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 613 records · Page 34Linked to original sources

PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗

PubMed1990

Application of a network structure in a knowledge-based system for medical diagnostics.

Systems supporting diagnostic decision making have been designed more often on the base of production rules than on semantic nets. One reason for that might be that concepts to process information on the latter structures are still rare. In this study the application of a semantic net is described. Via creation of various views, different sets of information are defined, depending on their meaning in the semantic net. Projections, which are in general a combination of mappings and set operations, are used to infer a set of information from another. The developed system was written in Pascal, the semantic net was implemented using a network-like pointer structure. The concept has been used to develop PROJECTOR, an expert system for differential diagnosis based on a semantic net. The structure has turned out to be fast and compact enough to allow large amounts of data to be handled easily even with a personal computer.

Diagnosis, Computer-Assisted↗

PubMed1994

Integration of quantitative and qualitative reasoning: an expert system for cardiosurgical patients.

In this work the possibility of building an expert system to reason on the status of post-operative cardiac patients in intensive care units is analysed. The long-term knowledge consists of causal network which describes the main relationships between hemodynamic and metabolic quantities involved in the evolution after cardiac surgery. The inference engine uses an original hybrid formalism, which integrates numerical simulation and qualitative methods. If available, the numerical values of quantities and their exact mathematical relationships are employed; otherwise, the inference engine reasons by using a discrete qualitative representation of quantities. Simulations performed using real data indicate that integration of quantitative and qualitative methods reduces the number of diagnostic scenarios compatible with patient data, and constitutes a valid tool for reasoning about physiological disorders in terms of deep causal knowledge.

Atrial Function, Right↗

PubMed2001

Experimental observations of a nuclear matrix.

Nuclei are intricately structured, and nuclear metabolism has an elaborate spatial organization. The architecture of the nucleus includes two overlapping and nucleic-acid-containing structures - chromatin and a nuclear matrix. The nuclear matrix is observed by microscopy in live, fixed and extracted cells. Its ultrastructure and composition show it to be, in large part, the ribonucleoprotein (RNP) network first seen in unfractionated cells more than 30 years ago. At that time, the discovery of this RNP structure explained surprising observations that RNA, packaged in proteins, is attached to an intranuclear, non-chromatin structure. Periodic and specific attachments of chromatin fibers to the nuclear matrix create the chromatin loop domains that can be directly observed by microscopy or inferred from biochemical experiments. The ultrastructure of the nuclear matrix is well characterized and consists of a nuclear lamina and an internal nuclear network of subassemblies linked together by highly structured fibers. These complex fibers are built on an underlying scaffolding of branched 10-nm filaments that connect to the nuclear lamina. The structural proteins of the nuclear lamina have been well characterized, but the structural biochemistry of the internal nuclear matrix has received less attention. Many internal matrix proteins have been identified, but far less is known about how these proteins assemble to make the fibers, filaments and other assemblies of the internal nuclear matrix. Correcting this imbalance will require the combined application of biochemistry and electron microscopy. The central problem in trying to define nuclear matrix structure is to identify the proteins that assemble into the 10-nm filaments upon which the interior architecture of the nucleus is constructed. Only by achieving a biochemical characterization of the nuclear matrix will we advance beyond simple microscopic observations of structure to a better understanding of nuclear matrix function, regulation and post-mitotic assembly.

