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At least 379 records · Page 21Linked to original sources
PubMed2003

Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis.

Cells adjust gene expression profiles in response to environmental and physiological changes through a series of signal transduction pathways. Upon activation or deactivation, the terminal regulators bind to or dissociate from DNA, respectively, and modulate transcriptional activities on particular promoters. Traditionally, individual reporter genes have been used to detect the activity of the transcription factors. This approach works well for simple, non-overlapping transcription pathways. For complex transcriptional networks, more sophisticated tools are required to deconvolute the contribution of each regulator. Here, we demonstrate the utility of network component analysis in determining multiple transcription factor activities based on transcriptome profiles and available connectivity information regarding network connectivity. We used Escherichia coli carbon source transition from glucose to acetate as a model system. Key results from this analysis were either consistent with physiology or verified by using independent measurements.

Cyclic AMP↗
PubMed1997

Calliper randomization: an artificial neural network based analysis of E. coli ribosome binding sites.

An artificial neural network based approach has been used in analyzing the translation initiation region of E. coli. The approach is based on using a trained network capable of recognizing a particular region and presenting the network with randomized calliper inputs of the true sequence. The network responds with an error when the regions which have been the main source of knowledge are randomized. Analysis of the E. coli ribosome binding sites using this approach reveal that the initiation codon and the Shine/Dalgarno sequence which are known to be important for translation initiation are also important in imparting knowledge to the network. Further, selectively changing the usually occurring initiation codon AUG, to GUG, UUG and AUU, which occur less frequently, decreases the network performance in accordance with the frequency of their occurrence. This approach can be used as a general method to derive consensus.

Binding Sites↗
PubMed2003

Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks.

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden-Fletcher-Goldfarb-Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

Algorithms↗
PubMed2006

Neural network-based analysis of thiol proteomics data in identifying potential selenium targets.

Generation of a monomethylated selenium metabolite is critical for the anticancer activity of selenium. Because of its strong nucleophilicity, the metabolite can react directly with protein thiols to cause redox modification. Here, we report a neural network-based analysis to identify potential selenium targets. A reactive thiol specific reagent, BIAM, was used to monitor thiol proteome changes on 2D gel. We constructed a dynamic model and evaluated the relative importance of proteins mediating the cellular responses to selenium. Information from this study will provide new clues to unravel mechanisms of anticancer action of selenium. High impact selenium targets could also serve as biomarkers to gauge the efficacy of selenium chemoprevention.

Antineoplastic Agents↗
PubMed2000

Lay theories of successful aging after the death of a spouse: a network text analysis of bereavement advice.

Social theories of successful aging attempt to explain how individuals adapt to changes characteristically associated with aging and to predict whether older adults' adaptations will lead to successful aging. The death of a spouse and the accompanying bereavement process entail dramatic changes to personal networks and experience to which individuals must adapt to age successfully. Network text analysis (including word frequencies, cluster analysis, and multidimensional scaling) of advice for adjusting to, and coping with, the loss of a spouse given by a sample of 60 bereaved spouses (mean age = 68) at 6 points in time after the death of their marital partner (3-4 weeks to 24 months) reveal respondents' lay theories of successful aging. Thematic clusters address social positioning and qualifiers, activity, communication, time, and spousal characteristics. Results indicate respondents frame their advice as unique to their context of social relationships while providing support for activity theory and negatively addressing disengagement theory.

Aged↗
PubMed2001

Functional networks in motor sequence learning: abnormal topographies in Parkinson's disease.

