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PubMed2000

On imputing function to structure from the behavioural effects of brain lesions.

What is the link, if any, between the patterns of connections in the brain and the behavioural effects of localized brain lesions? We explored this question in four related ways. First, we investigated the distribution of activity decrements that followed simulated damage to elements of the thalamocortical network, using integrative mechanisms that have recently been used to successfully relate connection data to information on the spread of activation, and to account simultaneously for a variety of lesion effects. Second, we examined the consequences of the patterns of decrement seen in the simulation for each type of inference that has been employed to impute function to structure on the basis of the effects of brain lesions. Every variety of conventional inference, including double dissociation, readily misattributed function to structure. Third, we tried to derive a more reliable framework of inference for imputing function to structure, by clarifying concepts of function, and exploring a more formal framework, in which knowledge of connectivity is necessary but insufficient, based on concepts capable of mathematical specification. Fourth, we applied this framework to inferences about function relating to a simple network that reproduces intact, lesioned and paradoxically restored orientating behaviour. Lesion effects could be used to recover detailed and reliable information on which structures contributed to particular functions in this simple network. Finally, we explored how the effects of brain lesions and this formal approach could be used in conjunction with information from multiple neuroscience methodologies to develop a practical and reliable approach to inferring the functional roles of brain structures.

Behavior↗

PubMed1992

Dynamics for communications data.

To create a complex dynamical model for a complex system, it is normally necessary to have a directed graph of the network, a dynamical model for each node, and a coupling function for each directed edge. But in many applications, the only observable data consists of communications from one node to another. In this situation, the modeler may infer a complex dynamical model for the network without any explicit knowledge of the independent dynamical behavior of the component systems (nodes). Here we present one procedure for this type of modeling problem, inspired by the attractor reconstruction procedure of chaos theory. Part of this proposal consists of a strategy for computer graphic presentation of the interactive dynamics of the complex system (or social network of dynamical schemes) called a netscope. We can imagine applications to diverse situations, such as decision groups, management, forecasting, international relations, classroom monitoring, therapy (personal, family, group, etc.) and distributed processors, to name a few.

Communication↗

PubMed2006

An integrative approach for causal gene identification and gene regulatory pathway inference.

MOTIVATION: Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms. RESULTS: We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)-DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network. AVAILABILITY: Source code is available upon request.

Chromosome Mapping↗

PubMed2005

Temporal correlations in stochastic models of double bursting during simulated locomotion.

The output of the spinal central pattern generator for locomotion falls into two broad categories: alternation between antagonistic muscles and double bursting within muscles acting on multiple joints. We first model an alternating half-center and then present two different models of double bursting. The first double-bursting model consists of a central clock with an explicit one-to-one mapping between interneuron activity and model output. The second double-bursting model consists of a half-center with an added feedback neuron. Models are built using rate-coded leaky integrator neurons with slow self-inhibition. Structure-function relationships are explored by the addition of noise. The interaction of noise with the dynamics of each network creates a unique pattern of correlation between phases of the simulated cycle. The effects of noise can be explained by perturbation of deterministic versions of the networks. Three basic results were obtained: slow self-inhibitory currents lead to correlations between parts of the step cycle that are separated in time and network relative; model outputs are most sensitive to perturbations presented just before competitive switches in network activity, and clock-like models possess substantial symmetries within the correlation structure of burst durations, whereas the correlation structure of feedback models are asymmetric. Our models suggest that variability in burst length durations can be analyzed to make inferences about the structure of the spinal networks for locomotion. In particular, correlation patterns within double-bursting outputs may yield important clues regarding the interaction between more central, clock-like networks and feedback from more peripheral interneurons.

Animals↗

PubMed2001

Association of fibrillin 1 single-nucleotide polymorphism haplotypes with systemic sclerosis in Choctaw and Japanese populations.

