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Results for “Network inference”

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At least 271 records · Page 15Linked to original sources

PubMed1998

Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter.

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS, by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm uses two adaptive network-based fuzzy inference systems to compose a generalized fuzzy rule-based stationary-nonstationary filter (GFST-NST). The training procedure of the fuzzy inference systems involves the outputs of the wavelet transform-based stationary-nonstationary filter (WTST-NST), proposed by Hadjileontiadis and Panas [1]. The basic idea of the GFST-NST was initially proposed by the authors with the introduction of the fuzzy rule-based stationary-nonstationary filter (FST-NST) [2], tested with the separation of crackles from VS. The main contribution of this paper is the modification of the structure of the FST-NST filter to a serial-type fuzzy filter that, unlike the parallel operation of the FST-NST filter, sends a predicted stationary signal (VS) into the predictor of the nonstationary (DAS). Applying the GFST-NST filter to fine-coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. The separation performance of the GFST-NST filter was evaluated through quantitative and qualitative indexes that proved its efficiency and superiority against the FST-NST filter. When compared to the WTST-NST filter, the GFST-NST filter performed similarly in accuracy and objectiveness, but in a faster way. Thus, the GFST-NST filter combines the separation accuracy of the WTST-NST filter with the real-time implementation of the FST-NST filter, so it can easily be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

Auscultation↗

PubMed2000

Discovery of regulatory interactions through perturbation: inference and experimental design.

We present two methods to be used interactively to infer a genetic network from gene expression measurements. The predictor method determines the set of Boolean networks consistent with an observed set of steady-state gene expression profiles, each generated from a different perturbation to the genetic network. The chooser method uses an entropy-based approach to propose an additional perturbation experiment to discriminate among the set of hypothetical networks determined by the predictor. These methods may be used iteratively and interactively to successively refine the genetic network: at each iteration, the perturbation selected by the chooser is experimentally performed to generate a new gene expression profile, and the predictor is used to derive a refined set of hypothetical gene networks using the cumulative expression data. Performance of the predictor and chooser is evaluated on simulated networks with varying number of genes and number of interactions per gene.

Algorithms↗

PubMed1999

Loss of IA expression and increased excitability in postnatal rat Cajal-Retzius cells.

Although an important secretory function of Cajal-Retzius (CR) cells has been discovered recently, the precise electrical status of these cells among other layer I neurons in particular and in cortical function in general is still unclear. In this paper, early postnatal CR cells from rat neocortex were found to express an inactivating K current whose molecular substrate is likely to be the Kv1.4 channel. Both electrophysiological and immunocytochemical experiments revealed that expression of this A-type current is down-regulated in vivo and virtually disappears by the end of the second postnatal week. At this time, CR cells have become capable of evoked repetitive firing, and their action potentials are larger and faster, yet these electrical properties still appear incompatible with a role in cortical network function, as inferred from comparisons with other cortical neurons. Also at this time, a large proportion of CR cells display spontaneous spiking activity, which suggests the possibility of additional roles for these cells. We conclude that the loss of A channels along with an increase in Na channel density shape the changes in excitability of postnatal CR cells, in terms of both the patterns of evoked firing and the emergence of spontaneous spiking.

Action Potentials↗

PubMed2025

Coarse-grained model of serial dilution dynamics in synthetic human gut microbiome.

Many microbial communities in nature are complex, with hundreds of coexisting strains and the resources they consume. We currently lack the ability to assemble and manipulate such communities in a predictable manner in the lab. Here, we take a first step in this direction by introducing and studying a simplified consumer resource model of such complex communities in serial dilution experiments. The main assumption of our model is that during the growth phase of the cycle, strains share resources and produce metabolic byproducts in proportion to their average abundances and strain-specific consumption/production fluxes. We fit the model to describe serial dilution experiments in hCom2, a defined synthetic human gut microbiome with a steady-state diversity of 63 species growing on a rich media, using consumption and production fluxes inferred from metabolomics experiments. The model predicts serial dilution dynamics reasonably well, with a correlation coefficient between predicted and observed strain abundances as high as 0.8. We applied our model to: (i) calculate steady-state abundances of leave-one-out communities and use these results to infer the interaction network between strains; (ii) explore direct and indirect interactions between strains and resources by increasing concentrations of individual resources and monitoring changes in strain abundances; (iii) construct a resource supplementation protocol to maximally equalize steady-state strain abundances.

