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At least 235 records · Page 13Linked to original sources
PubMed1998

Prediction of cirrhosis in patients with chronic hepatitis C infection by artificial neural network analysis of virus and clinical factors.

The diagnosis of cirrhosis in patients with hepatitis C virus (HCV) infection is currently made using a liver biopsy. In this study we have trained and validated artificial neural networks (ANN) with routine clinical host and viral parameters to predict the presence or absence of cirrhosis in patients with chronic HCV infection and assessed and interpreted the role of the different inputs on the ANN classification. Fifteen routine clinical and virological factors were collated from 112 patients who were HCV RNA positive by reverse transcriptase-polymerase chain reaction (RT-PCR). Standard and Ward-type feed-forward fully-connected ANN analyses were carried out both by training the networks with data from 82 patients and subsequently testing with data from 30 patients plus performing leave-one-out tests for the whole patient data set. The ANN results were also compared with those from multiple logistic regression. The performance of both ANN methods was superior compared with the logistic regression. The best performance was obtained with the Ward-type ANNs resulting in a sensitivity of 92% and a specificity of 98.9% together with a predictive value of a positive test of 95% and a predictive value of a negative test of 97% in the leave-one-out test. Hence, further validation of the ANN analysis is likely to provide a non-invasive test for diagnosing cirrhosis in HCV-infected patients.

Adult↗
PubMed2005

Infection in social networks: using network analysis to identify high-risk individuals.

Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals' risk of infection and time to infection in small-world and randomly mixing networks. Infection transmitted more rapidly but ultimately resulted in fewer infected individuals in the small-world, compared with the random, network. The ability of measures of network centrality to identify high-risk individuals was also assessed. "Centrality" describes an individual's position in a population; numerous parameters are available to assess this attribute. Here, the authors use the centrality measures degree (number of contacts), random-walk betweenness (a measure of the proportion of times an individual lies on the path between other individuals), shortest-path betweenness (the proportion of times an individual lies on the shortest path between other individuals), and farness (the sum of the number of steps between an individual and all other individuals). Each was associated with time to infection and risk of infection in the simulated outbreaks. In the networks examined, degree (which is the most readily measured) was at least as good as other network parameters in predicting risk of infection. Identification of more central individuals in populations may be used to inform surveillance and infection control strategies.

Community-Acquired Infections↗
PubMed2006

Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors.

Interpretation of Human Immunodeficiency Virus 1 (HIV-1) genotypic drug resistance is still a major challenge in the follow-up of antiviral therapy in infected patients. Because of the high degree of HIV-1 natural variation, complex interactions and stochastic behaviour of evolution, the role of resistance mutations is in many cases not well understood. Using Bayesian network learning of HIV-1 sequence data from diverse subtypes (A, B, C, F and G), we could determine the specific role of many resistance mutations against the protease inhibitors (PIs) nelfinavir (NFV), indinavir (IDV), and saquinavir (SQV). Such networks visualize relationships between treatment, selection of resistance mutations and presence of polymorphisms in a graphical way. The analysis identified 30N, 88S, and 90M for nelfinavir, 90M for saquinavir, and 82A/T and 46I/L for indinavir as most probable major resistance mutations. Moreover we found striking similarities for the role of many mutations against all of these drugs. For example, for all three inhibitors, we found that the novel mutation 89I was minor and associated with mutations at positions 90 and 71. Bayesian network learning provides an autonomous method to gain insight in the role of resistance mutations and the influence of HIV-1 natural variation. We successfully applied the method to three protease inhibitors. The analysis shows differences with current knowledge especially concerning resistance development in several non-B subtypes.

Bayes Theorem↗
PubMedDate not supplied

Artificial neural network analysis of 1H nuclear magnetic resonance spectroscopic data from human plasma.

