

PubMed1992

Quality control in nerve conduction studies with coupled knowledge-based system approach.

Contemporary equipment used for nerve conduction studies is usually capable of computerized measurement of latency, amplitude, duration, and area of nerve and muscle action potentials and resulting conduction velocities. Abnormalities can be due to technical error or disease. Identification of technical error is a major element of quality control in electromyography, and artificial intelligence could be useful for this purpose. We have developed a coupled knowledge-based prototype system (QUALICON) to assess the correctness of recording and stimulating characteristics in routine conduction studies. QUALICON extracts numeric features from CMAPs or SNAPs, which are translated into symbolic form to drive a Bayesian network. The network uses high-level knowledge to infer the quality of stimulating and recording electrode placement as well as polarity and stimulus strength making recommendations as to the likely technical error when abnormal potentials are detected. A preliminary assessment shows that QUALICON performs as well as manual assessment performed by professionals.

Action Potentials↗

PubMed2006

Regulation of charged groups and laminin patterns for selective neuronal adhesion.

Primary neuronal cultures on substrates patterned with extracellular matrix proteins such as laminin have yielded much information regarding the physiological characteristics of neuronal cells in vitro. Surface charge also influences neuronal adherence, and a positive charge can have stimulatory effects. The attraction between laminin patterns and polycation films are of interest in the study of neuronal adhesion. We cultured primary hippocampal neurons on poly(ethylenimine) (PEI) films with laminin grids and evaluated their viability and morphology by means of fluorescent microscopy after 5-7 days. The results showed that the neurons did not form networks on the laminin grids. It is inferred that the PEI films were more favourable for neuronal adhesion than the laminin grid.

Animals↗

PubMed2005

The evaluation of evidence in the forensic investigation of fire incidents (Part I): an approach using Bayesian networks.

The forensic investigation of the origin and cause of a fire incident is a particularly demanding area of expertise. As the available evidence is often incomplete or vague, uncertainty is a key element. The present study is an attempt to approach this through the use of Bayesian networks, which have been found useful in assisting human reasoning in a variety of disciplines in which uncertainty plays a central role. The present paper describes the construction of a Bayesian network (BN) and its use for drawing inferences about propositions of interest, based upon a single, possibly non replicable item of evidence: detected residual quantities of a flammable liquid in fire debris.

Journal Article↗

PubMed2004

Mathematical and computational techniques to deduce complex biochemical reaction mechanisms.

Time series data can now be routinely collected for biochemical reaction pathways, and recently, several methods have been proposed to infer reaction mechanisms for metabolic pathways and networks. In this paper we provide a survey of mathematical techniques for determining reaction mechanisms for time series data on the concentration or abundance of different reacting components, with little prior information about the pathways involved.

Algorithms↗

PubMed2006

Molecular phylogeny of musk deer: a genomic view with mitochondrial 16S rRNA and cytochrome b gene.

The phylogenetic status of the infra order Pecora is controversial, even though it is supported by paleontological, morphological, and molecular evidence. We analyzed two mitochondrial genes (i.e., 16S rRNA and cytochrome b) to resolve the phylogenetic position of pecoran species, i.e., the Bovidae, Cervidae, and Moschidae endemic to the Indian subcontinent. We used phylogenetic analysis based on different algorithms, including neighbor joining, maximum parsimony, Bayesian inference, maximum likelihood, minimum evolution, median joining network, along with multidimensional scaling, and DNA word analysis. Our results established the basal position of Tragulidae and the monophyly of the infra order Pecora within the Suborder Ruminantia. Our results also demonstrated that Bovidae, Cervidae, and Moschidae are allied with the placement of musk deer as more closely related to bovids than to cervids. Molecular dating based on sequence analysis shows that the radiation of Pecora occurred during the early Oligocene and that the majority of the pecoran families radiated and dispersed rapidly during the Oligocene/Miocene transition.

Animals↗

PubMed2002

Evaluation of scientific evidence using Bayesian networks.

Bayesian networks provide a valuable aid for representing epistemic relationships in a body of uncertain evidence. The paper proposes some simple Bayesian networks for standard analysis of patterns of inference concerning scientific evidence, with a discussion of the rationale behind the nets, the corresponding probabilistic formulas, and the required probability assessments.

Adult↗

PubMed2003

An interference effect of observed biological movement on action.

It has been proposed that actions are intrinsically linked to perception and that imagining, observing, preparing, or in any way representing an action excites the motor programs used to execute that same action. There is neurophysiological evidence that certain brain regions involved in executing actions are activated by the mere observation of action (the so-called "mirror system;" ). However, it is unknown whether this mirror system causes interference between observed and simultaneously executed movements. In this study we test the hypothesis that, because of the overlap between action observation and execution, observed actions should interfere with incongruous executed actions. Subjects made arm movements while observing either a robot or another human making the same or qualitatively different arm movements. Variance in the executed movement was measured as an index of interference to the movement. The results demonstrate that observing another human making incongruent movements has a significant interference effect on executed movements. However, we found no evidence that this interference effect occurred when subjects observed a robotic arm making incongruent movements. These results suggest that the simultaneous activation of the overlapping neural networks that process movement observation and execution infers a measurable cost to motor control.

Adult↗

PubMed2005

Inferring gene transcriptional modulatory relations: a genetical genomics approach.

