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PubMed1994

Artificial intelligence within the chemical laboratory.

Various techniques within the area of artificial intelligence such as expert systems and neural networks may play a role during the problem-solving processes within the clinical biochemical laboratory. Neural network analysis provides a non-algorithmic approach to information processing, which results in the ability of the computer to form associations and to recognize patterns or classes among data. It belongs to the machine learning techniques which also include probabilistic techniques such as discriminant function analysis and logistic regression and information theoretical techniques. These techniques may be used to extract knowledge from example patients to optimize decision limits and identify clinically important laboratory quantities. An expert system may be defined as a computer program that can give advice in a well-defined area of expertise and is able to explain its reasoning. Declarative knowledge consists of statements about logical or empirical relationships between things. Expert systems typically separate declarative knowledge residing in a knowledge base from the inference engine: an algorithm that dynamically directs and controls the system when it searches its knowledge base. A tool is an expert system without a knowledge base. The developer of an expert system uses a tool by entering knowledge into the system. Many, if not the majority of problems encountered at the laboratory level are procedural. A problem is procedural if it is possible to write up a step-by-step description of the expert's work or if it can be represented by a decision tree. To solve problems of this type only small expert system tools and/or conventional programming are required.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗

PubMed2003

Identification of in vivo enzyme activities in the cometabolism of glucose and acetate by Saccharomyces cerevisiae by using 13C-labeled substrates.

A detailed characterization of the central metabolic network of Saccharomyces cerevisiae CEN.PK 113-7D was carried out during cometabolism of different mixtures of glucose and acetate, using aerobic C-limited chemostats in which one of these two substrates was labeled with (13)C. To confirm the role of malic enzyme, an isogenic strain with the corresponding gene deleted was grown under the same conditions. The labeling patterns of proteinogenic amino acids were analyzed and used to estimate metabolic fluxes and/or make inferences about the in vivo activities of enzymes of the central carbon metabolism and amino acid biosynthesis. Malic enzyme flux increased linearly with increasing acetate fraction. During growth on a very-high-acetate fraction, the activity of malic enzyme satisfied the biosynthetic needs of pyruvate in the mitochondria, while in the cytosol pyruvate was supplied via pyruvate kinase. In several cases enzyme activities were unexpectedly detected, e.g., the glyoxylate shunt for a very-low-acetate fraction, phosphoenolpyruvate carboxykinase for an acetate fraction of 0.46 C-mol of acetate/C-mol of substrate, and glucose catabolism to CO(2) via the tricarboxylic acid cycle for a very-high-acetate fraction. Cytoplasmic alanine aminotransferase activity was detected, and evidence was found that alpha-isopropylmalate synthase has two active forms in vivo, one mitochondrial and the other a short cytoplasmic form.

Acetates↗

PubMed2006

Diffusion kernel-based logistic regression models for protein function prediction.

Assigning functions to unknown proteins is one of the most important problems in proteomics. Several approaches have used protein-protein interaction data to predict protein functions. We previously developed a Markov random field (MRF) based method to infer a protein's functions using protein-protein interaction data and the functional annotations of its protein interaction partners. In the original model, only direct interactions were considered and each function was considered separately. In this study, we develop a new model which extends direct interactions to all neighboring proteins, and one function to multiple functions. The goal is to understand a protein's function based on information on all the neighboring proteins in the interaction network. We first developed a novel kernel logistic regression (KLR) method based on diffusion kernels for protein interaction networks. The diffusion kernels provide means to incorporate all neighbors of proteins in the network. Second, we identified a set of functions that are highly correlated with the function of interest, referred to as the correlated functions, using the chi-square test. Third, the correlated functions were incorporated into our new KLR model. Fourth, we extended our model by incorporating multiple biological data sources such as protein domains, protein complexes, and gene expressions by converting them into networks. We showed that the KLR approach of incorporating all protein neighbors significantly improved the accuracy of protein function predictions over the MRF model. The incorporation of multiple data sets also improved prediction accuracy. The prediction accuracy is comparable to another protein function classifier based on the support vector machine (SVM), using a diffusion kernel. The advantages of the KLR model include its simplicity as well as its ability to explore the contribution of neighbors to the functions of proteins of interest.

Databases, Protein↗

PubMed2022

A deep learning approach to real-time HIV outbreak detection using genetic data.

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

Humans↗

PubMed2003

Horizontal gene transfer and phylogenetics.