Gastrointestinal Microbiome↗

PubMed2003

Ultrasonic tissue characterization for prostate diagnostics: spectral parameters vs. texture parameters.

An ultrasonic multi-feature tissue characterizing system for the detection of prostate cancer is presented. The system is based on the processing of radio frequency (RF) ultrasonic echo data. Data from 100 patients was acquired in a clinical study. Parameters are extracted from the RF echo data and classified using two adaptive network-based fuzzy inference systems (FIS) working in parallel as a nonlinear classifier. Next to spectral parameters, conventional texture parameters are calculated using demodulated and log-compressed echo data. In the first approach, the classifier is trained on both, spectral and texture parameters. In the second approach, the classifier is only trained on texture parameters. Classification results of both approaches are compared and it is demonstrated, that only the use of spectral parameters yields satisfying classification results. Results of a minimum distance classifier (MDC) are presented for comparison with the fuzzy inference system. For the final fuzzy inference systems used in this approach, the area under the ROC curve is between 84% and 86% for the combined approach and between 70% and 74% for the approach based on texture parameters only.

Aged↗

PubMed2001

A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process.

For control and automation of biological treatment processes, lack of reliable on-line sensors to measure water quality parameters is one of the most important problems to overcome. Many parameters cannot be measured directly with on-line sensors. The accuracy of existing hardware sensors is also not sufficient and maintenance problems such as electrode fouling often cause trouble. This paper deals with the development of software sensor techniques that estimate the target water quality parameter from other parameters using the correlation between water quality parameters. We focus our attention on the preprocessing of noisy data and the selection of the best model feasible to the situation. Problems of existing approaches are also discussed. We propose a hybrid neural network as a software sensor inferring wastewater quality parameter. Multivariate regression, artificial neural networks (ANN), and a hybrid technique that combines principal component analysis as a preprocessing stage are applied to data from industrial wastewater processes. The hybrid ANN technique shows an enhancement of prediction capability and reduces the overfitting problem of neural networks. The result shows that the hybrid ANN technique can be used to extract information from noisy data and to describe the nonlinearity of complex wastewater treatment processes.

Automation↗

PubMed1997

Understanding gene and allele function with two-hybrid methods.

Two-hybrid schemes for detecting protein-protein interactions have deepened our understanding of biology by allowing scientists to identify individual important proteins. Recent developments will allow biologists to chart regulatory networks and to rapidly generate hypotheses for the function of genes, allelic variants, and the connections between proteins that make up these networks. Future developments will allow biologists to test inferences about the function of network elements, and allow global approaches to questions of biological function.

Alleles↗

PubMed2000

Linear modeling of genetic networks from experimental data.

In this paper, the regulatory interactions between genes are modeled by a linear genetic network that is estimated from gene expression data. The inference of such a genetic network is hampered by the dimensionality problem. This problem is inherent in all gene expression data since the number of genes by far exceeds the number of measured time points. Consequently, there are infinitely many solutions that fit the data set perfectly. In this paper, this problem is tackled by combining genes with similar expression profiles in a single prototypical 'gene'. Instead of modeling the genes individually, the relations between prototypical genes are modeled. In this way, genes that cannot be distinguished based on their expression profiles are grouped together and their common control action is modeled instead. This process reduces the number of signals and imposes a structure on the model that is supported by the fact that biological genetic networks are thought to be redundant and sparsely connected. In essence, the ambiguity in model solutions is represented explicitly by providing a generalized model that expresses the basic regulatory interactions between groups of similarly expressed genes. The modeling approach is illustrated on artificial as well as real data.

Animals↗

PubMed2003

Assessing experimentally derived interactions in a small world.

Experimentally determined networks are susceptible to errors, yet important inferences can still be drawn from them. Many real networks have also been shown to have the small-world network properties of cohesive neighborhoods and short average distances between vertices. Although much analysis has been done on small-world networks, small-world properties have not previously been used to improve our understanding of individual edges in experimentally derived graphs. Here we focus on a small-world network derived from high-throughput (and error-prone) protein-protein interaction experiments. We exploit the neighborhood cohesiveness property of small-world networks to assess confidence for individual protein-protein interactions. By ascertaining how well each protein-protein interaction (edge) fits the pattern of a small-world network, we stratify even those edges with identical experimental evidence. This result promises to improve the quality of inference from protein-protein interaction networks in particular and small-world networks in general.