Nuclear magnetic resonance (NMR) spectroscopy is finding increasing use in studies of plasma and lipoproteins in health and disease, including cancer. Analysis of the NMR data is not straightforward due to complex systems and also partly unknown underlying biochemistry. Here we demonstrate how artificial neural networks can be utilised in biomedical NMR. Their quantification power is illustrated by establishing lipoprotein lipid quantification directly from plasma 1H NMR data. The biochemical rationale for this example is elucidated on the basis of the relative weights of the spectral inputs in a trained network. A novel application of a Kohonen-type network architecture to classify plasma 1H NMR spectra is also presented.

Humans↗
PubMed2005

A regulatory network analysis of phenotypic plasticity in yeast.

Models for the evolution of phenotypic plasticity suggest when and why plasticity might evolve. However, relatively little is known about the genetic basis of plasticity. Molecular studies have recently demonstrated that gene networks can provide a powerful way to infer phenotype from genotype. Information on the structure of the yeast gene regulatory network was combined with data on variation in gene expression in yeast across multiple environments in order to explore the genetic basis of phenotypic plasticity. The phenotypic plasticity of a gene was positively correlated with the number of transcription factors regulating that gene and was significantly lower for transcription factors than for downstream, nonregulatory genes. Plasticity of a gene was also affected by the local substructure of the network in which it was found and by the gene's function. These results illustrate how network analyses can be used to understand the complex genetic architecture of quantitative traits.

Gene Expression Regulation, Fungal↗
PubMed2006

Network analysis of online bidding activity.

With the advent of digital media, people are increasingly resorting to online channels for commercial transactions. The online auction is a prototypical example. In such online transactions, the pattern of bidding activity is more complex than traditional offline transactions; this is because the number of bidders participating in a given transaction is not bounded and the bidders can also easily respond to the bidding instantaneously. By using the recently developed network theory, we study the interaction patterns between bidders (items) who (that) are connected when they bid for the same item (if the item is bid by the same bidder). The resulting network is analyzed by using the hierarchical clustering algorithm, which is used for clustering analysis for expression data from DNA microarrays. A dendrogram is constructed for the item subcategories; this dendrogram is compared to a traditional classification scheme. The implication of the difference between the two is discussed.

Journal Article↗
PubMed2006

From microarray to biological networks: Analysis of gene expression profiles.

Powerful new methods, such as expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic, or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole-genome expression. The parameters in the model show the influence of one gene expression level on another and are calculated using singular value decomposition as a means of inverting noisy and near-singular matrices. Correlative networks can then be generated based on these parameters with a simple threshold approach. We also demonstrate how dynamic models can be used in conjunction with cluster analysis to analyze microarray time series. Using the parameters from the dynamic model as a metric, two-way hierarchical clustering could be performed to visualize how influencing genes affect the expression levels of responding genes. Application of these approaches is demonstrated using gene expression data in yeast cell cycle.

Computational Biology↗
PubMed2003

Prognosis in node-negative primary breast cancer: a neural network analysis of risk profiles using routinely assessed factors.

BACKGROUND: The present study investigated complex time-dependent effects of routinely assessed factors on the risk of breast cancer recurrence over follow-up time, with a partial logistic artificial neural network (PLANN) model. PATIENTS AND METHODS: PLANN was applied to data from 1793 patients with node-negative breast cancer, not submitted to any adjuvant treatment and with a minimal potential follow-up of 10 years. RESULTS: The shape of the hazard function changed according to histology, which showed a time-dependent effect, partly modulated by estrogen receptors (ERs). Age and progesterone receptors (PgR) showed protective effects; the latter was more evident for short follow-up and high ER values. Tumour size and ER content showed time-dependent unfavourable effects at early and long follow-up times, respectively. Predicted values of disease recurrence probability at 2 years of follow-up showed that low steroid-receptor content, young age and large tumour size were associated with the highest risk of relapse. Although the oldest patients with high ER content seem to be those most protected overall, high risk predictions tend to spread also to higher steroid-receptor contents, intermediate ages and small tumour size, with an increase in follow-up time. CONCLUSION: PLANN with suitable visualisation techniques provided thorough insights into the dynamics of breast cancer recurrence for improving individual risk staging of node-negative breast cancer patients.