The initial analysis of complete genomes has suggested that horizontal gene transfer events are very frequent between microorganisms. This could potentially render the inference, and even the concept itself, of the organismal phylogeny impossible. However, a coherent phylogenetic pattern has recently emerged from an analysis of about a hundred genes, the so-called 'core', strongly suggesting that it is possible to infer the phylogeny of prokaryotes. Also, estimation of the frequency of horizontal gene transfers at the genome level in a phylogenetic context seems to indicate that it is rather low, although of significant biological impact. Nevertheless, it should be emphasized that the history of microorganisms cannot be properly represented by the phylogeny of the core, which represents only a tiny fraction of the genome. This history, even if horizontal gene transfers are rare, should be represented by a network surrounding the core phylogeny.

Bacteria↗

PubMed2003

Variational mixture of Bayesian independent component analyzers.

There has been growing interest in subspace data modeling over the past few years. Methods such as principal component analysis, factor analysis, and independent component analysis have gained in popularity and have found many applications in image modeling, signal processing, and data compression, to name just a few. As applications and computing power grow, more and more sophisticated analyses and meaningful representations are sought. Mixture modeling methods have been proposed for principal and factor analyzers that exploit local gaussian features in the subspace manifolds. Meaningful representations may be lost, however, if these local features are nongaussian or discontinuous. In this article, we propose extending the gaussian analyzers mixture model to an independent component analyzers mixture model. We employ recent developments in variational Bayesian inference and structure determination to construct a novel approach for modeling nongaussian, discontinuous manifolds. We automatically determine the local dimensionality of each manifold and use variational inference to calculate the optimum number of ICA components needed in our mixture model. We demonstrate our framework on complex synthetic data and illustrate its application to real data by decomposing functional magnetic resonance images into meaningful-and medically useful-features.

Bayes Theorem↗

PubMed2006

A regression-based K nearest neighbor algorithm for gene function prediction from heterogeneous data.

BACKGROUND: As a variety of functional genomic and proteomic techniques become available, there is an increasing need for functional analysis methodologies that integrate heterogeneous data sources. METHODS: In this paper, we address this issue by proposing a general framework for gene function prediction based on the k-nearest-neighbor (KNN) algorithm. The choice of KNN is motivated by its simplicity, flexibility to incorporate different data types and adaptability to irregular feature spaces. A weakness of traditional KNN methods, especially when handling heterogeneous data, is that performance is subject to the often ad hoc choice of similarity metric. To address this weakness, we apply regression methods to infer a similarity metric as a weighted combination of a set of base similarity measures, which helps to locate the neighbors that are most likely to be in the same class as the target gene. We also suggest a novel voting scheme to generate confidence scores that estimate the accuracy of predictions. The method gracefully extends to multi-way classification problems. RESULTS: We apply this technique to gene function prediction according to three well-known Escherichia coli classification schemes suggested by biologists, using information derived from microarray and genome sequencing data. We demonstrate that our algorithm dramatically outperforms the naive KNN methods and is competitive with support vector machine (SVM) algorithms for integrating heterogenous data. We also show that by combining different data sources, prediction accuracy can improve significantly CONCLUSION: Our extension of KNN with automatic feature weighting, multi-class prediction, and probabilistic inference, enhance prediction accuracy significantly while remaining efficient, intuitive and flexible. This general framework can also be applied to similar classification problems involving heterogeneous datasets.

Algorithms↗

PubMed2004

A mixed model approach to identify yeast transcriptional regulatory motifs via microarray experiments.

A genome-wide location analysis method has been introduced as a means to simultaneously study protein-DNA binding interactions for a large number of genes on a microarray platform. Identification of interactions between transcription factors (TF) and genes provide insight into the mechanisms that regulate a variety of cellular responses. Drawing proper inferences from the experimental data is key to finding statistically significant TF-gene binding interactions. We describe how the analysis and interpretation of genome-wide location data can be fit into a traditional statistical modeling framework that considers the data across all arrays and formulizes appropriate hypothesis tests. The approach is illustrated with data from a yeast transcription factor binding experiment that illustrates how identified TF-gene interactions can enhance initial exploration of transcriptional regulatory networks. Examples of five kinds of transcriptional regulatory structure are also demonstrated. Some stark differences with previously published results are explored.

Journal Article↗

PubMed2006

Application of artificial neural networks to assess pesticide contamination in shallow groundwater.

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R=0.87) and pesticide detection efficiency (E=89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination.

Environmental Monitoring↗

PubMed1998

Comparative analysis of the noncollagenous NC1 domain of type IV collagen: identification of structural features important for assembly, function, and pathogenesis.