Cluster Analysis↗

PubMed2006

Chance and necessity in the evolution of minimal metabolic networks.

It is possible to infer aspects of an organism's lifestyle from its gene content. Can the reverse also be done? Here we consider this issue by modelling evolution of the reduced genomes of endosymbiotic bacteria. The diversity of gene content in these bacteria may reflect both variation in selective forces and contingency-dependent loss of alternative pathways. Using an in silico representation of the metabolic network of Escherichia coli, we examine the role of contingency by repeatedly simulating the successive loss of genes while controlling for the environment. The minimal networks that result are variable in both gene content and number. Partially different metabolisms can thus evolve owing to contingency alone. The simulation outcomes do preserve a core metabolism, however, which is over-represented in strict intracellular bacteria. Moreover, differences between minimal networks based on lifestyle are predictable: by simulating their respective environmental conditions, we can model evolution of the gene content in Buchnera aphidicola and Wigglesworthia glossinidia with over 80% accuracy. We conclude that, at least for the particular cases considered here, gene content of an organism can be predicted with knowledge of its distant ancestors and its current lifestyle.

Biological Evolution↗

PubMedDate not supplied

Photoinitiated cross-linking of the biodegradable polyester poly(propylene fumarate). Part I. Determination of network structure.

In this work, we investigated the mechanism involved in the photoinitiated cross-linking of the polyester poly(propylene fumarate) (PPF) using the initiator bis(2,4,6-trimethylbenzoyl) phenylphosphine oxide (BAPO). It was hypothesized that BAPO has the ability to cross-link PPF into solid polymer networks, without the use of a cross-linking monomer, because two pairs of radicals, both involving a fast adding phosphinoyl radical, were formed upon UV irradiation of BAPO. Spectroscopic investigation first confirmed the addition of BAPO derived radicals to the PPF olefin. Investigations of fumarate conversion and bulk network properties were then undertaken, using the BAPO initiator and a monoacylphosphine oxide (MAPO) initiator which contains a single photolabile bond. Results show that a single BAPO phosphinoyl radical was primarily responsible for the formation of a highly cross-linked PPF network and the additional radical pair which may be formed does not dramatically alter fumarate conversion or bulk network properties. From these results, the network structure of BAPO initiated, photo-cross-linked PPF may be deduced. Finally, this study demonstrates a method for inferring cross-linked network structures by contrasting properties of bulk materials formed from similar cross-linking initiators.

Biocompatible Materials↗

PubMed2000

Nature of altered pulsatile hormone release and neuroendocrine network signalling in human ageing: clinical studies of the somatotropic, gonadotropic, corticotropic and insulin axes.

Recent clinical investigations have implemented an array of new analytical tools to evaluate the neuroregulation of endocrine axes. These studies demonstrate multifold disruption within the growth hormone (GH), luteinizing hormone (LH)-testosterone, adrenocorticotropin (ACTH)-cortisol and the insulin axes in healthy ageing men and women. Novel research strategies in ageing include such developments as the indirect in vivo assessment of neuroendocrine network integration, via the approximate entropy (ApEn) statistic to monitor the unihormonal orderliness and bihormonal synchronicity of hormone release, and thus infer stability of network-integrative processes. For example, ApEn calculations show that the individual orderliness of GH, insulin or LH release falls progressively in older men and women, and the conditional synchrony between LH and testosterone (or LH and follicle-stimulating hormone/prolactin) release, and LH secretion and the neurogenically organized signal, nocturnal penile tumescence (NPT), all decline markedly in older men. Evaluation of the ACTH-cortisol axis points additionally to disrupted bihormonal synchrony within this stress-responsive system in healthy ageing. A complementary investigative tool, viz. a stochastic differential equation random-effects feedback construct of the interactive male gonadotropin-releasing hormone-LH-testosterone axis, predicts that only certain extant postulates of ageing in the male reproductive axis will give rise to the observed erosion of LH-testosterone synchrony. Collectively, available clinical data suggest a general model of early neuroendocrine ageing in the human, in both the male and female, wherein ageing is marked by variable disruption in the time-delayed feedback and feedforward interconnections among neuroendocrine glands, which constitute an integrated axis and which control the joint synchrony of hormone release.

