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 487 records · Page 27Linked to original sources

PubMed2006

Cross-species analysis of biological networks by Bayesian alignment.

Complex interactions between genes or proteins contribute a substantial part to phenotypic evolution. Here we develop an evolutionarily grounded method for the cross-species analysis of interaction networks by alignment, which maps bona fide functional relationships between genes in different organisms. Network alignment is based on a scoring function measuring mutual similarities between networks, taking into account their interaction patterns as well as sequence similarities between their nodes. High-scoring alignments and optimal alignment parameters are inferred by a systematic Bayesian analysis. We apply this method to analyze the evolution of coexpression networks between humans and mice. We find evidence for significant conservation of gene expression clusters and give network-based predictions of gene function. We discuss examples where cross-species functional relationships between genes do not concur with sequence similarity.

Algorithms↗

PubMed2006

Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities.

MOTIVATION: Quantitative estimation of the regulatory relationship between transcription factors and genes is a fundamental stepping stone when trying to develop models of cellular processes. Recent experimental high-throughput techniques, such as Chromatin Immunoprecipitation (ChIP) provide important information about the architecture of the regulatory networks in the cell. However, it is very difficult to measure the concentration levels of transcription factor proteins and determine their regulatory effect on gene transcription. It is therefore an important computational challenge to infer these quantities using gene expression data and network architecture data. RESULTS: We develop a probabilistic state space model that allows genome-wide inference of both transcription factor protein concentrations and their effect on the transcription rates of each target gene from microarray data. We use variational inference techniques to learn the model parameters and perform posterior inference of protein concentrations and regulatory strengths. The probabilistic nature of the model also means that we can associate credibility intervals to our estimates, as well as providing a tool to detect which binding events lead to significant regulation. We demonstrate our model on artificial data and on two yeast datasets in which the network structure has previously been obtained using ChIP data. Predictions from our model are consistent with the underlying biology and offer novel quantitative insights into the regulatory structure of the yeast cell. AVAILABILITY: MATLAB code is available from http://umber.sbs.man.ac.uk/resources/puma

Algorithms↗

PubMedDate not supplied

Integration of form and motion within a generative model of visual cortex.

One of the challenges faced by the visual system is integrating cues within and across processing streams for inferring scene properties and structure. This is particularly apparent in the inference of object motion, where psychophysical experiments have shown that integration of motion signals, distributed across space, must also be integrated with form cues. This has led several to conclude that there exist mechanisms which enable form cues to 'veto' or completely suppress ambiguous motion signals. We describe a probabilistic approach which uses a generative network model for integrating form and motion cues using the machinery of belief propagation and Bayesian inference. We show, using computer simulations, that motion integration can be mediated via a local, probabilistic representation of contour ownership, which we have previously termed 'direction of figure'. The uncertainty of this inferred form cue is used to modulate the covariance matrix of network nodes representing local motion estimates in the motion stream. We show with results for two sets of stimuli that the model does not completely suppress ambiguous cues, but instead integrates them in a way that is a function of their underlying uncertainty. The result is that the model can account for the continuum of bias seen for motion coherence and perceived object motion in psychophysical experiments.

Bayes Theorem↗

PubMed2026

Episode clustering in phylogenetic networks.

MOTIVATION: The classical duplication episode clustering (EC) model introduced by Guigó et al. in the 1990s provides a foundational approach for inferring genomic duplication events crucial to understanding genome evolution. This model clusters single gene duplications from a collection of gene trees at locations in the species tree to minimize the total number of such locations, called duplication episodes. However, it does not capture reticulate evolutionary histories. RESULTS: Here, we introduce NetEC, a novel extension of this problem to phylogenetic networks. To solve NetEC, we first develop a polynomial-time dynamic programming (DP) algorithm for testing whether a given set of network nodes can serve as episode locations. We then propose a main inference algorithm that utilizes this DP component to optimize the episode count; while the feasibility test runs in polynomial time, the full optimization has exponential worst-case complexity, and an optional heuristic mode is provided for larger instances. We also propose an extended episode analysis procedure that identifies additional genomic duplication candidates below reticulation nodes, complementing the main algorithm by resolving potential upward clustering of duplications induced by reticulation. We evaluate our method on simulated data and on an empirical Pandanales dataset comprising over 29 000 gene trees, demonstrating exact and accurate inference of genomic duplication events even in the presence of multiple reticulations. AVAILABILITY AND IMPLEMENTATION: All experiments were conducted using the NetEC tool (https://github.com/ppgorecki/netec), with all input data, scripts, and parameter settings for reproduction available in the same repository.

