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PubMed1988

Transplantation statistics in the UK--an agenda for the next quinquennium.

Our next quinquennial plan for transplantation studies extends MPI to corneal and unrelated marrow transplantation. It applies Bayesian hierarchical modelling to regional variation in donor procurement and continues a program of special studies to augment national databases with respect to kidney, corneal, heart and liver transplantation. It promotes research collaboration among European and other organ exchange organizations as pioneered in the Council of Europe 1986 Study on High Sensitization, which showed the effectiveness of the network of European organ exchange organizations in liaising with transplant units.

Bayes Theorem↗
PubMed2006

Predicting postoperative nausea and vomiting with the application of an artificial neural network.

BACKGROUND: Several medications have proved to be useful in preventing postoperative nausea and vomiting (PONV). However, routine antiemetic prophylaxis is not cost-effective. We evaluated the accuracy and discriminating power of an artificial neural network (ANN) to predict PONV. METHODS: We analysed data from 1086 in-patients who underwent various surgical procedures under general anaesthesia without antiemetic prophylaxis. Predictors used for ANN training were selected by computing the value of chi(2) statistic and information gain with respect to PONV. The configuration of the ANN was chosen by using a software tool. Then the training of the ANN was performed based on data from a training set (n=656). Testing validation was performed with the remaining patients (n=430) whose outcome regarding PONV was unknown to the ANN. Area under the receiver operating characteristic (ROC) curves were used to quantify predictive performance. ANN performance was compared with those of the Naïve Bayesian classifier model, logistic regression model, simplified Apfel score and Koivuranta score. RESULTS: ANN accuracy was 83.3%, sensitivity 77.9% and specificity 85.0% in predicting PONV. The areas under the ROC curve follow: ANN, 0.814 (0.774-0.850); Naïve Bayesian classifier, 0.570 (0.522-0.617); logistic regression, 0.669 (0.623-0.714); Koivuranta score, 0.626 (0.578-0.672); simplified Apfel score, 0.624 (0.576-0.670). ANN discriminatory power was superior to those of the other predicting models (P<0.05). CONCLUSIONS: The ANN provided the best predictive performance among all tested models.

Adult↗
PubMed2000

No free lunch for noise prediction.

No-free-lunch theorems have shown that learning algorithms cannot be universally good. We show that no free funch exists for noise prediction as well. We show that when the noise is additive and the prior over target functions is uniform, a prior on the noise distribution cannot be updated, in the Bayesian sense, from any finite data set. We emphasize the importance of a prior over the target function in order to justify superior performance for learning systems.

Algorithms↗
PubMed2005

Web content management by self-organization.

We present a new method for content management and knowledge discovery using a topology-preserving neural network. The method, termed topological organization of content (TOC), can generate a taxonomy of topics from a set of unannotated, unstructured documents. The TOC consists of a hierarchy of self-organizing growing chains (GCs), each of which can develop independently in terms of size and topics. The dynamic development process is validated continuously using a proposed entropy-based Bayesian information criterion (BIC). Each chain meeting the criterion spans child chains, with reduced vocabularies and increased specializations. This results in a topological tree hierarchy, which can be browsed like a table of contents directory or web portal. A brief review is given on existing methods for document clustering and organization, and clustering validation measures. The proposed approach has been tested and compared with several existing methods on real world web page datasets. The results have clearly demonstrated the advantages and efficiency in content organization of the proposed method in terms of computational cost and representation. The TOC can be easily adapted for large-scale applications. The topology provides a unique, additional feature for retrieving related topics and confining the search space.

Abstracting and Indexing↗
PubMed2006

An artificial neural network-based expert system for the appraisal of two-car crash accidents.

This paper employs artificial neural network (ANN) to develop an accident appraisal expert system. Two ANN models-- party-based and case-based-- with different hidden neurons are trained and validated by k-fold (k=3) cross validation method. A total of 537 two-car crash accidents (1074 parties involved) are randomly and equally divided into three subsets. For the comparison, a discrimination analysis (DA) model is also calibrated. The results show that the ANN model can achieve a high correctness rate of 85.72% in training and 77.91% in validation and a low Schwarz's Bayesian information criterion (SBC) of -0.82 in training and 0.13 in validation, which indicates that the ANN model is suitable for accident appraisal. Furthermore, in order to measure the importance of each explanatory variable, a general influence (GI) index is computed based on the trained weights of ANN. It is found that the most influential variable is right-of-way, followed by location and alcoholic use. This finding concurs with the prior knowledge in accident appraisal. Thus, for the fair assessment of accident liabilities the correctness of these three key variables is of critical importance to police investigation reports.