Type IV collagen alpha1-alpha6 chains have important roles in the assembly of basement membranes and are implicated in the pathogenesis of Goodpasture syndrome, an autoimmune disorder, and Alport syndrome, a hereditary renal disease. We report comparative sequence analyses and structural predictions of the noncollagenous C-terminal globular NC1 domain (28 sequences). The inferred tree verified that type IV collagen sequences fall into two groups, alpha1-like and alpha2-like, and suggested that vertebrate alpha3/alpha4 sequences evolved before alpha1/alpha2 and alpha5/alpha6. About one fifth of NC1 residues were identified to confer either the alpha1 or alpha2 group-specificity. These residues accumulate opposite charge in subdomain B of alpha1 (positive) and alpha2 (negative) sequences and may play a role in the stoichiometric chain selection upon type IV collagen assembly. Neural network secondary structure prediction on multiple aligned sequences revealed a subdomain core structure consisting of six hydrophobic beta-strands and one short alpha-helix with a significant hydrophobic moment. The existence of opposite charges in the alpha-helices may carry implications for intersubdomain interactions. The results provide a rationale for defining the epitope that binds Goodpasture autoantibodies and a framework for understanding how certain NC1 mutations may lead to Alport syndrome. A search algorithm, based entirely on amino acid properties, yielded a possible similarity of NC1 to tissue inhibitor of metalloproteinases (TIMP) and prompted an investigation of a possible functional relationship. The results indicate that NC1 preparations decrease the activity of matrix metalloproteinases 2 and 3 (MMP-2, MMP-3) toward a peptide substrate, though not to [14C]-gelatin. We suggest that an ancestral NC1 may have been incorporated into type IV collagen as an evolutionarily mobile domain carrying proteinase inhibitor function.

Algorithms↗

PubMed2005

Functional localization and double dissociations: the relationship between internal structure and behavior.

Lesioning studies are often used in cognitive neuroscience to make inferences about the architecture of cognition. Recently, computational models have been used to address some of the underlying assumptions-such as modularity and locality-often implicitly used when interpreting lesion data. In this article, we explore the "functional localization" assumption and its role in interpreting lesioning data, especially from double dissociations. The functional localization assumption states that units or subunits within an information processing system become functionally specialized for dealing with specific aspects of the input environment. Networks were trained on one of two problems-an abstract "rules and sub-rules" problem, and a more concrete "logic classification" problem-and then systematically lesioned. Networks were analyzed in terms of their overt behavior, and more importantly, in terms of their internal structure. Performance deficits in both form and magnitude could be directly related to the ablated internal structure of the networks. That is, if an ablated area had little or no functional localization, then little or no behavioral dissociations were observed. If, however, the ablated area had very specific internal structure, then very specific behavioral dissociations were observed. It is important to note, however, that there was not a one-to-one correspondence between internal structure and behavioral dissociations, implying that cognitive neuroscientists must be careful when using lesioning data to theorize about the functional architecture of cognition.

Brain↗

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↗

PubMed2002

Are developmental disorders like cases of adult brain damage? Implications from connectionist modelling.

It is often assumed that similar domain-specific behavioural impairments found in cases of adult brain damage and developmental disorders correspond to similar underlying causes, and can serve as convergent evidence for the modular structure of the normal adult cognitive system. We argue that this correspondence is contingent on an unsupported assumption that atypical development can produce selective deficits while the rest of the system develops normally (Residual Normality), and that this assumption tends to bias data collection in the field. Based on a review of connectionist models of acquired and developmental disorders in the domains of reading and past tense, as well as on new simulations, we explore the computational viability of Residual Normality and the potential role of development in producing behavioural deficits. Simulations demonstrate that damage to a developmental model can produce very different effects depending on whether it occurs prior to or following the training process. Because developmental disorders typically involve damage prior to learning, we conclude that the developmental process is a key component of the explanation of endstate impairments in such disorders. Further simulations demonstrate that in simple connectionist learning systems, the assumption of Residual Normality is undermined by processes of compensation or alteration elsewhere in the system. We outline the precise computational conditions required for Residual Normality to hold in development, and suggest that in many cases it is an unlikely hypothesis. We conclude that in developmental disorders, inferences from behavioural deficits to underlying structure crucially depend on developmental conditions, and that the process of ontogenetic development cannot be ignored in constructing models of developmental disorders.

Adolescent↗

PubMed2003

The Functional Brain Connectivity Workshop: report and commentary.

This report summarizes the presentations and discussions at a recent workshop entitled 'Functional Brain Connectivity', held in Düsseldorf, Germany. The aims of the workshop were to bring together researchers using different approaches to study connectivity in the brain, to enable them to share conceptual, mathematical and experimental ideas and to develop strategies for future work on functional integration. The main themes that emerged included: (1) the importance of anatomical knowledge in understanding functional interactions the brain; (2) the need to establish common definitions for terms used across disciplines; (3) the need to develop a satisfactory framework for inferring causality from functional imaging and electroencephalographic/magneto-encephalographic data; (4) the importance of analytic tools that capture the dynamics of neural interactions; and (5) the role of experimental paradigms that exploit the functional imaging of perturbations to cortical interactions.

