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PubMed2005

Hierarchical clustering of the correlation patterns: new method of domain identification in proteins.

New method of identification of dynamical domains in proteins - Hierarchical Clustering of the Correlation Patterns (HCCP) is proposed. HCCP allows to identify the domains using single three-dimensional structure of the studied proteins and does not require any adjustable parameters that can influence the results. The method is based on hierarchical clustering performed on the matrices of correlation patterns, which are obtained by the transformation of ordinary pairwise correlation matrices. This approach allows to extract additional information from the correlation matrices, which increases reliability of domain identification. It is shown that HCCP is insensitive to small variations of the pairwise correlation matrices. Particularly it produces identical results if the data obtained for the same protein crystallized with different spatial positions of domains are used for analysis. HCCP can utilize correlation matrices obtained by any method such as normal mode or essential dynamics analysis, Gaussian network or anisotropic network models, etc. These features make HCCP an attractive method for domain identification in proteins.

Algorithms↗
PubMed2006

Quantitative trait locus analysis for obesity reveals multiple networks of interacting loci.

Obesity is a highly heritable and genetically complex trait with hundreds of potential loci identified. An intercross of 513 F2 progeny between the SM/J x NZB/BINJ inbred mouse strains was generated to identify quantitative trait loci (QTL) that are involved in the weight of four fat pads: mesenteric, inguinal, gonadal, and retroperitoneal. Sex and lean body weight were treated as covariates in the analysis of these fat pads. This analysis uncoupled genetic effects related to overall body size from those influencing the adiposity of a mouse. We identified multiple significant QTL. QTL alleles associated with increased lean body weight and individual fat pad weights are contributed by the NZB background. Adiposity loci are distinct from these body size QTLs and high-adiposity alleles are contributed by the SM background. An extended network of epistatic QTL is also observed. A QTL on Chr 19 is the center of a network of eight interacting QTL, Chr 4 is the center of six, and Chr 17 the center of four interacting QTL. We conclude that interacting networks of multiple genes characterize the regulation of fat pad depots and body weight. Haplotype patterns and a literature-driven approach were used to generate hypotheses regarding the identity of the genes and pathways underlying the QTL.

Animals↗
PubMed2005

A novel method for generation of signature networks as biomarkers from complex high throughput data.

Traditionally, gene signatures are statistically deduced from large gene expression and proteomics datasets and have been applied as an experimental molecular diagnostic technique that is sensitive to experimental design and statistical treatment. We have developed and applied the approach of "signature networks" which overcomes some of the drawbacks of clustering methods. We have demonstrated signature network assembly, functional analysis and logical operations on the networks that can be generated. In addition, we have used this technique in a proof of concept study to compare the effect of differential drug treatment using 4-hydroxytamoxifen and estrogen on the MCF-7 breast cancer cell line from a previously published study. We have shown that the two compounds can be differentiated by the networks of interacting genes. Both networks consist of a core module of genes including c-Fos as part of c-Fos/c-Jun heterodimer and c-Myc which is clearly visible. Using algorithms in our MetaCore software we are able to subtract the 4-hydroxytamoxifen and estrogen networks to further understand differences between these two treatments and show that the estrogen network is assembled around the core with other modules essential for all phases of the cell cycle. For example, Cyclin D1 is present in networks for the estrogen treated cells from two separate studies. These signature networks represent an approach to identify biomarkers and a general approach for discovering new relationships in complex high throughput toxicology data.

Algorithms↗
PubMed2006

Cytomics - importance of multimodal analysis of cell function and proliferation in oncology.

Cancer is a highly complex and heterogeneous disease involving a succession of genetic changes (frequently caused or accompanied by exogenous trauma), and resulting in a molecular phenotype that in turn results in a malignant specification. The development of malignancy has been described as a multistep process involving self-sufficiency in growth signals, insensitivity to antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and finally tissue invasion and metastasis. The quantitative analysis of networking molecules within the cells might be applied to understand native-state tissue signalling biology, complex drug actions and dysfunctional signalling in transformed cells, that is, in cancer cells. High-content and high-throughput single-cell analysis can lead to systems biology and cytomics. The application of cytomics in cancer research and diagnostics is very broad, ranging from the better understanding of the tumour cell biology to the identification of residual tumour cells after treatment, to drug discovery. The ultimate goal is to pinpoint in detail these processes on the molecular, cellular and tissue level. A comprehensive knowledge of these will require tissue analysis, which is multiplex and functional; thus, vast amounts of data are being collected from current genomic and proteomic platforms for integration and interpretation as well as for new varieties of updated cytomics technology. This overview will briefly highlight the most important aspects of this continuously developing field.