Animals↗

PubMed2026

ceRNA network of lncRNAs and mRNAs in OSF-to-OSCC progression: Diagnostic biomarkers and functional pathways.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic potentially malignant disorder that can progress to oral squamous cell carcinoma (OSCC). Although dysregulated non-coding RNAs have been implicated in oral carcinogenesis, the competing endogenous RNA (ceRNA)-mediated regulatory mechanisms underlying OSF-to-OSCC progression remain poorly understood. This study aimed to identify candidate regulatory molecules and construct a putative lncRNA-miRNA-mRNA network associated with malignant transformation. METHODS: Publicly available microarray datasets (GSE117973 and GSE125866) were analyzed to identify differentially expressed genes between OSF and OSCC. Differentially expressed transcripts were classified into mRNAs and lncRNAs based on public transcript annotations. Highly correlated lncRNA-mRNA pairs were identified using Pearson correlation analysis and integrated with multiMiR-supported miRNA-mRNA interactions obtained from public databases to construct a putative ceRNA regulatory network. Functional characterization focused on apoptosis, epithelial-mesenchymal transition (EMT), and immune checkpoint-related pathways. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance, and selected biomarkers were externally validated using The Cancer Genome Atlas (TCGA) OSCC cohort. RESULTS: Integrated transcriptomic analysis identified several dysregulated mRNAs and lncRNAs associated with OSF-to-OSCC progression. Network analysis highlighted TBC1D3B, RREB1, TEAD3, SREBF1, TMEM41B, FOXK2, and KIAA1958 as prominent hub genes within the putative regulatory network. Functional analyses demonstrated significant associations with apoptosis-, EMT-, and immune checkpoint-related genes, suggesting potential involvement in multiple biological processes contributing to malignant transformation. Several hub genes exhibited strong diagnostic performance, with ROC analysis yielding AUC values ranging from 0.891 to 1.000, indicating excellent discrimination between OSF and OSCC samples. External validation using TCGA further supported the relevance of the identified biomarkers in OSCC. CONCLUSIONS: This study provides a comprehensive transcriptomic framework describing putative lncRNA-miRNA-mRNA regulatory interactions associated with OSF progression to OSCC. The identified hub genes and regulatory networks represent candidate biomarkers for early detection and provide a foundation for future mechanistic and experimental validation. As the proposed ceRNA interactions are computationally inferred, further biological validation is required before clinical application.

RNA, Long Noncoding↗

PubMed1998

Medullary raphe neurones and baroreceptor modulation of the respiratory motor pattern in the cat.

1. Perturbations of arterial blood pressure change medullary raphe neurone activity and the respiratory motor pattern. This study sought evidence for actions of baroresponsive raphe neurones on the medullary respiratory network. 2. Blood pressure was perturbed by intravenous injection of an alpha1-adrenergic receptor agonist, unilateral pressure changes in the carotid sinus, or occlusion of the descending aorta in thirty-six Dial-urethane-anaesthetized, vagotomized, paralysed, artificially ventilated cats. Neurones were monitored with microelectrode arrays in two or three of the following domains: nucleus raphe obscurus-nucleus raphe pallidus, nucleus raphe magnus, and rostral and caudal ventrolateral medulla. Data were analysed with cycle-triggered histograms, peristimulus time and cumulative sum histograms, cross-correlograms and spike-triggered averages of efferent phrenic nerve activity. 3. Prolongation of the expiratory phase and decreased peak integrated phrenic amplitude were most frequently observed. Of 707 neurones studied, 310 had altered firing rates during stimulation; changes in opposite directions were monitored simultaneously in fifty-six of eighty-seven data sets with at least two baroresponsive neurones. 4. Short time scale correlations were detected between neurones in 347 of 3388 pairs. Seventeen pairs of baroresponsive raphe neurones exhibited significant offset correlogram features indicative of paucisynaptic interactions. In correlated raphe-ventrolateral medullary neurone pairs with at least one baroresponsive neurone, six of seven ventrolateral medullary decrementing expiratory (E-Decr) neurones increased their firing rate during baroreceptor stimulation. Thirteen of fifteen ventrolateral medullary inspiratory neurones correlated with raphe cells decreased their firing rate during baroreceptor stimulation. 5. The results support the hypothesis that raphe neuronal assemblies transform and transmit information from baroreceptors to neurones in the ventral respiratory group. The inferred actions both limit and promote responses to sensory perturbations and match predictions from simulations of the respiratory network.

Adrenergic alpha-1 Receptor Agonists↗

PubMed2004

Modeling and analysis of heterogeneous regulation in biological networks.

In this study, we propose a novel model for the representation of biological networks and provide algorithms for learning model parameters from experimental data. Our approach is to build an initial model based on extant biological knowledge and refine it to increase the consistency between model predictions and experimental data. Our model encompasses networks which contain heterogeneous biological entities (mRNA, proteins, metabolites) and aims to capture diverse regulatory circuitry on several levels (metabolism, transcription, translation, post-translation and feedback loops, among them). Algorithmically, the study raises two basic questions: how to use the model for predictions and inference of hidden variables states, and how to extend and rectify model components. We show that these problems are hard in the biologically relevant case where the network contains cycles. We provide a prediction methodology in the presence of cycles and a polynomial time, constant factor approximation for learning the regulation of a single entity. A key feature of our approach is the ability to utilize both high-throughput experimental data, which measure many model entities in a single experiment, as well as specific experimental measurements of few entities or even a single one. In particular, we use together gene expression, growth phenotypes, and proteomics data. We tested our strategy on the lysine biosynthesis pathway in yeast. We constructed a model of more than 150 variables based on an extensive literature survey and evaluated it with diverse experimental data. We used our learning algorithms to propose novel regulatory hypotheses in several cases where the literature-based model was inconsistent with the experiments. We showed that our approach has better accuracy than extant methods of learning regulation.