Phylogeny↗

PubMed2003

Analysis of higher-order neuronal interactions based on conditional inference.

Higher-order neural interactions, i.e., interactions that cannot be reduced to interactions between pairs of cells, have received increasing attention in the context of recent attempts to understand the cooperative dynamics in cortical neural networks. Typically, likelihood-ratio tests of log-linear models are being employed for statistical inference. The parameter estimation of these models for simultaneously recorded single-neuron spiking activities is a crucial ingredient of this approach. Extending a previous investigation of a two-neuron system, we present here the general formulation of an exact test suited for the detection of positive higher-order interactions between m neurons. This procedure does not require the estimation of any interaction parameters and additionally optimizes the test power of the statistical inference. We apply the approach to a three-neuron system and show how second-order and third-order interactions can be reliably distinguished. We study the performance of the method as a function of the interaction strength.

Action Potentials↗

PubMed1978

A measure of the denseness of a phylogenetic network.

The concept of phylogenetic denseness bears critically on the accuracy of evolutionary pathways inferred from experimentally sequenced proteins isolated from extant species. In this paper I develop an objective measure, rho, of denseness to supplement previous intuitive concepts and which permits one to use this concept in comparing the quality of different evolutionary reconstructions. This measure is used to examine several published phylogenetic trees: insulin, alpha-hemoglobin, beta-hemoglobin, myoglobin, cytochrome c, and the parvalbumin family. The paper emphasizes 1) the importance of denseness in accurately estimating the number of nucleotide replacements which separate homologous sequences when this estimation is made by the method of parsimony, 2) the value of this concept in assessing the quality of those estimates, and 3) the use of this concept as a biologically practical heuristic method for identifying poorly studied regions in a phylogenetic tree, whether or not the tree was obtained by the parsimony method.

Biological Evolution↗

PubMed1991

[Laboratory interpretive reporting system. A prototypical approach for knowledge representation in acid-base and electrolyte disorders].

It is important to realize the problem-oriented diagnosis and the goal-oriented one to support general physicians as clinical decision makers under so many laboratory tests more than full with using practically. A prototypical model for computer-assisted interpretation of laboratory data was presented. To achieve both function of the problem-oriented inference and the goal-oriented one, knowledge base in this system was structured using the methods of semantic network and qualitative reasoning. Superficial knowledge level characterized by the intuitive reasoning from test results to diseases and deep knowledge level referring to the relations among the pathophysiologic states were stratified, and the heuristics based on the experiences at bed-side were incorporated in this knowledge representation. The pathophysiologic states were distilled from the textbooks and the articles, and were linked together by the relationships of cause and result, such as functional, constraint or conceptual relationship. Diagnostic knowledge on the computer was reconstructed by the causal network representing the pathological process as qualitative chaining between the pathophysiologic states, and was corrected and compiled by the clinical experiences and the experimental facts. Abnormal test data became a trigger of the start of inference, and presumptive pathophysiologic states and diagnoses were fired on the causal network. Consequently, the most important pathophysiologic states might become to be clear to understand the cause of changes of laboratory data.

Acid-Base Imbalance↗

PubMed2025

Adversarial attack of sequence-free enhancer prediction identifies chromatin architecture.