OBJECTIVE: Previously, we demonstrated with the use of microsatellite markers that a 2-cM haplotype on chromosome 15q containing the fibrillin 1 gene (FBN1) was strongly associated with systemic sclerosis (SSc) in the Choctaw, a population with high SSc prevalence. In this study, all 69 known FBN1 exons were sequenced to ascertain the presence of changes that might show associations with SSc in the Choctaw and Japanese SSc patients and controls. METHODS: Screening of FBN1 exons was accomplished by polymerase chain reaction-based fluorescence sequencing of genomic DNA using single-nucleotide polymorphism (SNP) haplotypes, and their frequencies were determined with a new algorithm that recognizes past recombination events between sites. Haplotype phylogenies were inferred using the median-joining network analysis. RESULTS: Five SNPs were identified in FBN1. They are located in the 5'-untranslated region (SNP-1), exon 15 (SNP-2), intron 17 (SNP-3), exon 27 (SNP-4), and intron 27 (SNP-5). Only SNP-1 (T-->C) demonstrated an association with SSc in the Choctaw. Eleven FBN1 SNP haplotypes were ascertained in the Choctaw population, 2 of which (SNPs 5 and 6) were found only in the SSc patients. These same FBN1 SNP haplotypes were associated with SSc in the Japanese. CONCLUSION: A SNP in the 5'-untranslated region of FBN1 (SNP-1, C allele) was strongly associated with SSc in the Choctaw. Furthermore, this polymorphism is present on 2 unique FBN1 haplotypes found only in Choctaw SSc patients. The same 2 haplotypes demonstrate associations with SSc in the Japanese. These data extend the earlier microsatellite studies and are consistent with the hypothesis that FBN1 or a nearby gene on chromosome 15q is involved in SSc susceptibility in the Choctaw and the Japanese.

5' Untranslated Regions↗

PubMed1993

A probabilistic rule-based expert system.

This paper explores a medical expert system combining techniques of Bayesian network modelling with ideas of weighted inference rules. The weights of the individual rules can be estimated objectively from a training set of actual cases; and they can be used in a Monte Carlo stimulation to estimate objectively conditional probabilities of diagnosis given particular combinations of symptoms. The paper describes and evaluates a medical expert system built according to this design. The diagnostic accuracy of the program was found to be similar to that obtained through the usual application of Bayes theorem with the assumption of conditional independence of symptoms given disease, even though the Bayesian classifier has more than 70 times as many numerical parameters. The method may be promising in cases where small training sets do not permit accurate estimation of large numbers of parameters.

Abdominal Pain↗

PubMed2005

A comparison of three modelling approaches for quantitative risk assessment using the case study of Salmonella spp. in poultry meat.

A comprehensive review of both the scientific literature and industry practices was undertaken to identify and quantify all sources of contamination throughout the entire poultry meat production chain by Salmonella spp. This information was used to develop a quantitative risk assessment (QRA) model for Salmonella in the production chain from the breeder farm to the chilled carcass. This was subsequently used as the basis on which to compare the merits of three approaches to QRA modelling in such systems. The original model used a Bayesian Network (BN). The second method was a Markov chain Monte Carlo (MCMC) approach, a numerical Bayesian technique which retained a similar network structure but allowed further development, such as the separation of variability and uncertainty. The third method was a more detailed simulation model. The BN responds immediately to changes, such as entering evidence, because it does not use simulation and can propagate information from any point in the network to all others by Bayesian inference. However, it requires all the variables to be discrete, which introduces errors if continuous variables have to be discretized. These errors can accumulate. The MCMC approach does not require discrete variables while retaining some of the properties of the BN model, such as the ability to draw inferences from evidence. Finally, the simulation offers greater flexibility, such as consideration of the individual carcass, but may be more complex to implement as a result and sacrifices the ability to propagate evidence.

Animals↗

PubMed2004

GeneWays: a system for extracting, analyzing, visualizing, and integrating molecular pathway data.

The immense growth in the volume of research literature and experimental data in the field of molecular biology calls for efficient automatic methods to capture and store information. In recent years, several groups have worked on specific problems in this area, such as automated selection of articles pertinent to molecular biology, or automated extraction of information using natural-language processing, information visualization, and generation of specialized knowledge bases for molecular biology. GeneWays is an integrated system that combines several such subtasks. It analyzes interactions between molecular substances, drawing on multiple sources of information to infer a consensus view of molecular networks. GeneWays is designed as an open platform, allowing researchers to query, review, and critique stored information.

Artificial Intelligence↗

PubMed2006

Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization.