Algorithms↗

PubMed2006

Phylogenomics and molecular evolution of polyomaviruses.

We provide in this chapter an overview of the basic steps to reconstruct evolutionary relationships through standard phylogeny estimation approaches as well as network approaches for sequences more closely related. We discuss the importance of sequence alignment, selecting models of evolution, and confidence assessment in phylogenetic inference. We also introduce the reader to a variety of software packages used for such studies. Finally, we demonstrate these approaches throughout using a data set of 33 whole genomes of polyomaviruses. A robust phylogeny of these genomes is estimated and phylogenetic relationships among the polyomaviruses determined using Bayesian and maximum likelihood approaches. Furthermore, population samples of SV40 are used to demonstrate the utility of network approaches for closely related sequences. The phylogenetic analysis suggested a close relationship among the BK viruses, JC viruses, and SV40 with a more distant association with mouse polyomavirus, monkey polymavirus (LPV) and then avian polyomavirus (BFDV).

Computational Biology↗

PubMed2002

Predicted solution structure of zymogen human coagulation FVII.

A model solution structure for the complete tissue factor-free calcium ion-bound human zymogen FVII (residues 1-406) (FVII) has been constructed to study possible conformational changes associated with the activation process and tissue factor (TF) binding. The initial structure for the present model was constructed using the X-ray crystallographic structure of human coagulation FVIIa/TF complex bound with calcium ions (Banner et al., Nature 1996, 380, 41-46). This model was subsequently subjected to lengthy molecular dynamics simulations. The Amber force field in conjunction with the PME electrostatic summation method was employed. The estimated TF free solution structure was then compared with the currently available X-ray crystal structures of FVIIa (with or without TF, variable inhibitor bound) to estimate the restructuring of FVII due to TF binding and activation. The solution structure of the zymogen FVII in the absence of TF is predicted to be an extended domain structure similar to that of the TF-bound X-ray crystal structure. An additional extension of the serine protease (SP) domain of the zymogen above a reference lipid surface by approximately 7 A was in agreement with experiment. Significant Gla-EGF1 and EGF1-EGF2 interdomain motions in the zymogen were observed. Carbohydrate dimers attached to Ser-52 and Ser-60 did not cause restructuring in this domain. Minimal restructuring of the SP domain is found upon inference of the zymogen from the activated form. The catalytic triad residues maintain the H-bonded network while Lys-341 occupies the S1 specific site in the zymogen.

Binding Sites↗

PubMed2005

A case study of capture-recapture methodology using scrapie surveillance data in Great Britain.

We applied capture-recapture methodology (CRC) to data from three surveillance sources (statutory notification, abattoir survey (AS) and fallen stock (FS) survey) to estimate the number of holdings infected with scrapie in Great Britain and to assess the sensitivity of the surveillance network. Between January 1, 2002 and March 31, 2003, 144 holdings were identified by the three sources. Using CRC modelling techniques, we estimated a minimum lower bound for the total number of holdings infected as 642. A biologically plausible positive dependence between the statutory reporting and the fallen stock survey was found statistically significant. The sensitivity of the three sources combined was very low. The integration of the three overlapping sources provided a better understanding of the interactions within the surveillance network. However, the scarcity of the data and reduced overlapping among sources only allowed for very cautious inferences to be drawn about the true proportion of scrapie affected holdings in the national population. Future surveys and surveillance activities should be planned such that the resulting data can be used more effectively as part of CRC modelling approaches.

Agriculture↗

PubMed1999

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems.

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.

Journal Article↗

PubMed2006

Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data.

As one of the most recent members of the omics family, large-scale quantitative metabolomics data are currently complementing our systems biology data pool and offer the chance to integrate the metabolite level into the functional analysis of cellular networks. Network-embedded thermodynamic analysis (NET analysis) is presented as a framework for mechanistic and model-based analysis of these data. By coupling the data to an operating metabolic network via the second law of thermodynamics and the metabolites' Gibbs energies of formation, NET analysis allows inferring functional principles from quantitative metabolite data; for example it identifies reactions that are subject to active allosteric or genetic regulation as exemplified with quantitative metabolite data from Escherichia coli and Saccharomyces cerevisiae. Moreover, the optimization framework of NET analysis was demonstrated to be a valuable tool to systematically investigate data sets for consistency, for the extension of sub-omic metabolome data sets and for resolving intracompartmental concentrations from cell-averaged metabolome data. Without requiring any kind of kinetic modeling, NET analysis represents a perfectly scalable and unbiased approach to uncover insights from quantitative metabolome data.

