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PubMed1981

Toward a resolution of methodological dilemmas in network mapping.

In mapping social networks, investigators have confronted the following choices in the selection of an instrument to assess networks: (1) qualitative versus quantitative approaches; (2) subjective versus objective criteria; (3) synchronic versus diachronic descriptions. Evolving out of fieldwork with discharged mental patients, the Network Analysis Profile was designed to resolve these methodological dilemmas. Although it has not been completely successful in meeting this goal, the Network Analysis Profile offers distinct advantages over other instruments. A description and an illustration of its use are provided.

Halfway Houses↗
PubMed2006

Metabolic network driven analysis of genome-wide transcription data from Aspergillus nidulans.

BACKGROUND: Aspergillus nidulans (the asexual form of Emericella nidulans) is a model organism for aspergilli, which are an important group of filamentous fungi that encompasses human and plant pathogens as well as industrial cell factories. Aspergilli have a highly diversified metabolism and, because of their medical, agricultural and biotechnological importance, it would be valuable to have an understanding of how their metabolism is regulated. We therefore conducted a genome-wide transcription analysis of A. nidulans grown on three different carbon sources (glucose, glycerol, and ethanol) with the objective of identifying global regulatory structures. Furthermore, we reconstructed the complete metabolic network of this organism, which resulted in linking 666 genes to metabolic functions, as well as assigning metabolic roles to 472 genes that were previously uncharacterized. RESULTS: Through combination of the reconstructed metabolic network and the transcription data, we identified subnetwork structures that pointed to coordinated regulation of genes that are involved in many different parts of the metabolism. Thus, for a shift from glucose to ethanol, we identified coordinated regulation of the complete pathway for oxidation of ethanol, as well as upregulation of gluconeogenesis and downregulation of glycolysis and the pentose phosphate pathway. Furthermore, on change in carbon source from glucose to ethanol, the cells shift from using the pentose phosphate pathway as the major source of NADPH (nicotinamide adenine dinucleotide phosphatase, reduced form) for biosynthesis to use of the malic enzyme. CONCLUSION: Our analysis indicates that some of the genes are regulated by common transcription factors, making it possible to establish new putative links between known transcription factors and genes through clustering.

Aspergillus nidulans↗
PubMed2005

Reconstruction of cellular signalling networks and analysis of their properties.

The study of cellular signalling over the past 20 years and the advent of high-throughput technologies are enabling the reconstruction of large-scale signalling networks. After careful reconstruction of signalling networks, their properties must be described within an integrative framework that accounts for the complexity of the cellular signalling network and that is amenable to quantitative modelling.

Animals↗
PubMed2006

A topology-constrained distance network algorithm for protein structure determination from NOESY data.

This article formulates the multidimensional nuclear Overhauser effect spectroscopy (NOESY) interpretation problem using graph theory and presents a novel, bottom-up, topology-constrained distance network analysis algorithm for NOESY cross peak interpretation using assigned resonances. AutoStructure is a software suite that implements this topology-constrained distance network analysis algorithm and iteratively generates structures using the three-dimensional (3D) protein structure calculation programs XPLOR/CNS or DYANA. The minimum input for AutoStructure includes the amino acid sequence, a list of resonance assignments, and lists of 2D, 3D, and/or 4D-NOESY cross peaks. AutoStructure can also analyze homodimeric proteins when X-filtered NOESY experiments are available. The quality of input data and final 3D structures is evaluated using recall, precision, and F-measure (RPF) scores, a statistical measure of goodness of fit with the input data. AutoStructure has been tested on three protein NMR data sets for which high-quality structures have previously been solved by an expert, and yields comparable high-quality distance constraint lists and 3D protein structures in hours. We also compare several protein structures determined using AutoStructure with corresponding homologous proteins determined with other independent methods. The program has been used in more than two dozen protein structure determinations, several of which have already been published.

Algorithms↗
PubMed1981

Measuring social interaction of the urban elderly: a methodological synthesis.

In general, the gerontological literature has characterized the inner-city elderly, especially those aged residing in single room occupancy hotels, as "isolates" or "loners". However, it is proposed that the notion of isolation is largely a myth and that many studies have been hampered by inadequate research instruments. This paper illustrates: 1) The severe limitations of the traditional measures of determining sociability; 2) How social network analysis can overcome many of the deficiencies of other methods; and 3) How a synthesis of the anthropological and sociological approaches to network analysis can optimize data collection and provide culturally meaningful information.

