SEARCH · PubMed Health

Results for “Network inference”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26Linked to original sources

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g., associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Journal Article↗

PubMed2025

MPAC: a computational framework for inferring pathway activities from multi-omic data.

MOTIVATION: Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. RESULTS: We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC leverages network relationships encoded in pathways through a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to allow identifying and prioritizing proteins with potential clinical relevance, e.g. associated with patient prognosis. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell composition. Our MPAC R package enables similar multi-omic analyses on new datasets. AVAILABILITY AND IMPLEMENTATION: The MPAC package is available at Bioconductor https://bioconductor.org/packages/MPAC.

Humans↗

PubMed2005

Algorithms for protein interaction networks.

The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.

Algorithms↗

PubMed2005

Interaction network containing conserved and essential protein complexes in Escherichia coli.

Proteins often function as components of multi-subunit complexes. Despite its long history as a model organism, no large-scale analysis of protein complexes in Escherichia coli has yet been reported. To this end, we have targeted DNA cassettes into the E. coli chromosome to create carboxy-terminal, affinity-tagged alleles of 1,000 open reading frames (approximately 23% of the genome). A total of 857 proteins, including 198 of the most highly conserved, soluble non-ribosomal proteins essential in at least one bacterial species, were tagged successfully, whereas 648 could be purified to homogeneity and their interacting protein partners identified by mass spectrometry. An interaction network of protein complexes involved in diverse biological processes was uncovered and validated by sequential rounds of tagging and purification. This network includes many new interactions as well as interactions predicted based solely on genomic inference or limited phenotypic data. This study provides insight into the function of previously uncharacterized bacterial proteins and the overall topology of a microbial interaction network, the core components of which are broadly conserved across Prokaryota.

Alleles↗

PubMed2005

Revisiting "scale-free" networks.

Recent observations of power-law distributions in the connectivity of complex networks came as a big surprise to researchers steeped in the tradition of random networks. Even more surprising was the discovery that power-law distributions also characterize many biological and social networks. Many attributed a deep significance to this fact, inferring a "universal architecture" of complex systems. Closer examination, however, challenges the assumptions that (1) such distributions are special and (2) they signify a common architecture, independent of the system's specifics. The real surprise, if any, is that power-law distributions are easy to generate, and by a variety of mechanisms. The architecture that results is not universal, but particular; it is determined by the actual constraints on the system in question.

Algorithms↗

PubMed2004

Detection of functional modules from protein interaction networks.

Complex cellular processes are modular and are accomplished by the concerted action of functional modules (Ravasz et al., Science 2002;297:1551-1555; Hartwell et al., Nature 1999;402:C47-52). These modules encompass groups of genes or proteins involved in common elementary biological functions. One important and largely unsolved goal of functional genomics is the identification of functional modules from genomewide information, such as transcription profiles or protein interactions. To cope with the ever-increasing volume and complexity of protein interaction data (Bader et al., Nucleic Acids Res 2001;29:242-245; Xenarios et al., Nucleic Acids Res 2002;30:303-305), new automated approaches for pattern discovery in these densely connected interaction networks are required (Ravasz et al., Science 2002;297:1551-1555; Bader and Hogue, Nat Biotechnol 2002;20:991-997; Snel et al., Proc Natl Acad Sci USA 2002;99:5890-5895). In this study, we successfully isolate 1046 functional modules from the known protein interaction network of Saccharomyces cerevisiae involving 8046 individual pair-wise interactions by using an entirely automated and unsupervised graph clustering algorithm. This systems biology approach is able to detect many well-known protein complexes or biological processes, without reference to any additional information. We use an extensive statistical validation procedure to establish the biological significance of the detected modules and explore this complex, hierarchical network of modular interactions from which pathways can be inferred.

Algorithms↗

PubMed1997

Oxidized low-density lipoproteins affect natural killer cell activity by impairing cytoskeleton function and altering the cytokine network.