Computational Biology↗

PubMed2003

Changes in biologically active ultraviolet radiation reaching the Earth's surface.

Since publication of the 1998 UNEP Assessment, there has been continued rapid expansion of the literature on UV-B radiation. Many measurements have demonstrated the inverse relationship between column ozone amount and UV radiation, and in a few cases long-term increases due to ozone decreases have been identified. The quantity, quality and availability of ground-based UV measurements relevant to assessing the environmental impacts of ozone changes continue to improve. Recent studies have contributed to delineating regional and temporal differences due to aerosols, clouds, and ozone. Improvements in radiative transfer modelling capability now enable more accurate characterization of clouds, snow-cover, and topographical effects. A standardized scale for reporting UV to the public has gained wide acceptance. There has been increased use of satellite data to estimate geographic variability and trends in UV. Progress has been made in assessing the utility of satellite retrievals of UV radiation by comparison with measurements at the Earth's surface. Global climatologies of UV radiation are now available on the Internet. Anthropogenic aerosols play a more important role in attenuating UV irradiances than has been assumed previously, and this will have implications for the accuracy of UV retrievals from satellite data. Progress has been made inferring historical levels of UV radiation using measurements of ozone (from satellites or from ground-based networks) in conjunction with measurements of total solar radiation obtained from extensive meteorological networks. We cannot yet be sure whether global ozone has reached a minimum. Atmospheric chlorine concentrations are beginning to decrease. However, bromine concentrations are still increasing. While these halogen concentrations remain high, the ozone layer remains vulnerable to further depletion from events such as volcanic eruptions that inject material into the stratosphere. Interactions between global warming and ozone depletion could delay ozone recovery by several years, and this topic remains an area of intense research interest. Future changes in greenhouse gases will affect the future evolution of ozone through chemical, radiative, and dynamic processes In this highly coupled system, an evaluation of the relative importance of these processes is difficult: studies are ongoing. A reliable assessment of these effects on total column ozone is limited by uncertainties in lower stratospheric response to these changes. At several sites, changes in UV differ from those expected from ozone changes alone, possibly as a result of long-term changes in aerosols, snow cover, or clouds. This indicates a possible interaction between climate change and UV radiation. Cloud reflectance measured by satellite has shown a long-term increase at some locations, especially in the Antarctic region, but also in Central Europe, which would tend to reduce the UV radiation. Even with the expected decreases in atmospheric chlorine, it will be several years before the beginning of an ozone recovery can be unambiguously identified at individual locations. Because UV-B is more variable than ozone, any identification of its recovery would be further delayed.

Earth, Planet↗

PubMed2006

Butyrophilin controls milk fat globule secretion.

The molecular mechanism underlying milk fat globule secretion in mammary epithelial cells ostensibly involves the formation of complexes between plasma membrane butyrophilin and cytosolic xanthine oxidoreductase. These complexes bind adipophilin in the phospholipid monolayer of milk secretory granules, the precursors of milk fat globules, enveloping the nascent fat globules in a layer of plasma membrane and pinching them off the cell. However, using freeze-fracture immunocytochemistry, we find these proteins in locations other than those previously inferred. Significantly, butyrophilin in the residual plasma membrane of the fat globule envelope is concentrated in a network of ridges that are tightly apposed to the monolayer derived from the secretory granule, and the ridges coincide with butyrophilin labeling in the globule monolayer. Therefore, we propose that milk fat globule secretion is controlled by interactions between plasma membrane butyrophilin and butyrophilin in the secretory granule phospholipid monolayer rather than binding of butyrophilin-xanthine oxidoreductase complexes to secretory granule adipophilin.

Antibodies↗

PubMed2006

Effects of diabetic neuropathy on body sway and slip perturbation detection in older population.