We examined the neural circuitry underlying the explicit learning of motor sequences in normal subjects and patients with early stage Parkinson's disease (PD) using 15O-water (H2 15O) positron emission tomography (PET) and network analysis. All subjects were scanned while learning motor sequences in a task emphasizing explicit learning, and during a kinematically controlled motor execution reference task. Because different brain networks are thought to subserve target acquisition and retrieval during motor sequence learning, we used separate behavioral indices to quantify these aspects of learning during the PET experiments. In the normal cohort, network analysis of the PET data revealed a significant covariance pattern associated with acquisition performance. This topography was characterized by activations in the left dorsolateral prefrontal cortex (PFdl), rostral supplementary motor area (preSMA), anterior cingulate cortex, and in the left caudate/putamen. A second independent covariance pattern was associated with retrieval performance. This topography was characterized by bilateral activations in the premotor cortex (PMC), and in the right precuneus and posterior parietal cortex. The normal learning-related topographies failed to predict acquisition performance in PD patients and predicted retrieval performance less accurately in the controls. A separate network analysis was performed to identify discrete learning-related topographies in the PD cohort. In PD patients, acquisition performance was associated with a covariance pattern characterized by activations in the left PFdl, ventral prefrontal, and rostral premotor regions, but not in the striatum. Retrieval performance in PD patients was associated with a covariance pattern characterized by activations in the right PFdl, and bilaterally in the PMC, posterior parietal cortex, and precuneus. These results suggest that in early stage PD sequence learning networks are associated with additional cortical activation compensating for abnormalities in basal ganglia function.

Adult↗
PubMed2003

A network-informed approach to investigating a tuberculosis outbreak: implications for enhancing contact investigations.

BACKGROUND: To elucidate networks of Mycobacterium tuberculosis transmission, it may be appropriate to characterize the types of relationships among tuberculosis (TB) cases and their contacts (with and without latent TB infection) in addition to relying on traditional efforts to distinguish 'close' from 'casual' contacts. SETTING: A TB outbreak in a US low incidence state. OBJECTIVE: To evaluate whether social network analysis can provide insights into transmission settings that might otherwise go unrecognized by routine practices. DESIGN: All adult outbreak-associated cases (n = 19) and a convenience sample of their contacts with and without latent TB infection (LTBI) (n = 26) were re-interviewed in 2001 using a structured questionnaire. Network analysis software was used to create diagrams illustrating important persons within the outbreak network, as well as types of activities TB cases engaged in with their contacts. RESULTS: Drug use and drug sharing were more commonly reported among cases and their infected contacts than among contacts without LTBI. TB cases central to the outbreak network used crack cocaine, uncovering the need to focus control efforts on specific sites and persons involved in illicit drug use. CONCLUSION: Outbreaks occur even in areas with low TB incidence, frequently among groups whose drug use or other illegal activities complicate control efforts. TB programs should consider the use of network analysis as a supplement to routine contact investigations to identify unrecognized patterns of M. tuberculosis transmission.

Adolescent↗
PubMed1997

[Use of neural networks for analysis of visual evoked potentials developing a type of "pattern"--preliminary report].

PURPOSE: The authors present the "pattern" visual evoked potentials (VEP) analysis with use of the artificial neural networks (ANN). MATERIALS AND METHODS: The study involved 11 patients with compressive chiasmal optic neuropathy, 20 patients with optic neuritis, 12 patients with anterior ischaemic optic neuropathy, 20 patients with optic nerve atrophy from neuritis, 8 patients with demyelinative neuropathy, 5 patients with oedema optic nerve, 20 healthy persons. The tests of visual evoked potentials were performed with the use of computer system UTAS-E1000. Classification of potentials was made by correlation of outputs of ANN with results of confirmed neuro-ophthalmology conditions. RESULTS: ANN of different architecture were classified correctly in 80-100% of VEP record samples. CONCLUSIONS: The obtained correctness of classification confirms usefulness of VEP analysis as the objective diagnostic method in some neuro-ophthalmological diseases and indicates application of ANN in multifactor analysis.

Adolescent↗
PubMed2006

Transcriptional network dynamics in macrophage activation.