Several lines of evidence indicate that oxidative imbalance can play an important role in determining an impairment of natural killer (NK) cell activity in a variety of human diseases. Because a specific role for oxidized low-density lipoproteins (LDL) as pro-oxidizing agents has been envisaged, we tested the activity of oxidized LDL (ox-LDL) on NK cell-mediated cytotoxicity, cytokine release, and membrane molecule modulation. Native LDL served as control. Treatment with ox-LDL at noncytotoxic concentrations (0.2 mg/ml) during the NK/target cell (TC) interaction markedly reduced NK cytotoxic activity against U937 tumor cells. This inhibitory activity was also noticed when NK cells were pretreated with ox-LDL. Scanning electron microscopy examination of NK-target cell conjugates failed to reveal any morphological cell damage. In addition, the number of conjugates and the expression of some adhesion molecules (CD11a, CD11b, CD18, CD2, and CD62L) were not modified by ox-LDL. These observations argued against a possible interference of ox-LDL with the binding process leading to the formation of NK/TC conjugates. By contrast, immunocytochemical analyses of cytoskeleton components of NK cells exposed to ox-LDL showed a partial depolymerization and a derangement of the microtubular apparatus. These alterations were accompanied by an evident decrease in their intracellular reduced glutathione content. Owing to the important role played by the microtubular network during the killing process, it is possible to infer that a cytoskeleton alteration underlies the inhibitory activity of ox-LDL on NK cell function. In addition, exposure of mitogen-stimulated peripheral blood mononuclear cells to ox-LDL markedly reduced specific mRNA transcription and release of cytokines relevant for NK cell activity (such as tumor necrosis factor-alpha, interferon gamma, and interleukin 12). These data suggest that the impairment of NK cell activity by ox-LDL likely reflects the concomitant dysregulation of some essential mechanisms of NK cell function.

Antigens, CD↗

PubMed1986

A diagnostic method that uses causal knowledge and linear programming in the application of Bayes' formula.

Bayes' formula has been applied extensively in computer-based medical diagnostic systems. One assumption that is often made in the application of the formula is that the findings in a case are conditionally independent. This assumption is often invalid and leads to inaccurate posterior probability assignments to the diagnostic hypotheses. This paper discusses a method for using causal knowledge to structure findings according to their probabilistic dependencies. An inference procedure is discussed which propagates probabilities within a network of causally related findings in order to calculate posterior probabilities of diagnostic hypotheses. A linear programming technique is described that bounds the values of the propagated probabilities subject to known probabilistic constraints.

Bayes Theorem↗

PubMed2006

Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system.

This study presents the development of a hybrid system consisting of an ensemble of Extended Kalman Filter (EKF) based Multi Layer Perceptron Network (MLPN) and a one-pass learning Fuzzy Inference System using Look-up Table Scheme for the recognition of electrocardiogram (ECG) signals. This system can distinguish various types of abnormal ECG signals such as Ventricular Premature Cycle (VPC), T wave inversion (TINV), ST segment depression (STDP), and Supraventricular Tachycardia (SVT) from normal sinus rhythm (NSR) ECG signal.

Algorithms↗

PubMed2001

A statistical assessment of changes in precipitation chemistry at CAPMoN sites, Canada. Canadian Air and Precipitation Monitoring Network.

The objective of this study is to decide if chemical concentrations measured during 1988-1997 in precipitation by the Canadian Air and Precipitation Monitoring Network (CAPMoN) indicate changes in the annual patterns. The inference is based on SO4, NO3, Cl, NH4, Na, Ca and K concentrations monitored daily with the rain water acidity pH. By pattern is understood the probability distribution of the annual sample at a particular location. Most of the annual data can be well described by means of a linear regression model with second order polynomial trend and autocorrelated noise. Statistical analysis based on the model shows luck of systematic significant year to year increases or declines of the concentrations.

Air Pollutants↗

PubMed2005

Tumour matrilysin expression predicts metastatic potential of stage I (pT1) colon and rectal cancers.

