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Results for “Network inference”

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At least 631 records · Page 35Linked to original sources

PubMed2006

Metachronal coupling between spinal neuronal networks during locomotor activity in newborn rat.

In the present study, we investigate spinal cord neuronal network interactions in the neonatal rat during locomotion. The behavioural and physiological relevance of metachronally propagated locomotor activity were inferred from kinematic, anatomical and in vitro electrophysiological data. Kinematic analysis of freely behaving animals indicated that there is a rhythmic sequential change in trunk curvature during the step cycle. The motoneurons innervating back and tail muscles were identified along the spinal cord using retrograde labelling. Systematic multiple recordings from ventral roots were made to determine the precise intrinsic pattern of coordination in the isolated spinal cord. During locomotor-like activity, rhythmic ventral root motor bursts propagate caudo-rostrally in the sacral and the thoracic spinal cord regions. Plotting the latency as a function of the cycle period revealed that the system adapts the intersegmental latency to the ongoing motor period in order to maintain a constant phase relationship along the spinal axis. The thoracic, lumbar and sacral regions were capable of generating right and left alternating motor bursts when isolated. Longitudinal sections of the spinal cord revealed that both the bilateral antiphase pattern observed for the sacral region with respect to the lumbar segment 2 as well as the intersegmental phase lag were due to cross-cord connections. Together, these results provide physiological evidence that the dynamic changes observed in trunk bending during locomotion are determined by the intrinsic organization of spinal cord networks and their longitudinal and transverse interactions. Similarities between this organization, and that of locomotor pattern generation in more primitive vertebrates, suggest that the circuits responsible for metachronal propagation of motor patterns during locomotion are highly conserved.

Algorithms↗

PubMed1995

An ignorant belief network to forecast glucose concentration from clinical databases.

Ignorant Belief Networks (IBNs) are a class of Bayesian Belief Networks (BBNs) able to reason on the basis of incomplete probabilistic information and to incrementally refine the precision of the inferred probabilities as more information becomes available. In this paper, we will describe how can be used to develop a system able to forecast blood glucose concentration in patients affected by insulin dependent diabetes mellitus (IDDM). The major difference between our approach and the traditional ones is that probability distributions over the IBN are not provided by some human expert or by the current literature but they are directly extracted from a clinical database of IDDM patients. This choice capitalizes on the large amount of information generated by the daily control of blood glucose and allows the system to improve the accuracy of predictions as more information becomes available. We will show how, even with a very small subset of the information needed to specify a BBN, the IBN is able to carry out predictions about the future blood glucose concentration in a patient by explicitly taking into consideration the level of ignorance embedded in the network.

Artificial Intelligence↗

PubMed2003

Continuous-time identification of gene expression models.

One objective of systems biology is to create predictive, quantitative models of the transcriptional regulation networks that govern numerous cellular processes. Gene expression measurements, as provided by microarrays, are commonly used in studies that attempt to infer the regulation underlying these processes. At present, most gene expression models that have been derived from microarray data are based in discrete-time, which have limited applicability to common biological data sets, and may impede the integration of gene expression models with other models of biological processes that are formulated as ordinary differential equations (ODEs). To overcome these difficulties, a continuous-time approach for process identification to identify gene expression models based in ODEs was developed. The approach utilizes the modulating functions method of parameter identification. The method was applied to three simulated systems: (1) a linear gene expression model, (2) an autoregulatory gene expression model, and (3) simulated microarray data from a nonlinear transcriptional network. In general, the approach was well suited for identifying models of gene expression dynamics, capable of accurately identifying parameters for small numbers of data samples in the presence of modest experimental noise. Additionally, numerous insights about gene expression modeling were revealed by the case studies.

Data Interpretation, Statistical↗

PubMed2005

Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system.

In this paper, a novel extension of neural network-based fuzzy model has been proposed to detect lung nodules. The proposed model can automatically identify a set of appropriate fuzzy inference rules, and refine the membership functions through the steepest gradient descent-learning algorithm. Twenty-nine clinical cases involving 583 thick section CT images were tested in this study. Receiver operating characteristic (ROC) analysis was used to evaluate the proposed autonomous pulmonary nodules detection system and yielded an area under the ROC curve of Azs=0.963. The overall detection sensitivity of the proposed method was 89.3% (with p-value less than 0.001), and the false positive was as low as 0.2 per image. This result demonstrates that the proposed neural network-based fuzzy system resolves the most suitable fuzzy rules, improves the detection rate, and reduces false positives compared to other approaches. The proposed system is fully automated with fast processing speed. The studies have shown a high potential for implementation of this system in clinical practice as a CAD tool.

