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Social network analysis is the study of social structure. This glossary introduces basic concepts in social network analysis. It is designed to help researchers to be more discriminating in their thinking and choice of methods.
Social network analysis comprises a set of research methods that can be used to analyze the relationships among entities such as people, departments, and organizations. The purpose of the analysis is to discover patterns of relationships that affect both individual and organizational attitudes and behavior such as the adoption, diffusion, and use of new medical informatics applications. This paper presents an introduction to the concepts and methods of social network analysis. Several applications to health informatics are described.
Stoichiometric network analysis is a systematic, general approach to the qualitative, nonlinear dynamics of chemical reaction mechanisms and other systems with stoichiometry. The advantage of a qualitative approach is that no rate constants are needed to determine qualitative features of the dynamics. If one is interested in stability, the approach yields inequalities among the steady-state concentrations and the rate of flow through sequences of important reactions. These parameters are often the ones most easily measured experimentally. By comparing such experiments with the inequalities derived from stoichiometric network analysis, one can often prove that certain mechanisms cannot account for oscillations or other types of observed dynamics. The approach covers far more than stability. The existence of steady states of zero concentration has an interesting mathematics and applies to chemical evolution. The folding of the manifold of steady states can be found by direct calculation and plays a role in switching enzymes on and off. The approach leads to theorems showing that some steady states are globally attracting or, possibly, that a region containing chaos or an oscillation is globally attracting. The subject of sensitivity analysis has been reformulated in this context. Algorithms that apply many of the theoretical results to chemical networks have been developed and combined into a computer program package.
Neural network analysis was applied for early diagnosis/exclusion of acute myocardial infarction (AMI), prediction of infarct size, and estimation of "time from onset of infarction." Eighty-eight patients admitted within 8 h after onset of chest pain were included. Blood samples for measurement of myoglobin, creatine kinase isoform MB, and troponin T were obtained every 30 min during the first 3 h and then after successively longer intervals. Data from 50 patients were used to train a set of neural network components of a decision support system. The performance of the system was evaluated and compared with experienced clinicians for the remaining 38 patients. The computer system detected myocardial infarction and predicted infarct size earlier than the clinicians, but did not differ significantly in terms of diagnostic sensitivity, specificity, and predictive values when disregarding time for diagnosis. With a cross-validation procedure the cumulated sensitivities of the computer system for the first five measurements were estimated to be (mean +/- 2SEM, n = 100): 0.77 +/- 0.03, 0.89 +/- 0.02, 0.94 +/- 0.02, 0.97 +/- 0.01, and 0.99 +/- 0.01, respectively, with corresponding cumulated specificities between 0.93 +/- 0.01 and 0.91 +/- 0.01. We concluded that neural network analysis of serial measurements of biochemical markers might provide useful support for the early assessment of patients with suspected AMI.
MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.
Neural Network Analysis, a form of artificial intelligence, was successfully used to predict the clinical outcome of node-positive breast cancer patients. A Neural Network was trained to predict clinical outcome using prognostic information from 1008 patients. During training, the network received as input information tumor hormone receptor status, DNA index and S-phase determination by flow cytometry, tumor size, number of axillary lymph nodes involved with tumor, and age of the patient, as well as length of clinical followup, relapse status, and time of relapse. The ability of the trained Network to determine relapse probability was then validated in a separate set of 960 patients. The Neural Network was as powerful as Cox Regression Modeling in identifying breast cancer patients at high and low risk for relapse.
The high information content in large data sets from voxel-based meta-analyses is complex, making it hard to readily resolve details. Using the meta-analysis network as a standardized data structure, network analysis algorithms can examine complex interrelationships and resolve hidden details. Two new network analysis algorithms have been adapted for use with meta-analysis networks. The first, called replicator dynamics network analysis (RDNA), analyzes co-occurrence of activations, whereas the second, called fractional similarity network analysis (FSNA), uses binary pattern matching to form similarity subnets. These two network analysis methods were evaluated using data from activation likelihood estimation (ALE)-based meta-analysis of the Stroop paradigm. Two versions of these data were evaluated, one using a more strict ALE threshold (P < 0.01) with a 13-node meta-analysis network, and the other a more lax threshold (P < 0.05) with a 22-node network. Java-based applications were developed for both RDNA and FSNA. The RDNA algorithm was modified to provide multiple subnets or maximal cliques for meta-analysis networks. Three different similarity measures were evaluated with FSNA to form subsets of nodes and experiments. RDNA provides a means to gauge importance of metanalysis subnets and complements FSNA, which provides a more comprehensive assessment of node similarity subsets, experiment similarity subsets, and overall node-to-factors similarity. The need to use both presence and absence of activations was an important finding in similarity analyses. FSNA revealed details from the pooled Stroop meta-analysis that would otherwise require separate highly filtered meta-analyses. These new analysis tools demonstrate how network analysis strategies can simplify greatly and enhance voxel-based meta-analyses.
