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PubMed2006

Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks.

MOTIVATION: The local and global aspects of metabolic network analyses allow us to identify enzymes or reactions that are crucial for the survival of the organism(s), therefore directing us towards the discovery of potential drug targets. RESULTS: We demonstrate a new method ('load points') to rank the enzymes/metabolites in the metabolic network and propose a model to determine and rank the biochemical lethality in metabolic networks (enzymes/metabolites) through 'choke points'. Based on an extended form of the graph theory model of metabolic networks, metabolite structural information was used to calculate the k-shortest paths between metabolites (the presence of more than one competing path between substrate and product). On the basis of these paths and connectivity information, load points were calculated and used to empirically rank the importance of metabolites/enzymes in the metabolic network. The load point analysis emphasizes the role that the biochemical structure of a metabolite, rather than its connectivity (hubs), plays in the conversion pathway. In order to identify potential drug targets (based on the biochemical lethality of metabolic networks), the concept of choke points and load points was used to find enzymes (edges) which uniquely consume or produce a particular metabolite (nodes). A non-pathogenic bacterial strain Bacillus subtilis 168 (lactic acid producing bacteria) and a related pathogenic bacterial strain Bacillus anthracis Sterne (avirulent but toxigenic strain, producing the toxin Anthrax) were selected as model organisms. The choke point strategy was implemented on the pathogen bacterial network of B.anthracis Sterne. Potential drug targets are proposed based on the analysis of the top 10 choke points in the bacterial network. A comparative study between the reported top 10 bacterial choke points and the human metabolic network was performed. Further biological inferences were made on results obtained by performing a homology search against the human genome. AVAILABILITY: The load and choke point modules are introduced in the Pathway Hunter Tool (PHT), the basic version of which is available on http://www.pht.uni-koeln.de.

Bacillus↗

PubMed2006

Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series.

We introduce a grammar-based hybrid approach to reverse engineering nonlinear ordinary differential equation models from observed time series. This hybrid approach combines a genetic algorithm to search the space of model architectures with a Kalman filter to estimate the model parameters. Domain-specific knowledge is used in a context-free grammar to restrict the search space for the functional form of the target model. We find that the hybrid approach outperforms a pure evolutionary algorithm method, and we observe features in the evolution of the dynamical models that correspond with the emergence of favorable model components. We apply the hybrid method to both artificially generated time series and experimentally observed protein levels from subjects who received the smallpox vaccine. From the observed data, we infer a cytokine protein interaction network for an individual's response to the smallpox vaccine.

Algorithms↗

PubMed2001

Benchmarking quantum computers: the five-qubit error correcting code.

The smallest quantum code that can correct all one-qubit errors is based on five qubits. We experimentally implemented the encoding, decoding, and error-correction quantum networks using nuclear magnetic resonance on a five spin subsystem of labeled crotonic acid. The ability to correct each error was verified by tomography of the process. The use of error correction for benchmarking quantum networks is discussed, and we infer that the fidelity achieved in our experiment is sufficient for preserving entanglement.

Journal Article↗

PubMed1996

Dihedral angles of septal "bend" structures in lung parenchyma.

Alveolar parenchyma comprises two interacting tensile systems: the cable system (a network of linear condensations of connective tissue) and the membrane system (a network of quasiplanar alveolar septa). Inferences can be drawn about the mechanics of this structure from it configuration. We reported earlier (E.H. Oldmixon, J.P. Butler, and F.G. Hoppin, Jr. J. Appl. Physiol. 64: 299-307, 1988) that the angles between alveolar septa at the common three-way junctions (J) are nearly uniform, indicating that septal tensions are also nearly uniform. We now report on the interseptal angles at the next most common class of septal junction (B), a structure where two septa meet along a segment of the cable system. We find, first, that the distributions of interseptal angles at B junctions have means > 120 degrees, are narrow, and have few, if any, angles < 120 degrees. The findings of uniform 120 degrees angles at J junctions and a cutoff below 120 degrees at B junctions are also characteristic of soap films supported on a frame, which follows the physical principle of surface area minimization. We suggest that this principle may be operative in parenchymal development and remodeling.

Animals↗

PubMed2006

Text mining of full-text journal articles combined with gene expression analysis reveals a relationship between sphingosine-1-phosphate and invasiveness of a glioblastoma cell line.