Aged↗
PubMed2002

Coordination among health care stakeholders: effects on state health policy choices.

This article proposes that coordination among health stakeholders is reflected in the structural relations within state health care lobbies and treats lobbies as a mechanism of social control over the industry. This article compares the effects of structural relations of lobbies and also professional associations and economic conditions upon a regulatory typology of health policy outcomes. Results of network analysis show that special interest structures exert social control, regulatory policy reflects choices between markets and regulation and vertical industry relations, and network analysis is useful for studying these complex ideas.

Decision Making↗
PubMed1999

Neural network-based analysis of MR time series.

Clustering has been introduced to analyze fMRI data by means of partitioning data into time series of similar temporal behavior. It is hoped that one of these clusters represents a dynamic effect of interest, like functional activation. Using self-organizing maps for clustering, additional information can be obtained by ordering cluster centers on a two-dimensional projection plane. The map's capability of data visualization is used to summarize all dynamic effects of an experiment by means of data partitioning. The map does allow differently sized and populated clusters in the data by forming "superclusters" on the map. The method is introduced as a conceptual extension to clustering. Applications to fMRI and to MR mammography are discussed.

Artifacts↗
PubMed2001

Framing the nicotine debate: a cultural approach to risk.

This study examined Congressional testimony concerning regulation of tobacco advertising by 3 policy factions representing industry, government, and lay activists. On the basis of the cultural theory of risk, policy disputants were divided into entrepreneurial, bureaucratic, and egalitarian communities, each with a distinct cosmology that impedes discourse among the groups. The authors examined ways in which the 3 policy factions framed the tobacco advertising issues to see the extent to which such unique cosmologies were expressed or whether mutual frames might signal opportunities for negotiation among the interest groups. Major themes in the testimony were identified through semantic network analysis and clustering of associated words that revealed discourse patterns peculiar to each group and reflective of their cultural biases toward health risk. Semantic network analysis can be a tool to clarify these presuppositions and unmask relations among factions, thereby bridging policy solutions across interest groups.

Advertising↗
PubMed2005

A network-based analysis of systemic inflammation in humans.

Oligonucleotide and complementary DNA microarrays are being used to subclassify histologically similar tumours, monitor disease progress, and individualize treatment regimens. However, extracting new biological insight from high-throughput genomic studies of human diseases is a challenge, limited by difficulties in recognizing and evaluating relevant biological processes from huge quantities of experimental data. Here we present a structured network knowledge-base approach to analyse genome-wide transcriptional responses in the context of known functional interrelationships among proteins, small molecules and phenotypes. This approach was used to analyse changes in blood leukocyte gene expression patterns in human subjects receiving an inflammatory stimulus (bacterial endotoxin). We explore the known genome-wide interaction network to identify significant functional modules perturbed in response to this stimulus. Our analysis reveals that the human blood leukocyte response to acute systemic inflammation includes the transient dysregulation of leukocyte bioenergetics and modulation of translational machinery. These findings provide insight into the regulation of global leukocyte activities as they relate to innate immune system tolerance and increased susceptibility to infection in humans.

Acute Disease↗
PubMed2006

Network-level analysis of metabolic regulation in the human red blood cell using random sampling and singular value decomposition.

BACKGROUND: Extreme pathways (ExPas) have been shown to be valuable for studying the functions and capabilities of metabolic networks through characterization of the null space of the stoichiometric matrix (S). Singular value decomposition (SVD) of the ExPa matrix P has previously been used to characterize the metabolic regulatory problem in the human red blood cell (hRBC) from a network perspective. The calculation of ExPas is NP-hard, and for genome-scale networks the computation of ExPas has proven to be infeasible. Therefore an alternative approach is needed to reveal regulatory properties of steady state solution spaces of genome-scale stoichiometric matrices. RESULTS: We show that the SVD of a matrix (W) formed of random samples from the steady-state solution space of the hRBC metabolic network gives similar insights into the regulatory properties of the network as was obtained with SVD of P. This new approach has two main advantages. First, it works with a direct representation of the shape of the metabolic solution space without the confounding factor of a non-uniform distribution of the extreme pathways and second, the SVD procedure can be applied to a very large number of samples, such as will be produced from genome-scale networks. CONCLUSION: These results show that we are now in a position to study the network aspects of the regulatory problem in genome-scale metabolic networks through the use of random sampling.