Cell Division↗
PubMed1999

Assessing swine thermal comfort by image analysis of postural behaviors.

Postural behavior is an integral response of animals to complex environmental factors. Huddling, nearly contacting one another on the side, and spreading are common postural behaviors of group-housed animals undergoing cold, comfortable, and warm/hot sensations, respectively. These postural patterns have been routinely used by animal caretakers to assess thermal comfort of the animals and to make according adjustment on the environmental settings or management schemes. This manual adjustment approach, however, has the inherent limitations of daily discontinuity and inconsistency between caretakers in interpretation of the animal comfort behavior. The goal of this project was to explore a novel, automated image analysis system that would assess the thermal comfort of swine and make proper environmental adjustments to enhance animal wellbeing and production efficiency. This paper describes the progress and on-going work toward the achievement of our proposed goal. The feasibility of classifying the thermal comfort state of young pigs by neural network (NN) analysis of their postural images was first examined. It included exploration of using certain feature selections of the postural behavioral images as the input to a three-layer NN that was trained to classify the corresponding thermal comfort state as being cold, comfortable, or warm. The image feature selections, a critical step for the classification, examined in this study included Fourier coefficient (FC), moment (M), perimeter and area (P&A), and combination of M and P&A of the processed binary postural images. The result was positive, with the combination of M and P&A as the input feature to the NN yielding the highest correct classification rate. Subsequent work included the development of hardware and computational algorithms that enable automatic image segmentation, motion detection, and the selection of the behavioral images suitable for use in the classification. Work is in progress to quantify the relationships of postural behavior and physiological responses of pigs using thermographs. The results are expected to facilitate objective training of NN, hence improving the accuracy of the postural image-based assessment of the thermal comfort state. Work is also in progress to implement the analysis and assessment algorithms into computer codes for real-time application.

Animal Welfare↗
PubMed2007

Analysis of alpha-synuclein, dopamine and parkin pathways in neuropathologically confirmed parkinsonian nigra.

The identification of mutations that cause familial Parkinson's disease (PD) provides a framework for studies into pathways that may be perturbed also in the far more common, non-familial form of the disorder. Following this hypothesis, we have examined the gene regulatory network that links alpha-synuclein and parkin pathways with dopamine metabolism in neuropathologically verified cases of sporadic PD. By means of an in silico approach using a database of eukaryotic molecular interactions and a whole genome transcriptome dataset validated by qRT-PCR and histological methods, we found parkin and functionally associated genes to be up-regulated in the lateral substantia nigra (SN). In contrast, alpha-synuclein and ubiquitin carboxyl-terminal hydrolase L1 (UCHL1) gene expression levels were significantly reduced in both the lateral and medial SN in PD. Gene expression for Septin 4, a member of the GTP-binding protein family involved in alpha-synuclein metabolism was elevated in the lateral parkinsonian SN. Additionally, catalase and mitogen-activated protein kinase 8 and poly(ADP-ribose) polymerase family member 1 (PARP1) known to function in DNA repair and cell death induction, all members of the dopamine synthesis pathway, were up-regulated in the lateral SN. In contrast, two additional PD-linked genes, glucocerebrosidase and nuclear receptor subfamily 4, group A, member 2 (NR4A2) showed reduced expression. We show that in sporadic PD, parkin, alpha-synuclein and dopamine pathways are co-deregulated. Alpha-synuclein is a member of all three gene regulatory networks. Our analysis results support the view that alpha-synuclein has a central role in the familial as well as the non-familial form of the disease and provide steps towards a pathway definition of PD.

Adult↗
PubMed2001

[Nondestructive quantitative analysis of Paracetamoli powder pharmaceutical by artificial neural network and near-infrared spectroscopy].

The application of artificial neural network for pharmaceutical nondestructive quantitative analysis were investigated. Real data set from near infrared reflectance spectra of Paracetamoli powder pharmaceutical were used to build up artificial network to predict unknown samples. The parameters affecting network were discussed. A new network evaluation criterion, the degree of approximation, was employed. Owing to good nonlinear multivariate calibration nature of ANN, the predicted results was reliable.