Adrenocorticotropic Hormone↗

PubMed2005

Neuronal correlates of theory of mind and empathy: a functional magnetic resonance imaging study in a nonverbal task.

Theory of Mind (ToM), the ability to attribute mental states to others, and empathy, the ability to infer emotional experiences, are important processes in social cognition. Brain imaging studies in healthy subjects have described a brain system involving medial prefrontal cortex, superior temporal sulcus and temporal pole in ToM processing. Studies investigating networks associated with empathic responding also suggest involvement of temporal and frontal lobe regions. In this fMRI study, we used a cartoon task derived from Sarfati et al. (1997) [Sarfati, Y., Hardy-Bayle, M.C., Besche, C., Widlocher, D. 1997. Attribution of intentions to others in people with schizophrenia: a non-verbal exploration with comic strips. Schizophrenia Research 25, 199-209.]with both ToM and empathy stimuli in order to allow comparison of brain activations in these two processes. Results of 13 right-handed, healthy, male volunteers were included. Functional images were acquired using a 1.5 T Phillips Gyroscan. Our results confirmed that ToM and empathy stimuli are associated with overlapping but distinct neuronal networks. Common areas of activation included the medial prefrontal cortex, temporoparietal junction and temporal poles. Compared to the empathy condition, ToM stimuli revealed increased activations in lateral orbitofrontal cortex, middle frontal gyrus, cuneus and superior temporal gyrus. Empathy, on the other hand, was associated with enhanced activations of paracingulate, anterior and posterior cingulate and amygdala. We therefore suggest that ToM and empathy both rely on networks associated with making inferences about mental states of others. However, empathic responding requires the additional recruitment of networks involved in emotional processing. These results have implications for our understanding of disorders characterized by impairments of social cognition, such as autism and psychopathy.

Adult↗

PubMed2003

Global atmospheric black carbon inferred from AERONET.

AERONET, a network of well calibrated sunphotometers, provides data on aerosol optical depth and absorption optical depth at >250 sites around the world. The spectral range of AERONET allows discrimination between constituents that absorb most strongly in the UV region, such as soil dust and organic carbon, and the more ubiquitously absorbing black carbon (BC). AERONET locations, primarily continental, are not representative of the global mean, but they can be used to calibrate global aerosol climatologies produced by tracer transport models. We find that the amount of BC in current climatologies must be increased by a factor of 2-4 to yield best agreement with AERONET, in the approximation in which BC is externally mixed with other aerosols. The inferred climate forcing by BC, regardless of whether it is internally or externally mixed, is approximately 1 W/m2, most of which is probably anthropogenic. This positive forcing (warming) by BC must substantially counterbalance cooling by anthropogenic reflective aerosols. Thus, especially if reflective aerosols such as sulfates are reduced, it is important to reduce BC to minimize global warming.

Journal Article↗

PubMed1998

Codes for sound-source location in nontonotopic auditory cortex.

We evaluated two hypothetical codes for sound-source location in the auditory cortex. The topographical code assumed that single neurons are selective for particular locations and that sound-source locations are coded by the cortical location of small populations of maximally activated neurons. The distributed code assumed that the responses of individual neurons can carry information about locations throughout 360 degrees of azimuth and that accurate sound localization derives from information that is distributed across large populations of such panoramic neurons. We recorded from single units in the anterior ectosylvian sulcus area (area AES) and in area A2 of alpha-chloralose-anesthetized cats. Results obtained in the two areas were essentially equivalent. Noise bursts were presented from loudspeakers spaced in 20 degrees intervals of azimuth throughout 360 degrees of the horizontal plane. Spike counts of the majority of units were modulated >50% by changes in sound-source azimuth. Nevertheless, sound-source locations that produced greater than half-maximal spike counts often spanned >180 degrees of azimuth. The spatial selectivity of units tended to broaden and, often, to shift in azimuth as sound pressure levels (SPLs) were increased to a moderate level. We sometimes saw systematic changes in spatial tuning along segments of electrode tracks as long as 1.5 mm but such progressions were not evident at higher sound levels. Moderate-level sounds presented anywhere in the contralateral hemifield produced greater than half-maximal activation of nearly all units. These results are not consistent with the hypothesis of a topographic code. We used an artificial-neural-network algorithm to recognize spike patterns and, thereby, infer the locations of sound sources. Network input consisted of spike density functions formed by averages of responses to eight stimulus repetitions. Information carried in the responses of single units permitted reasonable estimates of sound-source locations throughout 360 degrees of azimuth. The most accurate units exhibited median errors in localization of <25 degrees, meaning that the network output fell within 25 degrees of the correct location on half of the trials. Spike patterns tended to vary with stimulus SPL, but level-invariant features of patterns permitted estimates of locations of sound sources that varied through 20-dB ranges. Sound localization based on spike patterns that preserved details of spike timing consistently was more accurate than localization based on spike counts alone. These results support the hypothesis that sound-source locations are represented by a distributed code and that individual neurons are, in effect, panoramic localizers.