Algorithms↗
PubMed1996

Neural network differentiation of optic neuritis and anterior ischaemic optic neuropathy.

AIMS: The efficacy of an artificial intelligence technique, neural network analysis, was examined in differentiating two optic neuropathies with overlapping clinical profiles-idiopathic optic neuritis (ON) and non-arteritic anterior ischaemic optic neuropathy (AION). METHODS: A neural network was trained with data from 116 patients with 'gold standard' diagnoses of ON or AION. It was then tested with data from 128 patients with presumed ON or AION, and the correlation of the network's diagnosis with that of expert clinicians tabulated. RESULTS: The network agreed with the clinicians on 97.8% (88 of 90) of the patients with presumed ON and 94.7% (36 of 38) of the patients with presumed AION. Youth, female sex, better initial acuity, a central scotoma, subsequent improvement in acuity, or progressive disease biased the network towards a diagnosis of ON, while advanced age, male sex, presence of hypertension, poor initial acuity, an altitudinal field defect, disc oedema, or less improvement in acuity biased the network towards a diagnosis of AION. CONCLUSION: Neural network analysis is a useful technique for classification of optic neuropathies, particularly where there is overlap of clinical findings.

Adult↗
PubMed2004

Assessing interorganizational networks as a dimension of community capacity: illustrations from a community intervention to prevent lead poisoning.

Network analysis is often cited as a method for assessing collaboration among organizations as an indicator of community capacity. The purpose of this study was to (1) document patterns of collaboration in organizational networks related to lead poisoning prevention in a Native American community and (2) examine measurement issues in using organizational network analysis to assess community capacity. Interviews were conducted with representatives from 22 tribes, government agencies, schools, and community-based organizations in northeastern Oklahoma. Intensity, density, and reliability were assessed for several stages of collaboration. Intensity and density were greater for similar types of organizations than for the network as a whole and decreased as stage of collaboration increased. Network data were more reliable when responses were dichotomized than when intensities were compared. Mean reliability scores across two respondents from the same organization ranged from 60% to 90%. Results from network studies may help communities learn how to strengthen organizational networks to enhance community capacity.

Community Networks↗
PubMed2025

Target and biomarker exploration portal for drug discovery.

MOTIVATION: The discovery of novel drug targets and precision biomarkers remains a major challenge in drug development, with traditional differential expression analysis often overlooking key regulatory proteins. Here, we present a novel, web-based bioinformatics tool, the Target and Biomarker Exploration Portal (TBEP), designed to accelerate the drug discovery process by integrating large-scale biomedical data with network analysis techniques. RESULTS: TBEP harnesses machine-learning approaches to mine and combine multimodal datasets, including human genetics, functional genomics, and protein-protein interaction networks, to decode causal disease mechanisms and uncover novel therapeutic targets and precision biomarkers for specific phenotypes. A unique feature of the tool is its ability to process large-scale data in real-time, facilitated by an efficient cloud-based architecture. Additionally, the tool incorporates an integrated large language model (LLM), which assists researchers in exploring and interpreting complex biological relationships within the generated networks and multi-omics data using natural language (English). By offering an intuitive, interactive interface, the LLM enhances the exploration of biological insights, making it easier for scientists to derive actionable conclusions. This powerful integration of network analysis, multi-omics data, and LLM provides a robust framework for accelerating the identification of novel drug targets. AVAILABILITY AND IMPLEMENTATION: The tool is publicly available at https://tbep.missouri.edu. The source code, documentation and installation instructions are available at GitHub repository: https://github.com/mizzoudbl/tbep.

Drug Discovery↗
PubMed2004

Using social networks to understand and prevent substance use: a transdisciplinary perspective.

We review findings from research on smoking, alcohol, and other drug use, which show that the network approaCh is instructive for understanding social influences on substance use. A hypothetical network is used throughout to illustrate different network findings and provide a short glossary of terms. We then describe how network analysis can be used to design more effective prevention programs and to monitor and evaluate these programs. The article closes with a discussion of the inherent transdisciplinarity of social network analysis.

Biomedical Research↗
PubMed2025

Identification and validation of the important role of KIF11 in the development and progression of endometrial cancer.