Bayesian network modeling is a promising approach to define and evaluate gene expression circuits in diverse tissues and cell types under different experimental conditions. The power and practicality of this approach can be improved by restricting the number of potential interactions among genes and by defining causal relations before evaluating posterior probabilities for billions of networks. A newly developed genetical genomics method that combines transcriptome profiling with complex trait analysis now provides strong constraints on network architecture. This method detects those chromosomal intervals responsible for differences in mRNA expression using quantitative trait locus (QTL) mapping. We have developed an efficient Bayesian approach that exploits the genetical genomics method to focus computational effort on the most plausible gene modulatory networks. We exploit a dense marker map for a genetic reference population (GRP) that consists of 32 BXD strains of mice made by intercrossing two progenitor strains--C57BL/6J and DBA/2J. These progenitors differ at approximately 1.3 million known single nucleotide polymorphisms (SNPs), all of which can be exploited to estimate the probability that a gene contains functional polymorphisms that segregate within the GRP. We constructed 66 candidate networks that include all the candidate modulator genes located in the 209 statistically significant trans-acting QTL regions. SNPs that distinguish between the two progenitor strains were used to further winnow the list of candidate modulators. Bayesian network was then used to identify the genetic modulatory relations that best explain the microarray data.

Algorithms↗

PubMedDate not supplied

[Vaccination messages foster mobilization and high coverage in Mexico].

OBJECTIVE: To understand the perceptions of the effect of vaccination messages aired during the Second National Health Week in 1996 on the predisposition to vaccinate children among mothers from a lower class neighbourhood in Mexico City. METHODS: 120 mothers of children between the ages of 0 and 7 years who were exposed to the campaign messages participated. They were divided into 8 focus groups based on the age of the children and level of schooling attained by the mothers. A content analysis was conducted using open-ended coding and categorization based on shared concepts. RESULTS: The mothers had a positive image of vaccines for the health of their children. They perceived that the messages reminded them that they needed to vaccinate their children and contributed to the mobilization of their social network in support of vaccination. The authors inferred of mother 5 narratives that some components of messages generated inaccurate interpretations and knowledge and reinforced negative attitudes and cultural and organizational barriers to vaccination in some mothers. CONCLUSIONS: The messages prompted the use of vaccination services during the health campaign. In the production, the form and content should be modified to overcome misunderstandings and barriers to vaccination.

Female↗

PubMed2006

Inferring phylogenetic relationships avoiding forbidden rooted triplets.

To construct a phylogenetic tree or phylogenetic network for describing the evolutionary history of a set of species is a well-studied problem in computational biology. One previously proposed method to infer a phylogenetic tree/network for a large set of species is by merging a collection of known smaller phylogenetic trees on overlapping sets of species so that no (or as little as possible) branching information is lost. However, little work has been done so far on inferring a phylogenetic tree/network from a specified set of trees when in addition, certain evolutionary relationships among the species are known to be highly unlikely. In this paper, we consider the problem of constructing a phylogenetic tree/network which is consistent with all of the rooted triplets in a given set C and none of the rooted triplets in another given set F. Although NP-hard in the general case, we provide some efficient exact and approximation algorithms for a number of biologically meaningful variants of the problem.

Algorithms↗

PubMed2026

MetaCCI: meta cell-cell interaction inference and its application to CCIs characteristics of MDS.

MOTIVATION: Cell-cell interactions (CCIs) are fundamental to multicellular organisms and play crucial roles in diverse biological processes and disease mechanisms. Understanding CCIs is vital for deciphering disease pathogenesis and developing therapeutic strategies. Although numerous computational methods have been developed to infer CCIs from complex biological data, most existing approaches rely primarily on single-gene expression levels and ligand-receptor databases, often failing to capture the nuanced network-wide changes characteristic of disease states. RESULT: We propose MetaCCI, a novel computational strategy that integrates meta-information into CCI inference by extending the traditional gene expression-based analysis to a gene regulatory network framework. MetaCCI meticulously combines established ligand-receptor pairs with quantitative insights into gene behavior within complex gene networks, enabling the precise extraction of relevant targets for CCI inference. Subsequently, CCI inference was performed using an eigen cell co-expression network, providing a more holistic view of cell-cell communication. Monte Carlo simulations demonstrated that MetaCCI consistently outperforms existing methods in CCI inference. We applied MetaCCI to characterize cell-cell communication in Myelodysplastic Syndromes (MDS). Our results identified distinct interaction patterns in MDS compared with normal cell populations, specifically highlighting the loss of CCIs between "Dendritic cells and Hematopoietic precursor cells" and between "Dendritic cells and Hematopoietic multipotent progenitor cells" as characteristic features of MDS. Furthermore, FABP5, CD63, and HMGB1 were identified as MDS-specific markers. These findings suggest that diminished CCIs involving dendritic cells, hematopoietic precursor cells, and multipotent progenitor cells are pivotal to MDS pathogenesis. AVAILABILITY AND IMPLEMENTATION: The MetaCCI software is freely available at https://github.com/HeewonGitHub/MetaCCI. An archived version of the software and example datasets used in this study is available at Zenodo: https://doi.org/10.5281/zenodo.20101527.

Myelodysplastic Syndromes↗

PubMedDate not supplied

Neural network applications in physical medicine and rehabilitation.

The purpose of this article is to provide an overview of neural networks and their applications in physical medicine and rehabilitation. Conventional statistical models may present certain limitations that can be overcome by neural networks. We show what neural networks are, how they "learn" regularities from the data, and how they can classify previously unseen cases. We present advantages and disadvantages of using neural networks and compare them with regression models. We explain how neural networks can be used as statistical tools for making inferences using the example of a prognostic model that predicts ambulation after spinal cord injury.

Humans↗