Acetaminophen↗
PubMed1998

Frequency control in synchronized networks of inhibitory neurons.

We analyze the control of frequency for a synchronized inhibitory neuronal network. The analysis is done for a reduced membrane model with a biophysically based synaptic influence. We argue that such a reduced model can quantitatively capture the frequency behavior of a larger class of neuronal models. We show that in different parameter regimes, the network frequency depends in different ways on the intrinsic and synaptic time constants. Only in one portion of the parameter space, called phasic, is the network period proportional to the synaptic decay time. These results are discussed in connection with previous work of the authors, which showed that for mildly heterogeneous networks, the synchrony breaks down, but coherence is preserved much more for systems in the phasic regime than in the other regimes. These results imply that for mildly heterogeneous networks, the existence of a coherent rhythm implies a linear dependence of the network period on synaptic decay time and a much weaker dependence on the drive to the cells. We give experimental evidence for this conclusion.

Action Potentials↗
PubMed2004

Conceptual bases of Christian, faith-based substance abuse rehabilitation programs: qualitative analysis of staff interviews.

Faith-based substance abuse rehabilitation programs provide residential treatment for many substance abusers. To determine key governing concepts of such programs, we conducted semi-structured interviews with sample of eleven clinical and administrative staff referred to us by program directors at six, Evangelical Christian, faith-based, residential rehabilitation programs representing two large, nationwide networks. Qualitative analysis using grounded theory methods examined how spirituality is incorporated into treatment and elicited key theories of addiction and recovery. Although containing comprehensive secular components, the core activities are strongly rooted in a Christian belief system that informs their understanding of addiction and recovery and drives the treatment format. These governing conceptions, that addiction stems from attempts to fill a spiritual void through substance use and recovery through salvation and a long-term relationship with God, provide an explicit, theory-driven model upon which they base their core treatment activities. Knowledge of these core concepts and practices should be helpful to clinicians in considering referrals to faith-based recovery programs.

Attitude↗
PubMed1998

Improved distance analysis in RNA using network-editing techniques for overcoming errors due to spin diffusion.

Multispin magnetization transfer, or spin diffusion, is a significant source of error in NOESY-derived distance measurements for the determination of nucleic acid solution structures. The BD-NOESY and CBD-NOESY experiments, which allow the measurement of interproton distances with greatly reduced contributions from spin diffusion, have been adapted to structural analysis in RNA oligonucleotides. The techniques are applied to a lead-dependent ribozyme (LZ2). We demonstrate the measurement of both aromatic proton-aromatic proton NOEs free of spin diffusion involving the intervening ribose moieties and aromatic proton-ribose proton NOEs free of the efficient cross-relaxation within the ribose ring. In LZ2, the accuracy and precision of the resulting distances are significantly improved. We also find that, by allowing the use of longer mixing times with greater sensitivity, the experimental attenuation of spin diffusion in RNA increases the distance range of interactions that can be analyzed. This effect permits measurement of important long-range distances in LZ2 that are not accessible with standard techniques. Thus, these techniques allow the simultaneous optimization of the number, accuracy, and precision of distance constraints used for RNA structure determinations.

Base Sequence↗
PubMed2005

Fos expression in the vestibular brainstem: what one marker can tell us about the network.

Fos inducible transcription factor expression in rodent brains (rats and gerbils) during manipulations of vestibular input is reviewed. Stimuli included centripetal hypergravity, unilateral labyrinth lesion or semicircular canal plugging, rotational axis cross-coupling (Coriolis forces), high and low rotational vestibulo-ocular reflex gain adaptation, translabyrinth galvanic stimulation, pharmacological manipulation, and combinations thereof. Each type of stimulation elicited unique but partially redundant response patterns in the vestibulo-olivo-cerebellar (VOC) network that reflect the origin and interaction of the labyrinth inputs. On the basis of these patterns, a trained observer can predict what the animal experienced during testing; the patterns of VOC Fos expression reveal a trace of recent genomic activity. Based on principal component analysis, VOC network modules associated with lesion recovery, spatial representation and the calibration of gravity, and optokinetic influences are proposed. Probable and possible gene targets of the Fos protein are also reviewed.