BACKGROUND: Sphingosine 1-phosphate (S1P), a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma is not fully understood. Particularly, the concerted role played by matrix metalloproteinases (MMP) and S1P in aggressive tumor behavior and angiogenesis remains to be elucidated. RESULTS: To gain new insights in the effect of S1P on angiogenesis and invasion of this type of malignant tumor, we used microarrays to investigate the gene expression in glioblastoma as a response to S1P administration in vitro. We compared the expression profiles for the same cell lines under the influence of epidermal growth factor (EGF), an important growth factor. We found a set of 72 genes that are significantly differentially expressed as a unique response to S1P. Based on the result of mining full-text articles from 20 scientific journals in the field of cancer research published over a period of five years, we inferred gene-gene interaction networks for these 72 differentially expressed genes. Among the generated networks, we identified a particularly interesting one. It describes a cascading event, triggered by S1P, leading to the transactivation of MMP-9 via neuregulin-1 (NRG-1), vascular endothelial growth factor (VEGF), and the urokinase-type plasminogen activator (uPA). This interaction network has the potential to shed new light on our understanding of the role played by MMP-9 in invasive glioblastomas. CONCLUSION: Automated extraction of information from biological literature promises to play an increasingly important role in biological knowledge discovery. This is particularly true for high-throughput approaches, such as microarrays, and for combining and integrating data from different sources. Text mining may hold the key to unraveling previously unknown relationships between biological entities and could develop into an indispensable instrument in the process of formulating novel and potentially promising hypotheses.

Cell Line, Tumor↗

PubMed2005

Connecting the dots in Huntington's disease with protein interaction networks.

Analysis of protein-protein interaction networks is becoming important for inferring the function of uncharacterized proteins. A recent study using this approach has identified new proteins and interactions that might be involved in the pathogenesis of the neurodegenerative disorder Huntington's disease, including a GTPase-activating protein that co-localizes with protein aggregates in Huntington's disease patients.

Adaptor Proteins, Signal Transducing↗

PubMed1993

Automated linkage of free-text descriptions of patients with a practice guideline.

The process of applying a practice guideline to a patient requires a great deal of clinical data. AAPT (Appropriateness-Assessment Processing from Text) is an experimental computer program that can assess the appropriateness of coronary-artery bypass grafting surgery (CABG) in patients with coronary-artery disease (CAD) and chronic stable angina from the admission summaries of those patients. The AAPT architecture combines natural-language processing (NLP) and probabilistic inference. The NLP module identifies single clinical concepts of interest in the free-text document. The probabilistic inference module, a Bayesian belief network, estimates values for variables not specifically mentioned. AAPT produces a patient's summary of CAD that is similar to a manually generated clinical summary. Work is ongoing to improve AAPT and evaluate it as a tool to assist in the dissemination of guidelines and as a tool to encourage adherence to practice guidelines.

Angina Pectoris↗

PubMed2005

Genetic and pharmacological inactivation of adenosine A2A receptor reveals an Egr-2-mediated transcriptional regulatory network in the mouse striatum.

The adenosine A2A receptor (A2AR) is highly expressed in the striatum, where it modulates motor and emotional behaviors. We used both microarray and bioinformatics analyses to compare gene expression profiles by genetic and pharmacological inactivation of A2AR and inferred an A2AR-controlled transcription network in the mouse striatum. A comparison between vehicle (VEH)-treated A2AR knockout (KO) mice (A2AR KO-VEH) and wild-type (WT) mice (WT-VEH) revealed 36 upregulated genes that were partially mimicked by treatment with SCH-58261 (SCH; an A2AR antagonist) and 54 downregulated genes that were not mimicked by SCH treatment. We validated the A2AR as a specific drug target for SCH by comparing A2AR KO-SCH and A2AR KO-VEH groups. The unique downregulation effect of A2AR KO was confirmed by comparing A2AR KO-SCH with WT-SCH gene groups. The distinct striatal gene expression profiles induced by A2AR KO and SCH should provide clues to the molecular mechanisms underlying the different phenotypes observed after genetic and pharmacological inactivation of A2AR. Furthermore, bioinformatics analysis discovered that Egr-2 binding sites were statistically overrepresented in the proximal promoters of A2AR KO-affected genes relative to the unaffected genes. This finding was further substantiated by the demonstration that the Egr-2 mRNA level increased in the striatum of both A2AR KO and SCH-treated mice and that striatal Egr-2 binding activity in the promoters of two A2AR KO-affected genes was enhanced in A2AR KO mice as assayed by chromatin immunoprecipitation. Taken together, these results strongly support the existence of an Egr-2-directed transcriptional regulatory network controlled by striatal A2ARs.

Animals↗

PubMed2025

Detecting Interspecific Positive Selection Using Convolutional Neural Networks.

Traditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. To address these issues, we trained and tested convolutional neural network models on simulated data and achieved higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident when performing inference on noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical frameworks fail to do so in a tractable manner. We explore the generalizability of our convolutional neural network models to unseen evolutionary scenarios and identify future avenues to achieve broader utility. Once trained, our convolutional neural network model is faster at test time, making it a scalable alternative to traditional statistical methods for large-scale, multigene analyses. In addition to binary classification (inference of the presence or absence of positive selection during the evolution of the sequences), we use saliency maps to understand what the model learns and observe how this could be leveraged for sitewise inference of positive selection.

Neural Networks, Computer↗

PubMed2004

Clustering proteins from interaction networks for the prediction of cellular functions.

BACKGROUND: Developing reliable and efficient strategies allowing to infer a function to yet uncharacterized proteins based on interaction networks is of crucial interest in the current context of high-throughput data generation. In this paper, we develop a new algorithm for clustering vertices of a protein-protein interaction network using a density function, providing disjoint classes. RESULTS: Applied to the yeast interaction network, the classes obtained appear to be biological significant. The partitions are then used to make functional predictions for uncharacterized yeast proteins, using an annotation procedure that takes into account the binary interactions between proteins inside the classes. We show that this procedure is able to enhance the performances with respect to previous approaches. Finally, we propose a new annotation for 37 previously uncharacterized yeast proteins. CONCLUSION: We believe that our results represent a significant improvement for the inference of cellular functions, that can be applied to other organism as well as to other type of interaction graph, such as genetic interactions.

Cluster Analysis↗

PubMed2005

A comparison of Bayesian network learning algorithms from continuous data.

Learning a Bayesian network from data is an important problem in biomedicine for the automatic construction of decision support systems and inference of plausible causal relations. Most Bayesian network learning algorithms require discrete data; however discretization may impact the quality of the learned structure. In this project, we present a comparison of different approaches for learning from continuous data to identify the most promising one and to quantify the impact of discretization in Bayesian network learning.

Algorithms↗

PubMed1996

Analysis of diffuse parenchymal liver disease by liver scintigrams: differential diagnosis using neuro and fuzzy.

In colloid liver scintigraphy, diagnosis of diffuse parenchymal liver disease such as chronic hepatitis or cirrhosis is evaluated by size and distortion of the liver, distribution of tracer in the liver, size and activities of tracer in the spleen, visualization of the bone marrow and so on. It is not difficult to read a scintigram which shows a typical pattern of normal, chronic hepatitis and cirrhosis; however in some cases it is difficult to distinguish normal or chronic hepatitis and chronic hepatitis or cirrhosis visually. Therefore, we tried to use fuzzy inferences to perform differential diagnosis in chronic hepatitis (CH), severe fibrosis (SF) and liver cirrhosis (LC). First, five features in colloid liver scintigrams were measured or evaluated visually. These features were liver size index (left lobe/right lobe), splenomegaly, the degree of visualization of the bone marrow, liver deformity, and distribution of tracer in the liver. Having fuzziness in these data, certain characteristics of these features were considered to be fuzzy sets and thus could be expressed in membership functions. Fuzzy inference was carried out using these data and fuzzy rules. Using fuzzy inference, differential diagnosis in LC could be performed up to 100%, but those of CH and SF could not be performed sufficiently. Using neural network CH, SF and LC could be diagnosed up to 63%, 80%, and 88%, respectively. But fuzzy inference had the merit to evaluate the degree of disturbance by the center of gravity of the resulting membership function. Therefore by combining neural network and fuzzy inference, CH, SF, and LC could be differentiated to the degree 77%, 80%, and 100%, respectively.

Chronic Disease↗

PubMed1995

Extraction of fuzzy rules using neural networks with structure level adaptation and its application to diagnosis for hepatobiliary disorders.

First, this paper presents the reasoning and the learning method for fuzzy rules using structure level adaptation of neural networks. In a usual neural network's mechanism, during learning process of rules, we can observe the following two behaviors: Case 1: If a neural network does not have enough neurons to be satisfied to infer, then the input weight vector will have a tendency to fluctuate greatly, even after a certain long period the learning process. In this case, the network needs to generate a new neuron as its parent's attribute is inherited. Case 2: If a neural network has enough neurons to infer, and even if the input weight vector of each neuron will converge to a certain value, then we shall be able to turn out unnecessary neurons from the network in the calculation. In this case, because it is necessary to delete a redundant neuron to the calculation, the neuron is annihilated without affecting the performance of the network. By observing such behaviors, we can generate or annihilate the specified neuron respectively to achieve an overall good system. In the proposed method, we described a procedure to derive the neuron generation/annihilation automatically and applied the procedure to learning system. Next, we apply such procedure to the learning system in which the experimental data related to hepatobiliary disorders is used. We use a real medical database containing the results of ten biochemical terms test for four hepatobiliary disorders. We have 536 case data, including some errors. After the learning, by using 179 data chosen randomly from database, the proposed system converged to a certain small value and this constructed network has the optimal structure for these teaching data. In addition, we get that the fuzzy rules have some meanings related to the degree of the input weight vector, and the fuzzy rules for hepatobiliary disorders are extracted from the learned network with respect to the degree of input weight vector. Moreover, to verify the validity of the diagnosis of the proposed method, the feed-forward calculation was implemented using extracted fuzzy rules for all databases. As a result, the proposed system correctly diagnosed more than 70%.