Algorithms↗

PubMed1993

Neural networks and nonlinear regression modelling and control of depth of anaesthesia for spontaneously breathing and ventilated patients.

Various attempts have been made to control the depth of anaesthesia by observing different variables. In some studies, depth of anaesthesia has been correlated with inferential parameters and the control has been made through these inferred parameters. No single system has been reported which provides a fully developed architecture to control the depth of anaesthesia. This study is concerned with the development of controllers and patient models via Artificial Neural Networks and regression analysis. Two types of data sets were used for the training and development of models and controllers. The first set was for spontaneously breathing and the second set for ventilated patients. All of the controllers and patient models gave satisfactory results when tested individually. Later these two sets of controllers and patient models were studied in closed-loop modes. The robustness to the sensitivity of the regression patient model was also investigated. Various tests were performed with these closed-loop situations. Results and performance of these tests are discussed in the paper.

Anesthesia↗

PubMed2004

SINBAD: a neocortical mechanism for discovering environmental variables and regularities hidden in sensory input.

We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environmental regularities. Any neural representation in which such variables are represented only implicitly (thus requiring extra computing to use them) will make the regularities more complex and therefore more difficult, if not impossible, to learn. The task of discovering such important environmental variables is not an easy one since their existence is only indirectly suggested by the sensory input patterns the cortex receives--these variables are "hidden". We present a candidate computational strategy for (i) discovering regularity-simplifying environmental variables, (ii) learning the regularities, and (iii) using regularities in perceptual and decision-making tasks. The SINBAD computational model discovers useful environmental variables through a search for different, but nevertheless highly correlated, functions of any kind over nonoverlapping subsets of the known variables, this being indicative of some important environmental variable that is responsible for the correlation. We suggest that such a search is performed in the neocortex by the dendritic trees of individual pyramidal cells. According to the SINBAD model, the basic function of each pyramidal cell is to (i) discover and represent one of the regularity-simplifying environmental variables and (ii) learn to infer the state of its variable from the states of other variables, represented by other pyramidal cells. A network of such cells--each cell just attending to representation of its variable--can function as a sophisticated and useful inferential model of the outside world.

Association Learning↗

PubMed2006

Effect of stimulus orderability and reinforcement history on transitive responding in pigeons.

Transitive responding in humans and non-human animals has attracted considerable attention because of its presumably inferential nature. In an attempt to replicate our earlier study with crows [Lazareva, O.F., Smirnova, A.A., Bagozkaja, M.S., Zorina, Z.A., Rayevsky, V.V., Wasserman, E.A., 2004. Transitive responding in hooded crows requires linearly ordered stimuli. J. Exp. Anal. Behav. 82, 1-19], we trained pigeons to discriminate overlapping pairs of colored squares (A+ B-, B+ C-, C+ D-, and D+ E-). For some birds, the colored squares, or primary stimuli, were followed by a circle of the same color (feedback stimuli) whose diameter decreased from A to E (Ordered Feedback group); these circles were made available to help order the stimuli along a physical dimension. For other birds, all of the feedback stimuli had the same diameter (Constant Feedback group). In later testing, novel choice pairs were presented, including the critical BD pair. The pigeons' reinforcement history with Stimuli B and D was controlled, so that the birds should not have chosen Stimulus B during the BD test. Unlike the crows, the pigeons selected Stimulus B over Stimulus D in both ordered and Constant Feedback groups, suggesting that the orderability of the post-choice feedback stimuli did not affect pigeons' transitive responding. Post hoc simulations showed that associative models [Wynne, C.D.L., 1995. Reinforcement accounts for transitive inference (TI) performance. Anim. Learn. Behav. 23, 207-217; Siemann, M., Delius, J.D., 1998. Algebraic learning and neural network models for transitive and non-transitive responding. Eur. J. Cogn. Psychol. 10, 307-334] failed to predict pigeons' responding in the BD test.

Animals↗

PubMed2003

Reasoning and working memory: common and distinct neuronal processes.