Accidents, Traffic↗
PubMed1996

Analytical interpretation of feed-forward nets outputs after training.

The minimization quadratic error criterion which gives rise to the back-propagation algorithm is studied using functional analysis techniques. With them, we recover easily the well-known statistical result which states that the searched global minimum is a function which assigns, to each input pattern, the expected value of its corresponding output patterns. Its application to classification tasks shows that only certain output class representations can be used to obtain the optimal Bayesian decision rule. Finally, our method permits the study of other error criterions, finding out, for instance, that absolute value errors lead to medians instead of mean values.

Algorithms↗
PubMed1998

Flexible Bayesian modelling for survival data.

The analysis of failure time data often involves two strong assumptions. The proportional hazards assumption postulates that hazard rates corresponding to different levels of explanatory variables are proportional. The additive effects assumption specifies that the effect associated with a particular explanatory variable does not depend on the levels of other explanatory variables. A hierarchical Bayes model is presented, under which both assumptions are relaxed. In particular, time-dependent covariate effects are explicitly modelled, and the additivity of effects is relaxed through the use of a modified neural network structure. The hierarchical nature of the model is useful in that it parsimoniously penalizes violations of the two assumptions, with the strength of the penalty being determined by the data.

Bayes Theorem↗
PubMed1998

DIAS-NIDDM--a model-based decision support system for insulin dose adjustment in insulin-treated subjects with NIDDM.

A decision support system has been developed, Diabetes Insulin Advisory System for patients with non-insulin dependent diabetes mellitus (DIAS-NIDDM), assisting in the adjustment of insulin doses in insulin-treated subjects. DIAS-NIDDM uses a causal probabilistic network (CPN) model of carbohydrate metabolism to make stochastic predictions of blood glucose (BG) excursions. The CPN model is an extension of an existing model with an added component representing endogenous insulin secretion. A linear relationship between BG and insulin concentration due to BG stimulated insulin secretion is assumed. Model parameters (pancreatic sensitivity, insulin sensitivity, and time-to-peak of NPH insulin) are estimated by Bayesian probability updating from patient's specific data (food intake, insulin doses, BG measurements) recorded over a period of 4 days. The estimated parameters allow the system to be potentially used as a diagnostic tool to identify abnormalities of carbohydrate metabolism: impaired insulin secretion, insulin resistance and the severity of the impairments. DIAS-NIDDM was used to predict patient-specific BG profiles and advise on insulin doses during a pilot study in eight patients with NIDDM of whom five were treated with insulin. Compared to the administered insulin amount, daily insulin amount advised by DIAS-NIDDM was similar (within 4 U) in three patients, higher by 20% (19 U) in one patient and lower by 40% (18 U) and 50% (11 U) in two patients, respectively. The inter-day coefficient of variation of the daily insulin advice suggests that, at least according to DIAS-NIDDM criteria, day-to-day adjustment of insulin doses is necessary to maintain optimum control.

Computer Simulation↗
PubMed2005

Rhodopseudomonas palustris regulons detected by cross-species analysis of alphaproteobacterial genomes.