Biliary Tract Diseases↗

PubMed1998

Effective learning in recurrent max-min neural networks.

Max and min operations have interesting properties that facilitate the exchange of information between the symbolic and real-valued domains. As such, neural networks that employ max-min activation functions have been a subject of interest in recent years. Since max-min functions are not strictly differentiable, we propose a mathematically sound learning method based on using Fourier convergence analysis of side-derivatives to derive a gradient descent technique for max-min error functions. We then propose a novel recurrent max-min neural network model that is trained to perform grammatical inference as an application example. Comparisons made between this model and recurrent sigmoidal neural networks show that our model not only performs better in terms of learning speed and generalization, but that its final weight configuration allows a deterministic finite automation (DFA) to be extracted in a straightforward manner. In essence, we are able to demonstrate that our proposed gradient descent technique does allow max-min neural networks to learn effectively.

Journal Article↗

PubMed2004

Learning kernels from biological networks by maximizing entropy.

MOTIVATION: The diffusion kernel is a general method for computing pairwise distances among all nodes in a graph, based on the sum of weighted paths between each pair of nodes. This technique has been used successfully, in conjunction with kernel-based learning methods, to draw inferences from several types of biological networks. RESULTS: We show that computing the diffusion kernel is equivalent to maximizing the von Neumann entropy, subject to a global constraint on the sum of the Euclidean distances between nodes. This global constraint allows for high variance in the pairwise distances. Accordingly, we propose an alternative, locally constrained diffusion kernel, and we demonstrate that the resulting kernel allows for more accurate support vector machine prediction of protein functional classifications from metabolic and protein-protein interaction networks. AVAILABILITY: Supplementary results and data are available at noble.gs.washington.edu/proj/maxent

Algorithms↗

PubMedDate not supplied

Morse code recognition system with fuzzy algorithm for disabled persons.

It is generally known that Morse code is an efficient input method for one or two switches and it is made from long and short sounds separated by silence between the sounds. The long-to-short ratio in the definition is always 3 to 1, but the long-to-short ratio variation for a disabled person is so large that it is difficult to recognize. In the last few years, several Morse code recognition methods have been successfully built on the LMS adaptive algorithms and neural network algorithm. But LMS-related adaptive algorithms need mass computation to infer the characteristic of the controller; also the neural network must learn first, by inputting some data before it is used to recognize the Morse code sequence. In this study, two fuzzy algorithms are used to recognize the unstable Morse code sequences and the result demonstrates a significant improvement of recognition for real time signal processing in a single-chip microprocessor.

Adolescent↗

PubMed1999

Model-independent mean-field theory as a local method for approximate propagation of information.

We present a systematic approach to mean-field theory (MFT) in a general probabilistic setting without assuming a particular model. The mean-field equations derived here may serve as a local, and thus very simple, method for approximate inference in probabilistic models such as Boltzmann machines or Bayesian networks. Our approach is 'model-independent' in the sense that we do not assume a particular type of dependences; in a Bayesian network, for example, we allow arbitrary tables to specify conditional dependences. In general, there are multiple solutions to the mean-field equations. We show that improved estimates can be obtained by forming a weighted mixture of the multiple mean-field solutions. Simple approximate expressions for the mixture weights are given. The general formalism derived so far is evaluated for the special case of Bayesian networks. The benefits of taking into account multiple solutions are demonstrated by using MFT for inference in a small and in a very large Bayesian network. The results are compared with the exact results.

Child↗

PubMedDate not supplied

A knowledge-based system for real-time quality control and fault diagnosis of multitest analyzers.

A PC-based real-time quality control (QC) system for multitest analyzers has been developed as a prototype. The system is built with use of a relational database management system (DBMS). Control values from the various analytical channels are stored and administrated with use of the DBMS. The control values collected during various stages of control are filtered through statistical control procedures and the control status of the instrument is continuously presented in color-coded fields indicating the possible presence of critically sized systematic or random analytical errors. The knowledge about rational trouble-shooting of a specific instrument is represented in a network structure and stored in relational tables of the DBMS. An inference engine performs alternating backward and forward reasoning in the network and guides the operator in trouble-shooting.

Artificial Intelligence↗