The neuronal processes underlying reasoning and the related working memory subsystems were examined with functional magnetic resonance imaging (fMRI). Twelve volunteers solved relational reasoning problems which either supported a single (determinate) or several alternative solutions (indeterminate). In a second condition, participants maintained the identical premises of these problems in working memory without making inferences. Although problems were presented in auditory format, activity was detected for both reasoning and maintenance in a network comprising bilaterally the secondary visual cortex, the posterior cingulate cortex, and the medial anterior frontal cortex. In direct comparisons, reasoning was associated with stronger dorsolateral and medial prefrontal activation than maintenance, whereas maintenance led to stronger lateral parietal activation than reasoning. Participants' visuo-spatial abilities ("Block Design" score) covaried positively with behavioral performance and negatively with activity of the precuneus for reasoning, but not for maintenance. These results support the notion that relational reasoning is based on visuo-spatial mental models, and they help to distinguish the neuronal processes related to reasoning itself versus to the maintenance of problem information in working memory.

Adult↗

PubMed2025

Global lncRNA expression profiles in medulloblastoma reveal crucial lncRNA-oncogene interactions in Sonic hedgehog and Group 4.

BACKGROUND: Advances in multi-omic studies have improved medulloblastoma (MB) characterization, yet novel molecular biomarkers are needed to refine tumor biology and therapeutic strategies. Current profiling mainly targets the protein-coding genome, while the potential of noncoding regions remains unexplored. This study aims to identify long noncoding RNAs (lncRNAs), emerging as crucial regulators in MB, as potential key biomarkers specific to molecular group, enhancing understanding of MB's genomic landscape. METHODS: RNA-seq data from 54 Spanish MB patients (C1) and 207 public samples (C2) were analyzed to profile lncRNAs. Expression and Weighted Gene Coexpression Network (WGCNA) analyses were performed to identify lncRNA-oncogene interactions. Group-specific interactions were examined to infer their role in MB pathogenesis and highlight potential lncRNA involvement in disease mechanisms. RESULTS: LncRNA expression profiles identified 4 clusters corresponding to the MB molecular groups, confirming their potential as biomarkers. Expression and WGCNA analyses revealed group-specific lncRNAs for Sonic hedgehog (SHH), Group 3 (Gr3), and Group 4 (Gr4) MB. Lnc-SMARCA2 was exclusively upregulated in SHH MB, and associated with ATOH1 and PDLIM3, key cilium regulators of this group's cell of origin. In Gr4 MB, MGC32805 and LOC107986446 were upregulated and linked to SNCAIP, potentially influencing PRDM6 activation via enhancer hijacking. Additionally, a 5-lncRNA signature linked to phototransduction was exclusive to Gr3, offering insights into its lineage switch and molecular regulation. CONCLUSIONS: Lnc-SMARCA2 and, MGC32805 and LOC107986446, are exclusively deregulated in SHH and Gr4 MB, respectively, and directly associated with group-specific MB oncogenes, representing promising novel biomarkers and therapeutic targets in MB.

cancer biomarkers↗

PubMed2001

Estimating the magnitude of STD cofactor effects on HIV transmission: how well can it be done?

BACKGROUND: If sexually transmitted disease (STD) cofactor effects are strong and STDs are highly prevalent, STD control can be a strategy for HIV prevention. OBJECTIVE: To review possibilities for estimating cofactor effects of STDs on HIV transmission based on observational studies. STUDY DESIGN: This study consisted of an analysis of factors influencing associations between HIV and STDs, which can bias STD cofactor studies, from a sexual network perspective. Study designs that reduce distortions and methods to improve estimates in the presence of confounding are discussed. RESULTS: Standard statistical adjustments of cofactor estimates are insufficient because they ignore clustering between HIV and STDs in partners of study subjects, resulting from population heterogeneity in risk factors and assortative mixing. Reverse causation due to HIV-related immunosuppression may further inflate cofactor estimates. Misclassification of STDs and clustering between STDs can bias estimates in either direction. This study demonstrates quantitatively that ignorance of sexual network effects may result in considerable overestimation of cofactor magnitudes. CONCLUSION: The limitations of observational studies complicate quantitative inferences on the role of STDs in HIV transmission.

Confounding Factors, Epidemiologic↗

PubMed2003

Neuropsychological studies of musical timbre.