BACKGROUND: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies. METHODS: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC. Functional enrichment analysis of the DEGs was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. DEGs were further screened for hub genes through protein-protein interaction (PPI) network analysis and machine learning. The role of the hub gene KIF11 in EC was analyzed using clinical data from the TCGA database. The expression of KIF11 in EC was subsequently validated in clinical samples. In vitro experiments were utilized to evaluate the effects of KIF11 on biological functions such as proliferation, migration, apoptosis, and the cell cycle in endometrial cancer cells. RESULTS: A total of 877 DEGs, which are widely involved in important biological processes such as cell division, tubulin binding, and the cell cycle, were identified. Through PPI network analysis and machine learning, KIF11 was selected as the hub gene for subsequent analysis and experimental validation. An analysis of TCGA data revealed that KIF11 is highly expressed in EC and is associated with tumor grade, stage, and a low survival rate. The overexpression of KIF11 in tumor tissues was further confirmed in EC patient samples. KIF11 knockdown had inhibitory effects on cell proliferation, migration and invasion. Flow cytometry analysis revealed that KIF11 knockdown induced G2/M phase arrest and promoted apoptosis in EC cells. CONCLUSION: Our study demonstrated that KIF11 was upregulated in EC and was strongly associated with a poor prognosis. Notably, we found that reduced KIF11 expression inhibited EC cell proliferation, migration and invasion. KIF11 knockdown caused more EC cells to arrest in the G2/M phase and undergo apoptosis. The findings of our study emphasized that KIF11 may be a promising prognostic biomarker and therapeutic target for EC patients.

Humans↗
PubMed2006

Patient-centeredness and timeliness in a primary care network: baseline analysis and power assessment for detection of the effects of an electronic health record.

Electronic health records are expected to improve all six dimensions of quality care identified by the Institute of Medicine (safety, timeliness, effectiveness, efficiency, equity, and patient-centeredness). HealthTexas Provider Network, the ambulatory care network affiliated with the Baylor Health Care System in Dallas-Fort Worth, Texas, is implementing a networkwide ambulatory electronic health record (AEHR). To evaluate the quality of care and financial impact of the AEHR implementation, we examined the available indicators for quantitatively measuring performance in each dimension of quality. For patient-centeredness, the primary data source available is the patient satisfaction survey. To achieve a broad view of patient-centeredness, we identified two measures of satisfaction (overall satisfaction with the physician and willingness to refer the physician) to be examined individually and used additional survey items to construct physician interaction and organizational scales. These scales showed good reliability (Cronbach alpha = 0.95 and 0.89, respectively) and predictive ability ranging from 77% to 93% when applied to the overall satisfaction measures. Data from September 2003 to June 2006 showed mean pre-AEHR implementation baseline performance of 22.9 (±3.3) on the 25-point physician interaction scale and 38.0 (±5.8) on the 45-point organizational scale; 70.9% of patients reported excellent satisfaction with their physician, and 97.6% of patients reported willingness to refer. Timeliness data were collected using the same survey. Baseline performance showed that 43.4% of patients waited <2 days between making and keeping an appointment, and 50.6% of patients waited <5 minutes past appointment time. However, 12.5% waited >30 days between making and keeping an appointment, and 14.0% waited >30 minutes past appointment time. The power to detect changes in the patient-centeredness and timeliness measures in the 3-year multiple time series evaluation of the quality and financial impact of the AEHR was investigated and showed that even small changes in these measures will be detectable.

Journal Article↗
PubMed2002

Automatic amide I frequency selection for rapid quantification of protein secondary structure from Fourier transform infrared spectra of proteins.

Here we report the development of a new neural network based approach for rapid quantification of protein secondary structure from Fourier transform infrared (FTIR) spectra of proteins. A technique for efficiently reducing the amount of spectral data by almost 90% is suggested to facilitate faster neural network analysis. Additionally, an automatic procedure is introduced for selecting only those regions within the amide I band of protein FTIR spectra, which can be best related to secondary structure contents by subsequent neural network analysis. Based on a given reference set of FTIR spectra from proteins with known secondary structure, a subset of merely 29 out of 101 amide I absorbance values could be identified, which lead to an improved prediction accuracy. The average prediction accuracy achieved for helix, sheet, turn, bend, and other is 4.96% which is better than that achieved by alternative methods that have been previously reported indicating the significant potential of this approach. Our suggested automatic amide I frequency selection procedure may be easily extended to identify promising regions from spectral data recorded by other spectroscopic techniques, like for example circular dichroism spectroscopy.

Algorithms↗