High-throughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We discuss ways to automatically derive a Bayesian network model from proteomic data and to interpret the resulting model.
Social network theory and analysis are applied to examine whether adolescents who fill various social positions that characterize peer group structure differ in prevalence of current smoking. One thousand and ninety-two (1,092) ninth graders in one school system named their three best friends, allowing the identification of each adolescent as clique member, clique liaison, or isolate. At four of five schools, the odds of being a current smoker were significantly higher for isolates than for clique members and liaisons. The relationship was not explained by demographic variables or by the number of friends who smoke.
Network analysis is an approach to research that is uniquely suited to describing, exploring, and understanding structural and relational aspects of health. It is both a methodological tool and a theoretical paradigm that allows us to pose and answer important ecological questions in public health. In this review we trace the history of network analysis, provide a methodological overview of network techniques, and discuss where and how network analysis has been used in public health. We show how network analysis has its roots in mathematics, statistics, sociology, anthropology, psychology, biology, physics, and computer science. In public health, network analysis has been used to study primarily disease transmission, especially for HIV/AIDS and other sexually transmitted diseases; information transmission, particularly for diffusion of innovations; the role of social support and social capital; the influence of personal and social networks on health behavior; and the interorganizational structure of health systems. We conclude with future directions for network analysis in public health.
The use of nuclear grade as a prognostic indicator for breast carcinoma has been limited by interobserver variability. Advances in image analysis and automated cell classification offer one approach to this problem. The authors used the CAS-100 (Cell Analysis System. Elmhurst, IL) system to measure and analyze nuclear morphometric and texture features of cytologic preparations from 35 breast carcinomas (well, moderate, and poorly differentiated) as well as benign lesions. Morphometric and Markovian texture feature data from breast cancer nuclei of various grades comprised a training set, which was then used to establish classification criteria by multivariate (Bayesian) analysis and to train a neural network system. Both systems were tested for the ability to classify the nuclear grade of individual nuclei. There was good agreement between computer classification and the grade assigned by human observer to individual nuclei using either Bayesian or neural network analysis. Thirty-one unknown cases, which were assigned an overall grade by an observer, were then analyzed by computer, and an overall grade assigned based on the grade of nucleus most frequently present. Using this method, both classification systems were able to assign a "correct" grade to low-grade lesions (approximately 70% correct) more often than to high-grade tumors (approximately 20%). Difficulty in computer assignment of high-grade tumors was explained by nuclear heterogeneity in these tumors (i.e., although the percentage of high-grade nuclei was increased compared with that of low-grade tumors, high-grade nuclei frequently did not predominate). The authors present this study to demonstrate the feasibility of using image analysis as an objective means of nuclear grading. Further studies will be needed to establish criteria for assigning overall nuclear grade based on computer analysis of imaging data.
Pulsed laser-induced autofluorescence spectroscopic studies of pathologically certified normal, premalignant, and malignant oral tissues were carried out at 325 nm excitation. The spectral analysis and classification for discrimination among normal, premalignant, and malignant conditions were performed using principal component analysis (PCA) and artificial neural network (ANN) separately on the same set of spectral data. In case of PCA, spectral residuals, Mahalanobis distance, and scores of factors were used for discrimination among normal, premalignant, and malignant cases. In ANN, parameters like mean, spectral residual, standard deviation, and total energy were used to train the network. The ANN used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm for the training of the network. The specificity and sensitivity were determined in both classification schemes. In the case of PCA, they are 100 and 92.9%, respectively, whereas for ANN they are 100 and 96.5% for the data set considered.