Breast Neoplasms↗
PubMed1993

Network analysis of functional auditory pathways mapped with fluorodeoxyglucose: associative effects of a tone conditioned as a Pavlovian excitor or inhibitor.

The purpose of this study was to examine how opposite learned associative properties of the same auditory stimulus are represented by the pattern of network interactions between auditory system structures. [14C(U)]2-fluoro-2-deoxyglucose (FDG) autoradiography was used to compare mean auditory system activity and interregional correlations resulting from the presentation of a tone trained as either a Pavlovian conditioned excitor or inhibitor. Rats were trained with reinforced trials of the conditioned excitor (A+) intermixed with non-reinforced trials of a tone-light compound (AX-). For the Conditioned Excitor group, the tone was the excitor (A+), while for the Conditioned Inhibitor group the tone was the inhibitor (X-). After conditioning, both groups were injected with FDG and presented with the same tone. Structural equation models, constructed from the anatomical connections between auditory regions and their interregional correlations in FDG uptake, were used to calculate path coefficients representing the network interactions. The opposite associative significance of the tone was reflected as functional changes in the interactions between parallel auditory pathways. Direct covariance effects through lemniscal pathways from the ventral cochlear nucleus were similar in absolute magnitude but differed in sign between the Excitor and Inhibitor network models. Extra-auditory influences on the dorsal cochlear nucleus were greater for the tone-inhibitor, reflecting possible interactions of this nucleus with extra-auditory regions. The different associative effects of the tone suggest that central auditory pathways can code not only the physical qualities, but also the associative significance of auditory stimuli. These findings demonstrate that neural network interactions differentiate the associative effects of tones in the brain. It is proposed that associative learning is a distributed property of neural networks and that such a property can be understood by considering the interactions between component parts of the network.

Acoustic Stimulation↗
PubMed2026

Integrative Network Analysis of Bioactive Compounds from Punica granatum L. Peel: Multi-Target Mechanisms in Wound Healing.

BACKGROUND: Wound-healing agents often have limited efficacy and require prolonged recovery times, prompting growing interest in developing herbal-based formulations. Among these, Punica granatum L. has attracted considerable attention because of its high polyphenolic content. Despite its widespread use, the precise pharmacological targets underlying its wound-healing effects remain poorly understood and require systematic investigation. OBJECTIVES: This study aimed to elucidate the underlying pharmacological mechanisms of the topical wound-healing properties of P. granatum L. using a network pharmacology approach. METHODS: Bioactive compounds of P. granatum L. and their potential target genes were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Similarity Ensemble Approach (SEA), and SwissTargetPrediction databases. Wound healing-related genes were retrieved from the GeneCards database. Genes intersecting P. granatum L. targets and wound healing-associated genes were subjected to functional enrichment analyses, including protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The PPI network was further analyzed using Cytoscape, and the phytoconstituent-target interaction network was visualized using Gephi. These findings were validated using molecular docking. RESULTS: A total of 40 intersecting genes were identified as potential P. granatum L. targets involved in wound healing. Among these, EGFR, PTPN11, HRAS, IGF1R, and ESR1 were identified as key hub genes. Functional enrichment analysis indicated that the most significantly enriched signaling pathways included the MAPK, PI3K-Akt, EGFR tyrosine kinase inhibitor resistance, focal adhesion, and FoxO signaling pathways. Molecular docking analysis confirmed favorable binding of quercetin and ellagic acid to the hub targets EGFR, IGF1R, and ESR1. CONCLUSIONS: These findings elucidate the pharmacological pathways underlying P. granatum-mediated wound healing and suggest that P. granatum L. acts as a multi-target modulator in the wound-healing process.

Focal Adhesion↗
PubMed2025

DigiNet: Optimizing personalized care for patients with stage IV non-small cell lung cancer (NSCLC) through a digitally connected provider network-analysis plan of a prospective multicenter cohort trial.