Transcriptional regulatory networks govern cell differentiation and the cellular response to external stimuli. However, mammalian model systems have not yet been accessible for network analysis. Here, we present a genome-wide network analysis of the transcriptional regulation underlying the mouse macrophage response to bacterial lipopolysaccharide (LPS). Key to uncovering the network structure is our combination of time-series cap analysis of gene expression with in silico prediction of transcription factor binding sites. By integrating microarray and qPCR time-series expression data with a promoter analysis, we find dynamic subnetworks that describe how signaling pathways change dynamically during the progress of the macrophage LPS response, thus defining regulatory modules characteristic of the inflammatory response. In particular, our integrative analysis enabled us to suggest novel roles for the transcription factors ATF-3 and NRF-2 during the inflammatory response. We believe that our system approach presented here is applicable to understanding cellular differentiation in higher eukaryotes.

Algorithms↗
PubMed2005

Gene-expression analysis and network discovery using Genevestigator.

The Genevestigator software suite belongs to a new generation of web-based tools that provide categorized quantitative information about elements (genes or annotations) contained in large microarray databases. The first version of Genevestigator, in use since 2004, has now been updated to enable faster, more powerful and more diverse types of queries for gene-function analysis and network discovery.

Databases, Genetic↗
PubMed2004

Comparison of network-based pathway analysis methods.

Network-based definitions of biochemical pathways have emerged in recent years. These pathway definitions insist on the balanced use of a whole network of biochemical reactions. Two such related definitions, elementary modes and extreme pathways, have generated novel hypotheses regarding biochemical network function. The relationship between these two approaches can be illustrated by comparing and contrasting the elementary modes and extreme pathways of previously published metabolic reconstructions of the human red blood cell (RBC) and the human pathogen Helicobacter pylori. Descriptions of network properties generated by using these two approaches in the analysis of realistic metabolic networks need careful interpretation.

Algorithms↗
PubMed2025

PROLONG: penalized regression for outcome guided longitudinal omics analysis with network and group constraints.

MOTIVATION: There is a growing interest in longitudinal omics data paired with some longitudinal clinical outcome. Given a large set of continuous omics variables and some continuous clinical outcome, each measured for a few subjects at only a few time points, we seek to identify those variables that co-vary over time with the outcome. To motivate this problem we study a dataset with hundreds of urinary metabolites along with Tuberculosis mycobacterial load as our clinical outcome, with the objective of identifying potential biomarkers for disease progression. For such data clinicians usually apply simple linear mixed effects models which often lack power given the low number of replicates and time points. We propose a penalized regression approach on the first differences of the data that extends the lasso + Laplacian method [Li and Li (Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 2008;24:1175-82.)] to a longitudinal group lasso + Laplacian approach. Our method, PROLONG, leverages the first differences of the data to increase power by pairing the consecutive time points. The Laplacian penalty incorporates the dependence structure of the variables, and the group lasso penalty induces sparsity while grouping together all contemporaneous and lag terms for each omic variable in the model. RESULTS: With an automated selection of model hyper-parameters, PROLONG correctly selects target metabolites with high specificity and sensitivity across a wide range of scenarios. PROLONG selects a set of metabolites from the real data that includes interesting targets identified during EDA. AVAILABILITY AND IMPLEMENTATION: An R package implementing described methods called "prolong" is available at https://github.com/stevebroll/prolong. Code snapshot available at 10.5281/zenodo.14804245.

Humans↗
PubMed1997

Normative models and healthcare planning: network-based simulations within a geographic information system environment.