BACKGROUND AND AIMS: Nodal metastases are indisputable determinants of prognosis for colon and rectal cancer. Using classical histological criteria, many attempts to predict nodal metastasis have failed, preventing the adequate management of stage I (pT1) cancer. We investigated the role of tumour matrilysin in predicting metastatic potential, and discuss its potential use in individualising treatment of pT1 colon and rectal cancer. METHODS: The gene signature associated with nodal metastasis was investigated by cDNA array in 24 colon and rectal cancers. We studied 494 colon and rectal cancer patients to identify risk factors for nodal metastasis and evaluated the potential to predict nodal metastasis by either the logistic regression model or the Bayesian neural network model with built-in matrilysin. We then inferred possible causality of nodal metastasis from structural equation modelling. RESULTS: cDNA array revealed that matrilysin was maximally upregulated in the metastasis signature identified. Tumour matrilysin expression emerged as a stage independent risk factor for nodal metastasis, resulting in a similar predictive performance in receiver operating characteristic curve analysis in the two models. A Bayesian approach called automatic relevance determination identified matrilysin as one of the most relevant predictors examined. Structural equation modelling suggested possible direct causality between matrilysin and nodal metastasis. CONCLUSIONS: We have provided evidence that tumour matrilysin expression is a promising biomarker predicting nodal metastasis of colon and rectal cancer. Analysis of tumour matrilysin expression would help clinicians achieve the goal of individualised cancer treatment based on the metastatic potential of pT1 colon and rectal cancer.

Adenocarcinoma↗

PubMed2005

Adaptive diagnosis in distributed systems.

Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, we propose a cost-efficient, adaptive diagnostic technique called active probing. Probes are end-to-end test transactions that collect information about the performance of a distributed system. Active probing uses probabilistic reasoning techniques combined with information-theoretic approach, and allows a fast online inference about the current system state via active selection of only a small number of most-informative tests. We demonstrate empirically that the active probing scheme greatly reduces both the number of probes (from 60% to 75% in most of our real-life applications), and the time needed for localizing the problem when compared with nonadaptive (preplanned) probing schemes. We also provide some theoretical results on the complexity of probe selection, and the effect of "noisy" probes on the accuracy of diagnosis. Finally, we discuss how to model the system's dynamics using dynamic Bayesian networks (DBNs), and an efficient approximate approach called sequential multifault; empirical results demonstrate clear advantage of such approaches over "static" techniques that do not handle system's changes.

Algorithms↗

PubMed2004

Inferring models of gene expression dynamics.

We study the problem of identifying genetic networks in which expression dynamics are modeled by a differential equation that uses logical rules to specify time derivatives. We make three main contributions. First, we describe computationally efficient procedures for identifying the structure and dynamics of such networks from expression time series. Second, we derive predictions for the expected amount of data needed to identify randomly generated networks. Third, if expression values are available for only some of the genes, we show that the structure of the network for these "visible" genes can be identified and that the size and overall complexity of the network can be estimated. We validate these procedures and predictions using simulation experiments based on randomly generated networks with up to 30,000 genes and 17 distinct regulators per gene and on a network that models floral morphogenesis in Arabidopsis thaliana.

Arabidopsis↗

PubMed2005

Cross talking of network motifs in gene regulation that generates temporal pulses and spatial stripes.

Gene regulatory networks contain several substructures called network motifs, which frequently exist throughout the networks. One of such motifs found in Escherichia coli, Saccharomyces cerevisiae, and Drosophila melanogaster is the feed-forward loop, in which an effector regulates its target by a direct regulatory interaction and an indirect interaction mediated by another gene product. Here, we theoretically analyze the behavior of networks that contain feed-forward loops cross talking to each other. In response to levels of the effecter, such networks can generate multiple rise-and-fall temporal expression profiles and spatial stripes, which are typically observed in developmental processes. The mechanism to generate these responses reveals the way of inferring the regulatory pathways from experimental results. Our database study of gene regulatory networks indicates that most feed-forward loops actually cross talk. We discuss how the feed-forward loops and their cross talks can play important roles in morphogenesis.

Animals↗

PubMed2002

Interpreting small quantities of DNA: the hierarchy of propositions and the use of Bayesian networks.

The dramatic increase in the sensitivity of DNA profiling systems that has occurred over recent years has led to the need to address a wider range of interpretational problems in forensic science. The issues surrounding questions of the kind "whose DNA is this?" have been the subject of considerable controversy but now it is clear that the emphasis is shifting to questions of the kind "how did this DNA get here?" Such issues are discussed in this paper and new insights are provided by two particular recent developments. First, the notion of the "hierarchy of propositions" that has arisen from a project called Case Assessment and Interpretation (CAI) that has been running in the British Forensic Science Service (FSS). Second, a technique for drawing inferences in the face of many interacting considerations, known as "Bayesian networks"--or "Bayes' nets" for short--that has been the subject of an earlier paper in this journal (1). The discussion is carried out by means of case studies, based on actual cases. It is clear that, whereas the inference in relation to the source of the DNA in a crime sample might be overwhelmingly strong, the inference in relation to the propositions that a jury must consider relating to the identity of the actual offender may be much more tentative.