MOTIVATION: The wide range of cellular complexity created by multicellular organisms is due in large part to the intricate and synergistic interplay of regulatory complexes throughout the eukaryotic genome. These regulatory elements "enhance" specific gene programs and have been shown to operate in diverse networks that are distinct across cell states of the same organism. Attempts to characterize and predict enhancers have typically focused on leveraging information-dense DNA sequence in parallel with epigenomic assays. We examined the viability of enhancer prediction using only a minimal set of epigenomic datasets without direct DNA information. RESULTS: We demonstrate that chromatin datasets are sufficient to identify enhancers genome-wide with high accuracy. By training networks leveraging data from multiple cell types simultaneously, we generated a cell-type invariant enhancer prediction platform that utilized only the patterns of protein binding for inference. We also showed the utility of swarm-based adversarial attacks [adversarial particle swarm optimization (APSO)] to deconvolute trained genomic neural networks for the first time. Critically, unlike saliency mapping or other game-theory based approaches, APSO is completely network-architecture independent and can be applied to any prediction engine to derive the features that drive inference. AVAILABILITY AND IMPLEMENTATION: All software and code for data downloading, processing, enhancer inference, eXplainable AI (XAI), and complete figure generation are publicly available on GitHub at https://github.com/EpiGenomicsCode/ChromEnhancer and Zenodo at https://doi.org/10.5281/zenodo.15652797.

Enhancer Elements, Genetic↗

PubMed2004

Modeling infection transmission.

Understanding what determines patterns of infection spread in populations is important for controlling infection transmission. The science that advances this understanding uses mathematical and computer models that vary from deterministic models of continuous populations to models of dynamically evolving contact networks between individuals. These provide insight, serve as scientific theories, help design studies, and help analyze data. The key to their use lies in assessing the robustness of inferences made using them to violation of their simplifying assumptions. This involves changing model forms from deterministic to stochastic and from compartmental to network, as well as adding realistic detail and changing parameter values. Currently inferences about infection transmission are often made using stratified rate or risk comparisons, logistic regression models, or proportionate hazards models that assume an absence of transmission. Robustness assessment will show many of these inferences to be wrong. A community of epidemiologist modelers is needed for effective robustness assessment.

Data Interpretation, Statistical↗

PubMed2005

Beyond hypofrontality: a quantitative meta-analysis of functional neuroimaging studies of working memory in schizophrenia.

Although there is considerable evidence that patients with schizophrenia fail to activate the dorsolateral prefrontal cortex (DLPFC) to the degree seen in normal comparison subjects when performing working memory or executive tasks, hypofrontality may be coupled with relatively increased activity in other brain regions. However, most imaging studies of working memory in schizophrenia have focused on DLPFC activity. The goal of this work is to review functional neuroimaging studies that contrasted patients with schizophrenia and healthy comparison subjects during a prototypical working memory task, the n-back paradigm, to highlight areas of hyper- and hypoactivation in schizophrenia. We utilize a quantitative meta-analysis method to review 12 imaging studies where patients with schizophrenia were contrasted with healthy comparison subjects while performing the n-back paradigm. Although we find clear support for hypofrontality, we also document consistently increased activation in anterior cingulate and left frontal pole regions in patients with schizophrenia compared to that in controls. These data suggest that whereas reduced DLPFC activation is reported consistently in patients with schizophrenia relative to healthy subjects, abnormal activation patterns are not restricted to this region, raising questions as to whether the pathophysiological dysfunction in schizophrenia is specific to the DLPFC and about the relationship between impaired performance and aberrant activation patterns. The complex pattern of hyper- and hypoactivation consistently found across studies implies that rather than focusing on DLPFC dysregulation, researchers should consider the entire network of regions involved in a given task when making inferences about the biological mechanisms of schizophrenia.

Brain↗

PubMed2003

Molecular dynamics simulations of enhanced green fluorescent proteins: effects of F64L, S65T and T203Y mutations on the ground-state proton equilibria.

Molecular dynamics simulations with the Amber force field are carried out to study two mutants of the green fluorescent protein (GFP), namely EGFP (F64L/S65T) and T203Y-EGFP (E(2)GFP). Those variants display an opposite equilibrium between the structural A and B states, associated with neutral and anionic protonation forms of the chromophore. Configurations of those two states are simulated for each variant and the energetics of their equilibrium in the two mutants is studied by evaluating the change in the relative free energy of A and B states (DeltaG(AB)) upon T203Y mutation. The resulting DeltaDeltaG(AB) agrees with the value inferred from absorption measurements. A comparison of the hydrogen bond network around the chromophore rationalizes the different population of state A and B in EGFP and E(2)GFP. On the basis of structural and energetic considerations, a mechanism for destabilization of the neutral chromophore in S65T mutants is proposed. Simulations of the B state of the S65T variant and of WT GFP are also performed for comparison and to test the force field parameters of the chromophore derived for the present calculations. Possible paths of proton transfer leading to nonfluorescent states of the chromophore are discussed in light of the photodynamical behavior of GFP, as revealed by fluorescence correlation spectroscopy and single-molecule experiments.