Dynamical causal modeling (DCM) of evoked responses is a new approach to making inferences about connectivity changes in hierarchical networks measured with electro- and magnetoencephalography (EEG and MEG). In a previous paper, we illustrated this concept using a lead field that was specified with infinite prior precision. With this prior, the spatial expression of each source area, in the sensors, is fixed. In this paper, we show that using lead field parameters with finite precision enables the data to inform the network's spatial configuration and its expression at the sensors. This means that lead field and coupling parameters can be estimated simultaneously. Alternatively, one can also view DCM for evoked responses as a source reconstruction approach with temporal, physiologically informed constraints. We will illustrate this idea using, for each area, a 4-shell equivalent current dipole (ECD) model with three location and three orientation parameters. Using synthetic and real data, we show that this approach furnishes accurate and robust conditional estimates of coupling among sources and their orientations.

Auditory Perception↗

PubMed2006

Dynamic causal modeling: a generative model of slice timing in fMRI.

Dynamic causal modeling (DCM) of functional magnetic resonance imaging (fMRI) data allows one to make inferences about the architecture of distributed networks in the brain, in terms of effective connectivity. fMRI data are usually acquired using echo planar imaging (EPI). EPI sequences typically acquire slices at different times over a few seconds. DCM, in its original inception, was not informed about these slice timings and assumed that all slices were acquired simultaneously. It has been shown that DCM can cope with slice timing differences of up to 1 s. However, many fMRI studies employ a repetition time (TR) of 3 to 5 s, which precludes a straightforward DCM of these data. We show that this limitation can be overcome easily by including slice timing in the DCM. Using synthetic data we show that the extended DCM furnishes veridical posterior means, even if there are large slice-timing differences. Model comparisons show that, in general, the extended DCM out-performs the original model. We contrast the modeling of slice timing, in the context of DCM, with the less effective approach of 'slice-timing correction', prior to modeling. We apply our procedure to real data and show that slice timings are important parameters. We conclude that, generally, one should use DCM with slice timing.

Humans↗

PubMed2001

Tactile shape processing.

Neuroimaging techniques may aid in the identification of areas of the human brain that are involved in tactile shape perception. Bodegård et al. (2001) relate differences in the properties of tactile stimuli to differences in areas of cortical activation to infer tactile processing in the somatosensory network.

Brain↗

PubMedDate not supplied

Gap junctions between AII amacrine cells and calbindin-positive bipolar cells in the rabbit retina.

Electrical synapses or gap junctions occur between many retinal neurons. However, in most cases, the gap junctions have not been visualized directly. Instead, their presence has been inferred from tracer spread throughout the network of cells. Thus, tracer coupling is taken as a marker for the presence of gap junctions between coupled cells. AII amacrine cells are critical interneurons in the rod pathway of the mammalian retina. Rod bipolar cell output passes to AII amacrine cells, which in turn make conventional synapses with OFF cone bipolar cells and gap junctions with ON cone bipolar cells. Injections of biotinylated tracers into AII amacrine cells reveals coupling between the AII amacrine cell network and heterologous coupling with a variety of ON cone bipolar cells, including the calbindin-positive cone bipolar cell. To directly visualize gap junctions in this network, we prepared material for electron microscopy that was double labeled with antibodies to calretinin and calbindin to label AII amacrine cells and calbindin-positive cone bipolar cells, respectively. AII amacrine cells were postsynaptic to large vesicle-laden rod bipolar terminals, as previously reported. Gap junctions were identified between AII amacrine cells and calbindin-positive cone bipolar cell terminals identified by the presence of immunostaining and ribbon synapses. This represents direct confirmation of gap junctions between two different yet positively identified cells, which are tracer coupled, and provides additional evidence that tracer coupling with Neurobiotin indicates the presence of gap junctions. These results also definitively establish the presence of gap junctions between AII amacrine cells and calbindin bipolar cells which can therefore carry rod signals to the ON alpha ganglion cell.

Animals↗

PubMed2003

Social networks and risk of neural tube defects.