Musical timbre is a multidimensional property of sound that allows one to distinguish musical instruments. In this paper, studies that explore the cerebral substrate underlying the processing of musical timbre are discussed. Perceptual asymmetries measured in normal participants, deficits of musical timbre perception obtained in brain-damaged patients, as well as results obtained with various neuroimaging methods are reviewed. The findings obtained in all of these studies generally support the predominant involvement of right temporal lobe areas, and more specifically of its anterior part, in processing spectral and temporal envelopes of musical timbre. However, controversies still exist about the contribution of the left temporal lobe in timbre perception. The necessity of comparing data obtained with different perceptual paradigms (same-different discrimination and similarity judgment) and various types of stimuli (single tones and melodies) was emphasized by reporting lesion studies carried out in patients with unilateral temporal lobe lesions. The few neuroimaging studies published in this domain provided additional and complementary findings. Unlike lesion studies that allow us to infer the cerebral structures that are essential for timbre perception, the latter investigations implicate a more distributed neural network in timbre processing that extends along the superior temporal gyrus to include not only anterior but also posterior temporal regions and possibly frontal areas as well.

Auditory Perception↗

PubMed1993

Network models for sequence evolution.

We introduce a general class of models for sequence evolution that includes network phylogenies. Networks, a generalization of strictly tree-like phylogenies, are proposed to model situations where multiple lineages contribute to the observed sequences. An algorithm to compute the probability distribution of binary character-state configurations is presented and statistical inference for this model is developed in a likelihood framework. A stepwise procedure based on likelihood ratios is used to explore the space of models. Starting with a star phylogeny, new splits (nontrivial bipartitions of the sequence set) are successively added to the model until no significant change in the likelihood is observed. A novel feature of our approach is that the new splits are not necessarily constrained to be consistent with a treelike mode of evolution. The fraction of invariable sites is estimated by maximum likelihood simultaneously with other model parameters and is essential to obtain a good fit to the data. The effect of finite sequence length on the inference methods is discussed. Finally, we provide an illustrative example using aligned VP1 genes from the foot and mouth disease viruses (FMDV). The different serotypes of the FMDV exhibit a range of treelike and network evolutionary relationships.

Aphthovirus↗

PubMed2005

Inferring yeast cell cycle regulators and interactions using transcription factor activities.

BACKGROUND: Since transcription factors are often regulated at the post-transcriptional level, their activities, rather than expression levels may provide valuable information for investigating functions and their interactions. The recently developed Network Component Analysis (NCA) and its generalized form (gNCA) provide a robust framework for deducing the transcription factor activities (TFAs) from various types of DNA microarray data and transcription factor-gene connectivity. The goal of this work is to demonstrate the utility of TFAs in inferring transcription factor functions and interactions in Saccharomyces cerevisiae cell cycle regulation. RESULTS: Using gNCA, we determined 74 TFAs from both wild type and fkh1 fkh2 deletion mutant microarray data encompassing 1529 ORFs. We hypothesized that transcription factors participating in the cell cycle regulation exhibit cyclic activity profiles. This hypothesis was supported by the TFA profiles of known cell cycle factors and was used as a basis to uncover other potential cell cycle factors. By combining the results from both cluster analysis and periodicity analysis, we recovered nearly 90% of the known cell cycle regulators, and identified 5 putative cell cycle-related transcription factors (Dal81, Hap2, Hir2, Mss11, and Rlm1). In addition, by analyzing expression data from transcription factor knockout strains, we determined 3 verified (Ace2, Ndd1, and Swi5) and 4 putative interaction partners (Cha4, Hap2, Fhl1, and Rts2) of the forkhead transcription factors. Sensitivity of TFAs to connectivity errors was determined to provide confidence level of these predictions. CONCLUSION: By subjecting TFA profiles to analyses based upon physiological signatures we were able to identify cell cycle related transcription factors consistent with current literature, transcription factors with potential cell cycle dependent roles, and interactions between transcription factors.

Cell Cycle↗

PubMed2003

An enhanced cosmic-ray flux towards zeta Persei inferred from a laboratory study of the H3+-e- recombination rate.

The H3+ molecular ion plays a fundamental role in interstellar chemistry, as it initiates a network of chemical reactions that produce many molecules. In dense interstellar clouds, the H3+ abundance is understood using a simple chemical model, from which observations of H3+ yield valuable estimates of cloud path length, density and temperature. But observations of diffuse clouds have suggested that H3+ is considerably more abundant than expected from the chemical models. Models of diffuse clouds have, however, been hampered by the uncertain values of three key parameters: the rate of H3+ destruction by electrons (e-), the electron fraction, and the cosmic-ray ionization rate. Here we report a direct experimental measurement of the H3+ destruction rate under nearly interstellar conditions. We also report the observation of H3+ in a diffuse cloud (towards Persei) where the electron fraction is already known. From these, we find that the cosmic-ray ionization rate along this line of sight is 40 times faster than previously assumed. If such a high cosmic-ray flux is ubiquitous in diffuse clouds, the discrepancy between chemical models and the previous observations of H3+ can be resolved.