Animals↗

PubMed2005

Bayesian inference for stochastic kinetic models using a diffusion approximation.

This article is concerned with the Bayesian estimation of stochastic rate constants in the context of dynamic models of intracellular processes. The underlying discrete stochastic kinetic model is replaced by a diffusion approximation (or stochastic differential equation approach) where a white noise term models stochastic behavior and the model is identified using equispaced time course data. The estimation framework involves the introduction of m- 1 latent data points between every pair of observations. MCMC methods are then used to sample the posterior distribution of the latent process and the model parameters. The methodology is applied to the estimation of parameters in a prokaryotic autoregulatory gene network.

Bayes Theorem↗

PubMed2004

Error propagation and scaling for tropical forest biomass estimates.

The above-ground biomass (AGB) of tropical forests is a crucial variable for ecologists, biogeochemists, foresters and policymakers. Tree inventories are an efficient way of assessing forest carbon stocks and emissions to the atmosphere during deforestation. To make correct inferences about long-term changes in biomass stocks, it is essential to know the uncertainty associated with AGB estimates, yet this uncertainty is rarely evaluated carefully. Here, we quantify four types of uncertainty that could lead to statistical error in AGB estimates: (i) error due to tree measurement; (ii) error due to the choice of an allometric model relating AGB to other tree dimensions; (iii) sampling uncertainty, related to the size of the study plot; (iv) representativeness of a network of small plots across a vast forest landscape. In previous studies, these sources of error were reported but rarely integrated into a consistent framework. We estimate all four terms in a 50 hectare (ha, where 1 ha = 10(4) m2) plot on Barro Colorado Island, Panama, and in a network of 1 ha plots scattered across central Panama. We find that the most important source of error is currently related to the choice of the allometric model. More work should be devoted to improving the predictive power of allometric models for biomass.

Biomass↗

PubMed2002

Auditing the UMLS for redundant classifications.

The UMLS's Semantic Network (SN) serves as a valuable abstraction for the underlying concept repository called the Metathesaurus (META). Specifically, the SN forms a classification layer for the META, with each of the META's constituent concepts assigned to one or more semantic types in the SN. The rule in the design of the SN is to have concepts explicitly assigned to the lowest possible semantic types in the SN's IS-A hierarchy. Implicit assignment to higher semantic types can be inferred via the IS-A relationships. However, in subsequent versions of the UMLS, unnecessary, simultaneous assignments to descendant and ancestor semantic types have been discovered (e.g., 8,622 in the UMLS 1998 version and 12,657 in the 2001 version). The assignment of concepts to such ancestor semantic types is called redundant classification. There is a need for an automated auditing tool that can identify all these redundant classifications. In this paper, an efficient algorithm for this auditing task is introduced. Details of its application to the current (2001) version of the UMLS are presented and the results are discussed.

Algorithms↗

PubMed1990

Information systems and audit in antenatal care.

The obstetric record as initiated at the antenatal booking clinic essentially identifies the degree of risk engendered in that pregnancy so that consequent obstetric and paediatric management is tailored appropriately. Whether carried by the patient or based in the hospital with a summary carried by the patient (shared-care card), this record should be exhaustive, the emphasis being on quality, not quantity, of information recorded. To obviate human error in history-taking, patient management or record transcription, we believe on-line computerization of patient records with spin-off paperwork to be the only patient management system to fulfil the above criteria. User-friendly software can be designed with highly branching programmes which provide clinical action suggestions in high-risk cases. Various 'error traps' enhance the accuracy of information recorded. Such systems can be operated by medical and midwifery staff with minimal keyboard skills and are well accepted by patients and staff. Inexpensive and versatile microcomputer networks are excellent for such systems. The operational effects are discussed. Audit means different things to different people and one's view on the subject depends on which definition is selected. Obstetricians are quick to take credit for instituting audit in the form of local and national data collection exercises, such as statistics on perinatal mortality, birthweight, etc. While these exercises certainly constitute observational studies, they cannot be used to make conclusions about the quality of care. There is no sound inference that can be made from a review of information contained in amalgamated databases of hospital statistics. Audit, as properly defined, hinges on inference: the inference that the quality of care was or was not of a high standard. Descriptive statistics, therefore, can be used to generate hypotheses but should not be used as a form of audit, at least not in obstetrics. Auditing the quality of care involves a study of process. It therefore depends on the assumption that we know which practices maximize beneficial outcomes. This exercise is therefore only relevant when we have good evidence linking the process of care with these outcomes. In some cases the accepted standard against which the process of care can be compared is very obvious. In other cases, however, the accepted standard should itself be audited to ensure that it is based on sound evidence.

Confidentiality↗