The contribution of social support and social networks to risk of neural tube defects (NTDs) has not been explored, despite evidence that various aspects of the social environment contribute to their etiology. Using data from a population-based case-control study of deliveries occurring in California from 1989 to 1991, this study investigates whether social networks, as measured by the presence and frequency of various types of social contacts, are associated with NTD risk. In-person interviews were conducted with 538 (88% of eligible) mothers of NTD cases and with mothers of 539 non-malformed controls (88%) on average 5 months from the term delivery date. Unadjusted results indicate that women who reported having close friends, close relatives, or monthly contacts with close friends or relatives had 30-70% lower risks of having NTD-affected pregnancies, relative to women reporting 'none' to any of these measures. Only the presence of close friends was associated with reduced NTD risk after adjustment for other aspects of the social network and several potential covariates. This study is the first to investigate the association between social networks and risk of NTDs. Stronger inferences await replication and extension of these results.

California↗

PubMed2003

NSL a Neuro-Symbolic Language for a Neuro-Symbolic Processor (NSP).

A Neuro-Symbolic Language for monotonic and non-monotonic parallel logical inference by means of artificial neural networks (ANNs) is presented. Both the language and its compiler have been designed and implemented in order to translate the neural representation of a given problem into a VHDL software, which in turn can set devices such as FPGA. The result of this operation leads to an electronic circuit that we call NSP (Neuro-Symbolic Processor).

Artificial Intelligence↗

PubMed2006

SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms.

BACKGROUND: The development of algorithms to infer the structure of gene regulatory networks based on expression data is an important subject in bioinformatics research. Validation of these algorithms requires benchmark data sets for which the underlying network is known. Since experimental data sets of the appropriate size and design are usually not available, there is a clear need to generate well-characterized synthetic data sets that allow thorough testing of learning algorithms in a fast and reproducible manner. RESULTS: In this paper we describe a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms. CONCLUSION: This network generation technique offers a valid alternative to existing methods. The topological characteristics of the generated networks more closely resemble the characteristics of real transcriptional networks. Simulation of the network scales well to large networks. The generator models different types of biological interactions and produces biologically plausible synthetic gene expression data.

Algorithms↗

PubMed1990

The effects of nodality on the formation of equivalence classes.

A four-member equivalence class (A----B----C----D) can be formed by training AB, BC, and CD. The nodal stimuli, B and C, mediate all of the derivative (transitive and equivalence) relations in the class. The derivative relations AC, CA, BD, and DB are separated by one node, whereas AD and DA are separated by two nodes. How do the number of nodes that separate the stimuli in a derivative relation influence the induction of stimulus control exerted by that relation? Seven college students learned two four-member classes made up of nonsense syllables. After training, all derivative relations were presented repeatedly without informative feedback. Stimulus control exerted by each derivative relation was assessed concurrently. For the 7 subjects, control exerted by the derivative relations increased gradually with repeated presentations. With 6 of the 7 subjects, the one-node relations exerted more control than the two-node relations during the process. However, the disparity between the one- and two-node relations decreased with repeated presentations. Eventually, all derivative relations exerted complete control. The control exerted by derivative relations during induction was inversely related to the number of nodes separating the terms in the derivative relations. These results demonstrate that nodal distance is a determinant of the relatedness of stimuli in equivalence classes. The findings are discussed in terms of remote association, semantic memory networks, and the study of transitive inference.

Adult↗

PubMed2004

Beyond average protein secondary structure content prediction using FTIR spectroscopy.

This paper demonstrates that secondary structure information beyond purely protein secondary structure content can be predicted from FTIR (Fourier transform infrared spectroscopy) spectra of proteins with a high degree of accuracy. Both neural networks and adaptive neuro-fuzzy inference systems (ANFISs) were employed to predict helix/sheet segment information. The best results were achieved using ANFISs with fuzzy subtractive clustering based on normalised, compressed amide I data with an average SEP (standard error of prediction, root mean of squared errors) of 1.51. Predictions for average helix/sheet length based merely on the amide I band maximum position in combination with the full-width at half-height resulted in a comparable average SEP of 1.62. This suggests the importance of information on the position and width of the amide I band maximum for the prediction of helix/sheet segment information. Finally, the most promising pattern recognition approaches found in this study were applied to a protein with an as yet unknown x-ray structure: native a1-antichymotrypsin (a1-ACT).

Algorithms↗

PubMed2005

A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics.

Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity. Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.

Journal Article↗