OBJECTIVES: Network analysis to integrate patient, transportation and hospital characteristics for healthcare planning in order to assess the role of geographic information systems (GIS). A normative model of base-level responses of patient flows to hospitals, based on estimated travel times, was developed for this purpose. DATA SOURCES/STUDY SETTING: A GIS database developed to include patient discharge data, locations of hospitals, US TIGER/Line files of the transportation network, enhanced address-range data, and U.S. Census variables. The study area included a 16-county region centered on the city of Charlotte and Mecklenburg County, North Carolina, and contained 25 hospitals serving nearly 2 million people over a geographic area of nearly 9,000 square miles. STUDY DESIGN: Normative models as a tool for healthcare planning were derived through a spatial Network analysis and a distance optimization model that was implemented within a GIS. Scenarios were developed and tested that involved patient discharge data geocoded to the five-digit zip code, hospital locations geocoded to their individual addresses, and a transportation network of varying road types and corresponding estimated travel speeds to examine both patient discharge levels and a doubling of discharge levels associated with total discharges and DRG 391 (Normal Newborns). The Network analysis used location/allocation modeling to optimize for travel time and integrated measures of supply, demand, and impedance. DATA COLLECTION/EXTRACTION METHODS: Patient discharge data from the North Carolina Medical Database Commission, address-ranges from the North Carolina Institute for Transportation Research and Education, and U.S. Census TIGER/Line files were entered-into the ARC/INFO GIS software system for analysis. A relational database structure was used to organize the information and to link spatial features to their attributes. PRINCIPAL FINDINGS: Advances in healthcare planning can be achieved by examining baseline responses of patient flows to distance optimization simulations and healthcare scenarios conducted within a spatial context that uses a normative model to integrate characteristics of population, patients, hospitals, and transportation networks. Model runs for the defined scenarios indicated that a doubling of the 1991 patient discharge levels resulted in an areal constriction of the service areas to those zip codes immediately adjacent to the hospitals, thereby leaving substantial areas unassigned to hospitals during the allocation process, but that doubling the demand for obstetrics care (DRG 391) resulted in little change in the pattern of accessibility to care as indicated by the size, orientation, and pattern of the service areas. CONCLUSIONS: The GIS-Network system supported "what if" simulations, portrayed service areas within a spatial context, integrated disparate data in the execution of the location/allocation model, and used estimated travel time along a transportation network instead of Euclidean distance for calculating accessibility. The results of the simulations suggest that the GIS-Network system is an effective approach for exploring a variety of healthcare scenarios where changes in the supply, demand, and impedance variables can be examined within a spatial context and where variations in system trajectories can be simulated and observed.

Computer Communication Networks↗
PubMed1988

Network thermodynamic analysis and stimulation of isotonic solute-coupled volume flow in leaky epithelia: an example of the use of network theory to provide the qualitative aspects of a complex system and its verification by stimulation.

A network thermodynamic model was developed to provide insights into the nature of isotonic solute-coupled volume flow in "leaky" epithelia, where the transepithelial volume flow is assumed to be primarily through the cellular pathway. The coupled flows of solute and volume at each membrane in this four membrane model are described by the practical phenomenological equations as developed by Kedem & Katchalsky (1958). The model contains one permeable non-electrolyte solute (s) and a fixed amount of an impermeable non-electrolyte (i) inside the cell. The cell is assumed to be capable of volume regulation under the steady-state experimental conditions simulated. A solute-pump, located in the basolateral membrane, uses feedback regulation to adjust Cs in the cell in order to maintain cell volume at or near control levels in all simulations. Model behavior is, in general, very consistent with experimental observations with respect to tonicity and magnitude of volume flow over a wide range of experimental conditions. Examination of the parameter space suggests the following important features when isotonic solute-coupled volume flow moves primarily through the cellular pathway: (1) the apical membrane reflection coefficient must be less than that of the basolateral membrane; (2) the basement membrane reflection coefficient must be small; (3) the apical membrane solute permeability and reflection coefficient are the two most "sensitive" parameters and need to vary in an inverse manner in order to maintain isotonicity when both solute and volume flows increase; and (4) relationships (1) and (3) above imply the need for at least two separate solute pathways in the apical membrane, one that is shared with volume flow and one that is not.

Biological Transport↗
PubMed1998

Serial VCG/ECG analysis using neural networks.

Serial ECG analysis is an important diagnostic tool in which two or more successive ECG recordings from the same patient are compared in order to find changes due to, e.g. myocardial infarction. The present study investigates a new approach to serial analysis which is based on artificial neural networks. Interrecording changes are sometimes falsely detected due to electrode misplacement or positional changes of the heart. In order to compensate for such problems, a new technique for VCG loop alignment was employed. A study population of 1000 patients with two recordings was used and manually scrutinized by three experienced ECG interpreters. Pathological changes indicating newly developed infarcts were found in 256 patients. Different combinations of VCG/ECG measurements served as input data to the neural network. The best performance of the neural network was obtained when ECG and VCG measurements were combined and the resulting sensitivity was 69% at a specificity of 90%. The use of only ECG or VCG measurements reduced the sensitivity to 63% and 60%, respectively. The results indicated that serial analysis based on neural networks did not improve significantly when VCG loop alignment was included.