Bayes Theorem↗

PubMed1985

A model for signal transmission in an ear having hair cells with free-standing stereocilia. II. Macromechanical stage.

A model of the signal-processing properties of the macromechanical system of the alligator-lizard middle and inner ear is developed. The model is based on measurements (as a function of tone frequency) of ossicular and basilar-membrane motion and of acoustic input-admittance at the tympanic membrane before and after alterations of middle-ear and inner-ear structures. From the structure of the ear, we formulate an equivalent electric network consisting of six admittance blocks and two transformers. The admittance blocks represent the mechanical properties of particular structures, e.g. the tympanic membrane (TM), the ossicle, the TM-ossicular joint, the vestibule, the basilar membrane and the helicotrema. The transformers represent the conversions from acoustical to mechanical variables performed by the TM-ossicular system and by the columella footplate. The block admittances and transformer ratios are inferred from the measurements, and each admittance is approximated by a network with a few simple elements. The resulting model fits the experimental results satisfactorily, allows correlations between specific structures and response behavior, and predicts the macromechanical transfer function. This transfer function acts as the first stage of a model of the alligator-lizard auditory periphery (Weiss et al., Weiss and Leong, 1985, Hearing Res. 20, 131-138, 157-174, 175-195). In the model the frequency dependence of basilar-membrane motion (1) is determined primarily by the tympanic membrane and extracolumella in the middle frequencies and (2) is affected by the helicotrema at low frequencies and the TM-ossicular joint at high frequencies.

Acoustics↗

PubMed2003

Suitability and utility of computational analysis tools: characterization of erythrocyte parameter variation.

Systems engineering can provide insights into multivariate regulatory networks and pooling in complex biological networks that cannot be fully interpreted through experiments alone. Herein, we analyzed the use of phase planes, modal and time-lagged correlation (TLC) analyses of the human erythrocyte to explore the utility of these techniques for understanding the effect of single parameter changes on the behavior of a metabolic network. Specifically, several parameters in key regulatory steps in erythrocyte glycolysis, Rapoport-Leubering bypass, pentose phosphate pathway, adenosine metabolism, and membrane transport were perturbed. The most sensitive parameters were identified based on the steady-state metabolite concentration changes and were explored further. Modal analysis identified relevant time scales for each parameter change. These time scales were further explored using phase plane and TLC analyses. Phase plane and TLC both inferred pooling changes, while TLC also identified changes in the regulatory network structure that resulted from various parameter changes. Each method has strengths and weaknesses for exploring and gaining insight into complex biological networks.

Adenosine↗

PubMed2001

A method for diagnosing multiple diseases in MUNIN.

A new method for diagnosing multiple diseases in large medical decision support systems based on causal probabilistic networks is proposed. The method is based on characteristics of the diagnostic process that we believe to be present in many diagnostic tasks, both inside and outside medicine. The diagnosis must often be made under uncertainty, choosing between diagnoses that each have small prior probabilities, but not so small that the possibility of two or more simultaneous diseases can be ignored. Often a symptom can be caused by several diseases and the presence of several diseases tend to aggravate the symptoms. For diagnostic problems that share these characteristic, we have proposed a method that operates in a number of phases: in the first phase only single diseases are considered and this helps to focus the attention on a smaller number of plausible diseases. In the second phase, pairs of diseases are considered, which make it possible to narrow down the field of plausible diagnoses further. In the following phases, larger subsets of diseases are considered. The method was applied to the diagnosis of neuromuscular disorders, using previous experience with the so-called MUNIN system as a starting point. The results showed that the method gave large reductions in computation time without compromising the computational accuracy in any substantial way. It is concluded that the method enables practical inference in large medical expert systems based on causal probabilistic networks.

Decision Trees↗