Algorithms↗

PubMed2004

The neural correlates of theory of mind within interpersonal interactions.

Tasks that engage a theory of mind seem to activate a consistent set of brain areas. In this study, we sought to determine whether two different interactive tasks, both of which involve receiving consequential feedback from social partners that can be used to infer intent, similarly engaged the putative theory of mind neural network. Participants were scanned using fMRI as they played the Ultimatum Game (UG) and the Prisoner's Dilemma Game (PDG) with both alleged human and computer partners who were outside the scanner. We observed a remarkable degree of overlap in brain areas that activated to partner decisions in the two games, including commonly observed theory of mind areas, as well as several brain areas that have not been reported previously and may relate to immersion of participants in real social interactions that have personally meaningful consequences. Although computer partners elicited activation in some of the same areas activated by human partners, most of these activations were stronger for human partners.

Adult↗

PubMed2000

Origin and evolution of primate social organisation: a reconstruction.

The evolution and origin of primate social organisation has attracted the attention of many researchers, and a solitary pattern, believed to be present in most nocturnal prosimians, has been generally considered as the most primitive system. Nocturnal prosimians are in fact mostly seen alone during their nightly activities and therefore termed 'solitary foragers', but that does not mean that they are not social. Moreover, designating their social organisation as 'solitary', implies that their way of life is uniform in all species. It has, however, emerged over the last decades that all of them exhibit not only some kind of social network but also that those networks differ among species. There is a need to classify these social networks in the same manner as with group-living (gregarious) animals if we wish to link up the different forms of primate social organisation with ecological, morphological or phylogenetic variables. In this review, we establish a basic classification based on spatial relations and sociality in order to describe and cope properly with the social organisation patterns of the different species of nocturnal prosimians and other mammals that do not forage in cohesive groups. In attempting to trace the ancestral pattern of primate social organisation, the Malagasy mouse and dwarf lemurs and the Afro-Asian bushbabies and lorises are of special interest because they are thought to approach the ancestral conditions most closely. These species have generally been believed to exhibit a dispersed harem system as their pattern of social organisation ('dispersed' means that individuals forage solitarily but exhibit a social network). Therefore, the ancestral pattern of primate social organisation was inferred to be a dispersed harem. In fact, new field data on cheirogaleids combined with a review of patterns of social organisation in strepsirhines (lemurs, bushbabies and lorises) revealed that they exhibit either dispersed multi-male systems or dispersed monogamy rather than a dispersed harem system. Therefore, the concept of a dispersed harem system as the ancestral condition of primate social organisation can no longer be supported. In combination with data on social organisation patterns in 'primitive' placentals and marsupials, and in monotremes, it is in fact most probable that promiscuity is the ancestral pattern for mammalian social organisation. Subsequently, a dispersed multi-male system derived from promiscuity should be regarded as the ancestral condition for primates. We further suggest that the gregarious patterns of social organisation in Aotus and Avahi, and the dispersed form in Tarsius evolved from the gregarious patterns of diurnal primates rather than from the dispersed nocturnal type. It is consequently proposed that, in addition to Aotus and Tarsius, Avahi is also secondarily nocturnal.

Animals↗

PubMed2005

Frontoparietal control of spatial attention and motor intention in human EEG.

Relations between spatial attention and motor intention were investigated by means of an EEG potential elicited by shifting attention to a location in space as well as by the selection of a hand for responding. High-density recordings traced this potential to a common frontoparietal network activated by attentional orienting and by response selection. Within this network, parietal and frontal cortex were activated sequentially, followed by an anterior-to-posterior migration of activity culminating in the lateral occipital cortex. Based on temporal and polarity information provided by EEG, we hypothesize that the frontoparietal activation, evoked by directional information, updates a task-defined preparatory state by deselecting or inhibiting the behavioral option competing with the cued response side or the cued direction of attention. These results from human EEG demonstrate a direct EEG manifestation of the frontoparietal attention network previously identified in functional imaging. EEG reveals the time-course of activation within this network and elucidates the generation and function of associated directing-attention EEG potentials. The results emphasize transient activation and a decision-related function of the frontoparietal attention network, contrasting with the sustained preparatory activation that is commonly inferred from neuroimaging.