Case-Control Studies↗
PubMedDate not supplied

Neural-network applications for analysis of adverse drug reactions.

A neural-network analysis has been applied to predict adverse or side effects of drugs using a database of antidepressant agents with known effects as well as input from a database with both patient parameters and drug information. A NeuralWork software package was implemented on a Macintosh Quadra 700 and trained on a database of ten drugs with known adverse effects. Another agent (not in the database) was used to test the ability of the network to predict the relative incidence of its side effects. Despite the small number of drugs used for training, the adverse effects of some drugs, such as doxepin, were predicted with 90-100% accuracy. These results indicate that neural-network analysis can be used to predict adverse drug effects for drugs within a given class and ultimately can be extended to include patient parameters to predict the mechanisms of action of drugs from relatively large databases.

Adverse Drug Reaction Reporting Systems↗
PubMed2026

Gut microbiota-derived metabolites target C5AR1/KDM2A/HCAR3 axis in inflammatory bowel disease: a multi-machine learning algorithms and molecular docking study.

BACKGROUND: Inflammatory bowel disease (IBD) is a chronic recurrent disorder. Gut microbiota-derived metabolites regulate intestinal homeostasis, but their molecular mechanisms in IBD remain unclear. Current studies lack systematic "microbiota-metabolite-target" network mining with multi-method validation. This study integrates network pharmacology, three machine learning algorithms, and molecular docking to construct this regulatory network in IBD. METHODS: Transcriptome data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using limma (p < 0.05, |log2FC| > 0.5). Weighted gene co-expression network analysis (WGCNA) with an optimal soft threshold of &#x3b2; = 7 was performed to identify key module genes. Candidate genes were obtained by intersecting DEGs, gut microbiota-associated genes from the gutMGene database, and WGCNA module genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of candidate genes. Core genes were identified using three machine learning algorithms (LASSO, Boruta, and SVM-RFE), followed by protein-protein interaction (PPI) network analysis. Molecular docking was performed to assess the binding affinities between hub proteins and gut microbiota-derived metabolites. RESULTS: A total of 885 DEGs were identified between the IBD and control groups, including 463 upregulated and 422 downregulated genes. WGCNA identified 280 key module genes from the purple and yellow modules. The intersection of DEGs, gut microbiota-associated genes, and WGCNA module genes yielded 19 core candidate genes. PPI network analysis combined with three machine learning algorithms jointly identified C5AR1, KDM2A, and HCAR3 as core hub genes. ROC curve analysis demonstrated that all three hub genes achieved AUC values greater than 0.7 in both the training and validation sets, indicating excellent diagnostic performance for IBD. Enrichment analysis revealed significant associations with the TNF, NF-&#x3ba;B, and IL-17 signaling pathways. Molecular docking confirmed stable binding of C5AR1 with 1,3-Diphenylpropan-2-Ol (-7.87 &#xb1; 0.83 kcal&#xb7;mol-&#xb9;) and HCAR3 with 3-Indolepropionic Acid (-6.35 &#xb1; 0.70 kcal&#xb7;mol-&#xb9;), both below -5.0 kcal&#xb7;mol-&#xb9;. CONCLUSION: This study first constructs a "gut microbiota-metabolite-hub gene" axis in IBD, providing a computational framework for microbiota-targeted precision therapy, and identifying C5AR1/KDM2A/HCAR3 as computationally predicted diagnostic biomarkers and 1,3-Diphenylpropan-2-Ol/3-Indolepropionic Acid as candidate intervention molecules that warrant further experimental validation.

Molecular Docking Simulation↗