Action Potentials↗

PubMed2006

Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor.

A fuzzy inference system using sensor measurements was developed to estimate the influent COD/N ratio and ammonia load. The sensors measured ORP, DO and pH. The sensor profiles had a close relationship with the influent COD/N ratio and ammonia load. To confirm this operational knowledge for constructing a rule set, a correlation analysis was conducted. The results showed that a rule generation method based only on operational knowledge did not generate a sufficiently accurate relationship between sensor measurements and target variables. To compensate for this defect, a decision tree algorithm was used as a standardized method for rule generation. Given a set of inputs, this algorithm was used to determine the output variables. However, the generated rules could not estimate the continuous influent COD/N ratio and ammonia load. Fuzzified rules and the fuzzy inference system were developed to overcome this problem. The fuzzy inference system estimated the influent COD/N ratio and ammonia load quite well. When these results were compared to the results from a predictive polynomial neural network model, the fuzzy inference system was more stable.

Algorithms↗

PubMed2006

Using a state-space model with hidden variables to infer transcription factor activities.

MOTIVATION: In a gene regulatory network, genes are typically regulated by transcription factors (TFs). Transcription factor activity (TFA) is more difficult to measure than gene expression levels are. Other models have extracted information about TFA from gene expression data, but without explicitly modeling feedback from the genes. We present a state-space model (SSM) with hidden variables. The hidden variables include regulatory motifs in the gene network, such as feedback loops and auto-regulation, making SSM a useful complement to existing models. RESULTS: A gene regulatory network incorporating, for example, feed-forward loops, auto-regulation and multiple-inputs was constructed with an SSM model. First, the gene expression data were simulated by SSM and used to infer the TFAs. The ability of SSM to infer TFAs was evaluated by comparing the profiles of the inferred and simulated TFAs. Second, SSM was applied to gene expression data obtained from Escherichia coli K12 undergoing a carbon source transition and from the Saccharomyces cerevisiae cell cycle. The inferred activity profile for each TF was validated either by measurement or by activity information from the literature. The SSM model provides a probabilistic framework to simulate gene regulatory networks and to infer activity profiles of hidden variables. AVAILABILITY: Supplementary data and Matlab code will be made available at the URL below. SUPPLEMENTARY INFORMATION: http://www.chems.msu.edu/groups/chan/ssm.zip.

Algorithms↗

PubMed2006

A metabolic network in the evolutionary context: multiscale structure and modularity.

The enormous complexity of biological networks has led to the suggestion that networks are built of modules that perform particular functions and are "reused" in evolution in a manner similar to reusable domains in protein structures or modules of electronic circuits. Analysis of known biological networks has revealed several modules, many of which have transparent biological functions. However, it remains to be shown that identified structural modules constitute evolutionary building blocks, independent and easily interchangeable units. An alternative possibility is that evolutionary modules do not match structural modules. To investigate the structure of evolutionary modules and their relationship to functional ones, we integrated a metabolic network with evolutionary associations between genes inferred from comparative genomics. The resulting metabolic-genomic network places metabolic pathways into evolutionary and genomic context, thereby revealing previously unknown components and modules. We analyzed the integrated metabolic-genomic network on three levels: macro-, meso-, and microscale. The macroscale level demonstrates strong associations between neighboring enzymes and between enzymes that are distant on the network but belong to the same linear pathway. At the mesoscale level, we identified evolutionary metabolic modules and compared them with traditional metabolic pathways. Although, in some cases, there is almost exact correspondence, some pathways are split into independent modules. On the microscale level, we observed high association of enzyme subunits and weak association of isoenzymes independently catalyzing the same reaction. This study shows that evolutionary modules, rather than pathways, may be thought of as regulatory and functional units in bacterial genomes.

Biological Evolution↗