Rhodopseudomonas palustris, an alpha-proteobacterium, carries out three of the chemical reactions that support life on this planet: the conversion of sunlight to chemical-potential energy; the absorption of carbon dioxide, which it converts to cellular material; and the fixation of atmospheric nitrogen into ammonia. Insight into the transcription-regulatory network that coordinates these processes is fundamental to understanding the biology of this versatile bacterium. With this goal in mind, we predicted regulatory signals genomewide, using a two-step phylogenetic-footprinting and clustering process that we had developed previously. In the first step, 4,963 putative transcription factor binding sites, upstream of 2,044 genes and operons, were identified using cross-species Gibbs sampling. Bayesian motif clustering was then employed to group the cross-species motifs into regulons. We have identified 101 putative regulons in R. palustris, including 8 that are of particular interest: a photosynthetic regulon, a flagellar regulon, an organic hydroperoxide resistance regulon, the LexA regulon, and four regulons related to nitrogen metabolism (FixK2, NnrR, NtrC, and sigma54). In some cases, clustering allowed us to assign functions to proteins that previously had been annotated with only putative functions; we have identified RPA0828 as the organic hydroperoxide resistance regulator and RPA1026 as a cell cycle methylase. In addition to predicting regulons, we identified a novel inverted repeat that likely forms a highly conserved stem-loop and that occurs downstream of over 100 genes.

Alphaproteobacteria↗
PubMed2006

A fluctuation method to quantify in vivo fluorescence data.

Quantitative in vivo measurements are essential for developing a predictive understanding of cellular behavior. Here we present a technique that converts observed fluorescence intensities into numbers of molecules. By transiently expressing a fluorescently tagged protein and then following its dilution during growth and division, we observe asymmetric partitioning of fluorescence between daughter cells at each division. Such partition asymmetries are set by the actual numbers of proteins present, and thus provide a means to quantify fluorescence levels. We present a Bayesian algorithm that infers from such data both the fluorescence conversion factor and an estimate of the measurement error. Our algorithm works for arbitrarily sized data sets and handles consistently any missing measurements. We verify the algorithm with extensive simulation and demonstrate its application to experimental data from Escherichia coli. Our technique should provide a quantitative internal calibration to systems biology studies of both synthetic and endogenous cellular networks.

Algorithms↗
PubMed2006

Dynamic causal modeling of evoked responses in EEG and MEG.

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.

Auditory Pathways↗
PubMed2004

Advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixtures.

In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed.

Bayes Theorem↗
PubMed1996

Predicting solvent accessibility: higher accuracy using Bayesian statistics and optimized residue substitution classes.

We introduce a novel Bayesian probabilistic method for predicting the solvent accessibilities of amino acid residues in globular proteins. Using single sequence data, this method achieves prediction accuracies higher than previously published methods. Substantially improved predictions-comparable to the highest accuracies reported in the literature to date-are obtained by representing alignments of the example proteins and their homologs as strings of residue substitution classes, depending on the side chain types observed at each alignment position. These results demonstrate the applicability of this relatively simple Bayesian approach to structure prediction and illustrate the utility of the classification methodology previously developed to extract information from aligned sets of structurally related proteins.

Amino Acid Sequence↗
PubMed2006

Large-scale neural models and dynamic causal modelling.

Dynamic causal modelling (DCM) is a method for estimating and making inferences about the coupling among small numbers of brain areas, and the influence of experimental manipulations on that coupling [Friston, K.J., Harrison, L., Penny, W., 2003 Dynamic causal modelling. Neuroimage 19, 1273-1302]. Large-scale neural modelling aims to construct neurobiologically grounded computational models with emergent behaviours that inform our understanding of neuronal systems. One such model has been used to simulate region-specific BOLD time-series [Horwitz, B., Friston, K.J., Taylor, J.G., 2000. Neural modeling and functional brain imaging: an overview. Neural Netw. 13, 829-846]. DCM was used to make inferences about effective connectivity using data generated by a model implementing a visual delayed match-to-sample task [Tagamets, M.A., Horwitz, B., 1998. Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb. Cortex 8, 310-320]. The aim was to explore the validity of inferences made using DCM about the connectivity structure and task-dependent modulatory effects, in a system with a known connectivity structure. We also examined the effects of misspecifying regions of interest. Models with hierarchical connectivity and reciprocal connections were examined using DCM and Bayesian Model Comparison [Penny, W.D., Stephan, K.E., Mechelli, A., Friston, K.J., 2004. Comparing dynamic causal models. Neuroimage 22, 1157-1172]. This approach revealed strong evidence for those models with correctly specified anatomical connectivity. Furthermore, Bayesian model comparison favoured those models when bilinear effects corresponded to their implementation in the neural model. These findings generalised to an extended model with two additional areas and reentrant circuits. The conditional uncertainty of coupling parameter estimates increased in proportion to the number of incorrectly specified regions. These results highlight the role of neural models in establishing the validity of estimation and inference schemes. Specifically, Bayesian model comparison confirms the validity of DCM in relation to a well-characterised and comprehensive neuronal model.