Postural control is a common mechanism to compensate for unexpected displacements of the body. In the older population, a slip or fall due to a failure of postural control is a common cause of morbidity and mortality. The ability of postural control decreases with aging or neuropathy. In this study, 2 groups, diabetics and non-diabetics in the older population, were compared to determine how patterns of postural sway during quiet standing were related to the detection of perturbation. The SLIP-FALLS system was applied to the measurement of sway and detection of perturbation. In phase 1 of the development of the predictive model, neural network algorithms were applied to find determinant variables for perturbation detection. In phase 2, a fuzzy logic inference system was developed to investigate the relationship between sway and perturbation detection. Results of this study may be applied to the design of floor mats or shoe insoles for preventing fatigue in workplaces.

Aging↗

PubMed1977

[A case of multiple anomalies of cerebral vessels--fenestration of the middle cerebral artery aneurysm of the anterior communicating artery and arteriovenous malformation on the frontopolar region (author's transl)].

A case associated with multiple cerebral vascular anomalies, which consisted of fenestration of the middle cerebral artery, arteriovenous malformation and aneurysm of the anterior communicating artery, was reported. A 48 year-old male has been suffering from the left paralysis and mental disorder after the initial attack of subarachnoid hemorrhage, and the second attack resulted in the deterioration of the symptoms. He was admitted to our clinic on October 28, 1974. On neurological examination, mental disorders, such as disorientation, emotional incontinence, amnesia and acalculia, hemiplegia on the left and meningeal irritation signs were observed in admission period. Physical examination was negative. Cerebral angiographic findings were as follows: 1) Moderate vasospasm of the right internal carotid artery at the terminal segment, mild bowing of the anterior cerebral artery and stretching of the frontparietal opercular branches of the middle cerebral artery were observed. 2) Right frontpolar arteriovenous malformation fed by the frontobasal artery and the frontopolar artery, and drained via the aberrant cortical vein into the superior sagittal sinus. 3) Aneurysm of the anterior communicating artery was opacified by left carotid angiography. 4) An abnormal vessel derived from the terminal segment of the right internal carotid artery and terminated at the portion of the sphenoidal segment of the middle cerebral artery. Complete loop was formed between genuine middle cerebral artery and this abnormal artery. He was operated with dissecting microscope on November 11, 1974. The arteriovenous malformation at right frontopolar region was totally removed and aneurysm of the anterior communicating artery was clipped. According to the operative findings, the arachnoid membrane over the right frontopolar region was turbid and adhered to the adjacent tissues. On the contrary, no abnormal findings suggestive of previous subarachnoid hemorrhage were observed around the region of the anterior communicating artery aneurysm. These findings showed that subarachnoidal bleeding was caused by rupture of the arteriovenous malformation of right frontopolar region, but not by the aneurysm on the anterior communicating artery. The postoperative course was uneventful and during the hospitalization the patient starts on rehabilitation therapy. The authors discussed the genesis of fenestration of the middle cerebral artery and relation among these combined vascular anomalies. We inferred that fenestration of the middle cerebral artery arose from the in complete fusion of procursor vascular network in embryonic stage. Additionally, we emphasized that it was necessary to make a distinction between these two terms "fenestration" and "duplication".

Cerebral Angiography↗

PubMed2006

fMRI models of dendritic and astrocytic networks.

In order to elucidate the relationships between hierarchical structures within the neocortical neuropil and the information carried by an ensemble of neurons encompassing a single voxel, it is essential to predict through volume conductor modeling LFPs representing average extracellular potentials, which are expressed in terms of interstitial potentials of individual cells in networks of gap-junctionally connected astrocytes and synaptically connected neurons. These relationships have been provided and can then be used to investigate how the underlying neuronal population activity can be inferred from the measurement of the BOLD signal through electrovascular coupling mechanisms across the blood-brain barrier. The importance of both synaptic and extrasynaptic transmission as the basis of electrophysiological indices triggering vascular responses between dendritic and astrocytic networks, and sequential configurations of firing patterns in composite neural networks is emphasized. The purpose of this review is to show how fMRI data may be used to draw conclusions about the information transmitted by individual neurons in populations generating the BOLD signal.

Animals↗

PubMed2005

Bioinformatic methods for integrating whole-genome expression results into cellular networks.

Extracting a comprehensive overview from the huge amount of information arising from whole-genome analyses is a significant challenge. This review critically surveys the state of the art methods that are used to connect information from functional genomic studies to biological function. Cluster analysis methods for inferring the correlation between genes are discussed, as are the methods for integrating gene expression information with existing information on biological pathways and the methods that combine cluster analysis with biological information to reconstruct novel biological networks.

Cluster Analysis↗