PURPOSE: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care. METHODS: DigiNet is a prospective, controlled, non-randomized multicenter cohort study including patients with stage IV NSCLC in two study regions (East and West) in Germany. The results of molecular diagnostics and clinical information, along with the entire treatment data are documented in a shared database. A board of lung cancer specialists monitors critical events. Patients digitally complete quality of life questionnaires, with results visualized for physicians. To assess the impact of this personalized digital care, a population-based control group will be identified by matching cohorts within the involved cancer registries. The primary endpoint is OS, and secondary endpoints comprise time on first-line treatment and hospitalization rates. Furthermore, a health economic and business economic evaluation will be conducted. Qualitative interviews with patients and physicians will be performed to assess barriers and facilitating factors for implementing the DigiNet intervention. ETHICS: The study protocol was reviewed and approved by the Ethics Committee of the University Hospital of Cologne (21-1521). TRIAL REGISTRATION: NCT05818449, registered retrospectively on December 12, 2022.

Humans↗
PubMedDate not supplied

Nonlinear V1 responses to natural scenes revealed by neural network analysis.

A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response mapping functions of neurons in primary visual cortex (area V1) using natural image stimuli. The network is capable of extracting several known V1 response properties such as: orientation and spatial frequency tuning, the spatial phase invariance of complex cells, and direction selectivity. We present details of a method for training networks and visualizing their properties. We also compare how well conventional explicit models and those developed using neural networks can predict novel responses to natural scenes.

Action Potentials↗
PubMed1999

Genetic network analysis in light of massively parallel biological data acquisition.

Complementary DNA microarray and high density oligonucleotide arrays opened the opportunity for massively parallel biological data acquisition. Application of these technologies will shift the emphasis in biological research from primary data generation to complex quantitative data analysis. Reverse engineering of time-dependent gene-expression matrices is amongst the first complex tools to be developed. The success of reverse engineering will depend on the quantitative features of the genetic networks and the quality of information we can obtain from biological systems. This paper reviews how the (1) stochastic nature, (2) the effective size, and (3) the compartmentalization of genetic networks as well as (4) the information content of gene expression matrices will influence our ability to perform successful reverse engineering.

Computational Biology↗
PubMed2006

Integrating genetic and network analysis to characterize genes related to mouse weight.

Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight-related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest.

Animals↗
PubMed1997

Neural network analysis of Doppler ultrasound blood flow signals: a pilot study.

It has been hypothesised that each artery in the human body has its own characteristic "signature" -a unique Doppler flow profile which can identify the artery and which may also be modified by the presence of disease. To test this hypothesis an artificial neural network (ANN) was trained to recognise three groups of maximum frequency envelopes derived from Doppler ultrasound spectrograms; these were the common carotid, common femoral and popliteal arteries. Data were collected from 24 subjects known to have no significant atheromatous disease. The maximum frequency envelopes were used to create sets of training and testing vectors for a backpropagation ANN. The ANN demonstrated a high success rate for appropriate classification of the test vectors: 100% for the carotid; 92% for the femoral; and 96% for the popliteal artery. This work has demonstrated the ability of the ANN to differentiate accurately between different and similar flow profiles, outlining the potential of this technology to identify subtle changes induced by the onset of arterial disease within a specific vessel. It should be noted that the ANN not only models the maximum frequency envelope but also, unlike standard indices, makes a decision as to which artery the maximum frequency envelope belongs to, thus providing the potential to obviate human subjective classification.

Adult↗
PubMed2006

Cell-cycle-dependent variations in FTIR micro-spectra of single proliferating HeLa cells: principal component and artificial neural network analysis.

We have previously reported spectral differences for cells at different stages of the eukaryotic cell division cycle. These differences are due to the drastic biochemical and morphological changes that occur as a consequence of cell proliferation. We correlate these changes in FTIR absorption and Raman spectra of individual cells with their biochemical age (or phase in the cell cycle), determined by immunohistochemical staining to detect the appearance (and subsequent disappearance) of cell-cycle-specific cyclins, and/or the occurrence of DNA synthesis. Once spectra were correlated with their cells' staining patterns, we used methods of multivariate statistics to analyze the changes in cellular spectra as a function of cell cycle phase.

Cell Cycle↗