Bayes Theorem↗
PubMed2004

Combination of direct and indirect evidence in mixed treatment comparisons.

Mixed treatment comparison (MTC) meta-analysis is a generalization of standard pairwise meta-analysis for A vs B trials, to data structures that include, for example, A vs B, B vs C, and A vs C trials. There are two roles for MTC: one is to strengthen inference concerning the relative efficacy of two treatments, by including both 'direct' and 'indirect' comparisons. The other is to facilitate simultaneous inference regarding all treatments, in order for example to select the best treatment. In this paper, we present a range of Bayesian hierarchical models using the Markov chain Monte Carlo software WinBUGS. These are multivariate random effects models that allow for variation in true treatment effects across trials. We consider models where the between-trials variance is homogeneous across treatment comparisons as well as heterogeneous variance models. We also compare models with fixed (unconstrained) baseline study effects with models with random baselines drawn from a common distribution. These models are applied to an illustrative data set and posterior parameter distributions are compared. We discuss model critique and model selection, illustrating the role of Bayesian deviance analysis, and node-based model criticism. The assumptions underlying the MTC models and their parameterization are also discussed.

Humans↗
PubMed2025

The signed two-space proximity model for learning representations in protein-protein interaction networks.

MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. RESULTS: S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns. AVAILABILITY: S2-SPM is implemented and freely available under the MIT license at https://github.com/Nicknakis/S2SPM.

Protein Interaction Mapping↗
PubMed2006

Singularities affect dynamics of learning in neuromanifolds.

The parameter spaces of hierarchical systems such as multilayer perceptrons include singularities due to the symmetry and degeneration of hidden units. A parameter space forms a geometrical manifold, called the neuromanifold in the case of neural networks. Such a model is identified with a statistical model, and a Riemannian metric is given by the Fisher information matrix. However, the matrix degenerates at singularities. Such a singular structure is ubiquitous not only in multilayer perceptrons but also in the gaussian mixture probability densities, ARMA time-series model, and many other cases. The standard statistical paradigm of the Cramér-Rao theorem does not hold, and the singularity gives rise to strange behaviors in parameter estimation, hypothesis testing, Bayesian inference, model selection, and in particular, the dynamics of learning from examples. Prevailing theories so far have not paid much attention to the problem caused by singularity, relying only on ordinary statistical theories developed for regular (nonsingular) models. Only recently have researchers remarked on the effects of singularity, and theories are now being developed. This article gives an overview of the phenomena caused by the singularities of statistical manifolds related to multilayer perceptrons and gaussian mixtures. We demonstrate our recent results on these problems. Simple toy models are also used to show explicit solutions. We explain that the maximum likelihood estimator is no longer subject to the gaussian distribution even asymptotically, because the Fisher information matrix degenerates, that the model selection criteria such as AIC, BIC, and MDL fail to hold in these models, that a smooth Bayesian prior becomes singular in such models, and that the trajectories of dynamics of learning are strongly affected by the singularity, causing plateaus or slow manifolds in the parameter space. The natural gradient method is shown to perform well because it takes the singular geometrical structure into account. The generalization error and the training error are studied in some examples.

Journal Article↗
PubMed1999

Higher-order probabilistic perceptrons as Bayesian inference engines.

An explicit structural connection is established between the Bayes optimal classifier operating on K binary input variables and a corresponding two-layer perceptron having normalized output activities and couplings from input to output units of all orders up to K. With suitable modification of connection weights and biases, such a higher-order probabilistic perceptron should in principle be able to learn the statistics of the classification problem and match the a posteriori probabilities given by Bayes optimal inference. Specific training algorithms are developed that allow this goal to be approximated in a controlled variational sense. An application to the task of discriminating between stable and unstable nuclides in nuclear physics yields network models with predictive performance comparable to the best that has been achieved with conventional multilayer perceptrons containing only pairwise connections.

Journal Article↗