Journal Article↗

PubMed2005

Species status and phylogeography of two closely related Coptolabrus species (Coleoptera: Carabidae) in South Korea inferred from mitochondrial and nuclear gene sequences.

We investigated the species status and intraspecific phylogeography in South Korea of two ground beetle species, Coptolabrus jankowskii and Coptolabrus smaragdinus (Coleoptera: Carabidae), using statistical parsimony networks and nested clade analyses based on sequences from the mitochondrial cytochrome oxidase subunit I (COI) and nuclear phosphoenolpyruvate carboxykinase (PepCK) and wingless (Wg) genes. Although traditional parsimony tree construction generally failed to resolve interspecific relationships and construct biologically meaningful genealogies, analysis using statistical parsimony networks yielded statistically significant inter- and intraspecific genealogical structures. We found that although these two species represent a notable case of trans-species polymorphisms in both mitochondrial and nuclear gene sequences, their status as separate species was evidenced by the nonrandom association between species and nested clades at various nesting levels. The exceptional occurrence of shared identical or very similar COI sequences was considered to be the result of introgressive hybridization. In addition, range expansion and fragmentation events across the Korean Peninsula and adjacent islands were inferred from nested clade phylogeographical analyses. The COI gene revealed the geographical divergence of major eastern and western clades and historical biogeographical events within each major clade, whereas the nuclear PepCK gene, which did not reveal corresponding east-west clades, indicated past fragmentation and range expansion across wide areas that may have been the result of older biogeographical events. Thus, phylogeographical inferences drawn from analyses of mitochondrial and nuclear genes can reveal different and potentially complementary information about phylogeographical processes.

Animals↗

PubMed1997

A logic-based dynamical theory for a genesis of biological threshold.

To help the study of constructing a formal neuron by computer, we propose a logic-based dynamical theory for a genesis of the biological threshold which specific proteins, such as ion channel proteins, or their networks can produce. By viewing such a protein or a protein network as a computational machine, the statements concerning the states of reaction chains which eventually activate or inactivate the protein are treated. By introducing dynamical systems, associated with an inference process based on statements with continuous truth values, we investigated invariant characters of such dynamics and we obtained a sigmoidal function for an invariant distribution function of the truth values. The domain of solutions of the functional equations regarded as representing the self-description of proteins or protein networks as a machine indicates the emergence of a threshold, namely the realization of dyadic value, 0 or 1, based on the continuous truth values. The results obtained may highlight the mechanism of neuronal threshold in a framework which differs from population dynamics. The derived dynamical systems may also provide a simple model of 'demon' rectifying the thermal fluctuations to drive unidirectional movements.

Animals↗

PubMed2026

Resolving cellular signaling in space and time: From organelle proteomics to spatial phosphoproteomics.

Cellular signaling is inherently organized in space and time, requiring coordinated control of protein localization, molecular interactions, and enzymatic activity across subcellular compartments. Recent advances in chemical biology, protein engineering, and quantitative proteomics have made it possible to interrogate these dimensions in an integrated manner. Here, we highlight emerging strategies to resolve signaling organization across three interconnected dimensions: organelle-resolved proteome mapping to define spatial context, proximity labeling to capture local protein interaction networks, and spatially resolved phosphoproteomics to quantify signaling outputs. Developments in proximity labeling, including split, conditionally activated and light-gated enzymes, enable temporally controlled, context-dependent profiling of transient protein assemblies in living cells. Advances in high-throughput and low-input phosphoproteomics, together with improved computational frameworks for kinase activity inference and subcellular enrichment strategies, are enabling spatially resolved measurement of signaling activity. Together, these approaches are shifting the field from static localization maps toward dynamic models of signaling networks.

Proteomics↗

PubMed2006

Modelling in molecular biology: describing transcription regulatory networks at different scales.

Approaches to describe gene regulation networks can be categorized by increasing detail, as network parts lists, network topology models, network control logic models or dynamic models. We discuss the current state of the art for each of these approaches. We study the relationship between different topology models, and give examples how they can be used to infer functional annotations for genes of unknown function. We introduce a new simple way of describing dynamic models called finite state linear model (FSLM). We discuss the gap between the parts list and topology models on one hand, and network logic and dynamic models, on the other hand. The first two classes of models have reached a genome-wide scale, while for the other model classes high-throughput technologies are yet to make a major impact.

Computational Biology↗