Adult↗

PubMed2004

Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data.

BACKGROUND: Functional genomics studies are yielding information about regulatory processes in the cell at an unprecedented scale. In the yeast S. cerevisiae, DNA microarrays have not only been used to measure the mRNA abundance for all genes under a variety of conditions but also to determine the occupancy of all promoter regions by a large number of transcription factors. The challenge is to extract useful information about the global regulatory network from these data. RESULTS: We present MA-Networker, an algorithm that combines microarray data for mRNA expression and transcription factor occupancy to define the regulatory network of the cell. Multivariate regression analysis is used to infer the activity of each transcription factor, and the correlation across different conditions between this activity and the mRNA expression of a gene is interpreted as regulatory coupling strength. Applying our method to S. cerevisiae, we find that, on average, 58% of the genes whose promoter region is bound by a transcription factor are true regulatory targets. These results are validated by an analysis of enrichment for functional annotation, response for transcription factor deletion, and over-representation of cis-regulatory motifs. We are able to assign directionality to transcription factors that control divergently transcribed genes sharing the same promoter region. Finally, we identify an intrinsic limitation of transcription factor deletion experiments related to the combinatorial nature of transcriptional control, to which our approach provides an alternative. CONCLUSION: Our reliable classification of ChIP positives into functional and non-functional TF targets based on their expression pattern across a wide range of conditions provides a starting point for identifying the unknown sequence features in non-coding DNA that directly or indirectly determine the context dependence of transcription factor action. Complete analysis results are available for browsing or download at http://bussemaker.bio.columbia.edu/papers/MA-Networker/.

Algorithms↗

PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗

PubMed2002

A graph-theoretic method to identify candidate mechanisms for deriving the rate law of a catalytic reaction.

Stoichiometrically, exact candidate pathways or mechanisms for deriving the rate law of a catalytic or complex reaction can be determined through the synthesis of networks of plausible elementary reactions constituting such pathways. A rigorous algorithmic method is proposed for executing this synthesis, which is exceedingly convoluted due to its combinatorial complexity. Such a method for synthesizing networks of reaction pathways follows the general framework of a highly exacting combinatorial method established by us for process-network synthesis. It is based on the unique graph-representation in terms of P-graphs, a set of axioms, and a group of combinatorial algorithms. In the method, the inclusion or exclusion of a step of each elementary reaction in the mechanism of concern hinges on the general combinatorial properties of feasible reaction networks. The decisions are facilitated by solving linear programming problems comprising a set of mass-balance constraints to determine the existence or absence of any feasible solution. The search is accelerated further by exploiting the inferences of preceding decisions, thereby eliminating redundancy. As a result, all feasible independent reaction networks, i.e. pathways, are generated only once; the pathways violating any first principle of either stoichiometry or thermodynamics are eliminated. The method is also capable of generating those combinations of independent pathways directly, which are not microscopically reversible. The efficiency and efficacy of the method are demonstrated with the identification of the feasible mechanisms of ammonia synthesis involving as many as 14 known elementary reactions.

Journal Article↗

PubMed2005

Motif-based protein ranking by network propagation.

MOTIVATION: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used pairwise sequence comparison algorithms for homology detection, such as BLAST and PSI-BLAST, often fail to detect less conserved remotely-related targets. RESULTS: In this paper, we propose a new general graph-based propagation algorithm called MotifProp to detect more subtle similarity relationships than pairwise comparison methods. MotifProp is based on a protein-motif network, in which edges connect proteins and the k-mer based motif features that they contain. We show that our new motif-based propagation algorithm can improve the ranking results over a base algorithm, such as PSI-BLAST, that is used to initialize the ranking. Despite the complex structure of the protein-motif network, MotifProp can be easily interpreted using the top-ranked motifs and motif-rich regions induced by the propagation, both of which are helpful for discovering conserved structural components in remote homologies.

Algorithms↗