The major aim of this work was to compare sensitized responses to amphetamine with those of apomorphine. This was done using both a univariant analysis of locomotor activity and a multivariant neural network analysis of five different behaviors. The neural network analysis compares the pattern of behaviors from a treated group to a set of patterns from control conditions and estimates the dose of drug in control conditions that best matches the pattern of behaviors of the treated group. Both methods indicated that repeated administration of 2.0 mg/kg amphetamine but not 0.5 mg/kg amphetamine resulted in sensitization to the administration of a 0.5 mg/kg amphetamine challenge given 10 days following the end of the sensitizing regimen. Both analyses indicated sensitization following repeated administration of 5 mg/kg apomorphine. Studies of cross sensitization were done using a neural network analysis that could distinguish patterns of behavior elicited by amphetamine from those elicited by apomorphine. Such studies indicated that apomorphine elicits an apomorphine-like response in animals sensitized to either apomorphine or amphetamine. In contrast, amphetamine produces an amphetamine-like response in animals sensitized to amphetamine and an apomorphine-like response in animals sensitized to apomorphine. The results indicate that neural network analysis may be a useful tool for analyzing drug effects on patterns of behavior.
Metabolic network analysis is a tool for investigating the features that identify the topology of a metabolic network and the relative activities of its individual branches. The pillars of metabolic network analysis are mathematical modeling, allowing for a quantitative analysis, biochemical knowledge of, for example, reaction stoichiometry, and the experimental techniques, providing input for the modeling part. The modeling part includes metabolite balancing, usually the basis for metabolic flux analysis, and isotope balancing. Isotope balancing can be used for both identification of active pathways and for estimation of the relative fluxes through two pathways leading to the same metabolite, aspects that are difficult to investigate using metabolite balancing. The combination of metabolite and isotope balancing is very powerful and constitutes the basis of metabolic network analysis. With the main focus being on investigating the metabolic network structure, this review describes how central metabolic features, for example, pathway identification, flux distribution, and compartmentation, can be addressed using a combination of metabolite balancing and labeling experiments.
A pattern recognition system based on Neural Network Analysis, a form of artificial intelligence, was used to search DNA flow cytometry histograms for features that correlated with breast cancer patients' risk of relapse. DNA flow cytometry histograms and clinical follow-up information from 796 breast cancer patients were used to train a Neural Network to predict the clinical outcome of patients in a separate independent set of 794 patients. Median follow-up in this patient data base was short, 23 months. Neural Network Analysis resulted in a model that evaluated DNA flow cytometry histograms differently than conventional analysis, which categorizes the histograms by ploidy and S-phase fraction. Neural Network Analysis appeared to identify low risk and high risk subsets of patients as accurately as conventional analysis. Neural Network Analysis placed heavy emphasis on the region to the right of the diploid G2/M peak, where a subpopulation of nuclei with high DNA content is seen even in many histograms scored as diploid by conventional techniques. The number of nuclei in this region was found to be a powerful predictor of patient outcome, and multivariate analysis showed that the number of nuclei in this region and the S-phase fraction both were independently predictive of relapse. This pilot study suggests that conventional analysis (based on a mechanistic interpretation of regions in flow cytometry histograms) might be used in conjunction with and improved by pattern recognition systems or insights derived from them.
The purpose of this study was to demonstrate how a form of neural network analysis could be used to perform survival analysis on censored data, and to compare neural network analysis with the most commonly used technique for this type of analysis, Cox regression. In this study computer simulated data sets were used. The underlying rules connecting prognostic information to the hazard of death were defined to allow the construction of data sets with specific realistic properties that could be used to demonstrate situations in which neural network analysis had particular strengths in comparison with Cox regression modeling. Using these simulated data sets neural network analysis could produce successful predictive models, find interactions between variables, and recognize the importance of variables that contributed to the hazard rate as a complex function of the variables value and in situations where the proportionality of hazards assumption was violated. It was also demonstrated that neural network analysis was not a 'black box', but could lead to useful insights into the roles played by different prognostic variables in determining patient outcome.
Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log kp) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r2=0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r2=0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.
The effects of two insole materials within the shoe are compared using neural network analysis. Seven male subjects without locomotor disorders walk on a treadmill at a controlled speed and cadence wearing a common shoe and no socks, under three conditions; these are two types of insole of the same thickness, and a no insole condition. Pressure-related data from under the foot, within the shoe, are obtained by the MICRO-EMED system during walking. A back-propagation neural network is trained to associate sets of pressure-related data with the insole conditions. Subsequently neural network analysis is performed to reveal the abstract rules that govern the decision-making processes within the neural network, based on the synergistic interactions between the measured variables. Data are also analysed using ANOVA. The neural network analysis finds trends in the way in which the trained neural network responds. The interpretation of those trends gives a delicate description of the dynamic behaviour of the insoles despite the fact that no significant differences are found using ANOVA. It is concluded that neural network analysis can distinguish between insole behaviour during use, even though these differences are not significantly different based on statistical tests.