Animals↗
PubMed2001

Characterization of acute neurotoxic effects of trimethylolpropane phosphate via neuronal network biosensors.

We have utilized cultured neuronal networks grown on microelectrode arrays to demonstrate rapid, reliable detection of a toxic compound, trimethylolpropane phosphate (TMPP). Initial experiments, which were performed blind, demonstrated rapid classification of the compound as a convulsant, a finding consistent with previous whole animal neurobehavioral studies. TMPP (2-200 microM) reorganized network spike activity into synchronous, quasi-periodic burst episodes. Integrated burst amplitudes invariably increased, reflecting higher spike frequencies within each burst. The variability of network burst parameters, quantified as coefficients of variation (CVs), was decreased. Mean CVs for burst duration, interburst interval, and burst rate were lowered by 42+/-13, 58+/-5.5, and 62+/-1.8%, respectively (mean+/-SEM, n=8 cultures, 197 channels). These changes in network activity paralleled the effects induced by bicuculline, a known disinhibitory and seizure-inducing drug, and confirmed classification of TMPP as a potential epileptogenic compound. Simple pharmacological tests permit exploration of mechanisms underlying observed activity shifts. The EC(50) for GABA inhibition of network activity was increased from 2.8 to 7.0 microM by 20 microM TMPP and to 20.5 microM by 200 microM TMPP. Parallel dose-response curves suggest that TMPP acts by a competitive antagonism of GABA inhibition, and are consistent with reported patch-clamp analysis of TMPP-induced reduction of inhibitory postsynaptic current amplitudes. The potency of TMPP in generating epileptiform activity in vitro was comparable to concentrations reported for in vivo studies. TMPP and bicuculline produced both increases and decreases in burst rate depending on native spontaneous bursting levels. These results demonstrate a need for multivariate analysis of network activity changes to yield accurate predictions of compound effects.

Action Potentials↗
PubMed2005

Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map).

MOTIVATION: Many genomes have been completely sequenced. However, detecting and analyzing their protein-protein interactions by experimental methods such as co-immunoprecipitation, tandem affinity purification and Y2H is not as fast as genome sequencing. Therefore, a computational prediction method based on the known protein structural interactions will be useful to analyze large-scale protein-protein interaction rules within and among complete genomes. RESULTS: We confirmed that all the predicted protein family interactomes (the full set of protein family interactions within a proteome) of 146 species are scale-free networks, and they share a small core network comprising 36 protein families related to indispensable cellular functions. We found two fundamental differences among prokaryotic and eukaryotic interactomes: (1) eukarya had significantly more hub families than archaea and bacteria and (2) certain special hub families determined the topology of the eukaryotic interactomes. Our comparative analysis suggests that a very small number of expansive protein families led to the evolution of interactomes and seemed to have played a key role in species diversification. SUPPLEMENTARY INFORMATION: http://interactomics.org.

Algorithms↗
PubMed2025

FuNTB: a functional network clustering tool for the analysis of genome-wide genetic variants in Mycobacterium tuberculosis.

MOTIVATION: Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), still claims around 1.25 million lives each year. The growing threat of drug resistance-often driven by single‑nucleotide polymorphisms (SNPs) in Mtb genomes underscores the need for high‑quality genomic data and powerful bioinformatics tools. We present FuNTB, a python‑based pipeline that detects non‑synonymous SNPs in Mtb and builds functional network clusters to reveal genotype-phenotype relationships. RESULTS: FuNTB profiles non‑synonymous SNPs at the gene level across user‑defined phenotypes, pinpointing both shared and unique mutations. It ingests annotated Variant Call Format (VCF) files or MTBseq outputs and merges them with clinical metadata to produce network‑XML files compatible with Cytoscape and Gephi. When applied to the CRyPTIC Mtb collection, FuNTB rapidly recovered established resistance genes and surfaced novel candidates, validating its utility for mapping genotype-phenotype associations. AVAILABILITY AND IMPLEMENTATION: FuNTB is implemented in Python 3.8+ and is freely available under the MIT license at https://doi.org/10.5281/zenodo.15399917.

Mycobacterium tuberculosis↗
PubMed1996

Classifying individuals among infra-specific taxa using microsatellite data and neural networks.

The method of neural networks was tested for its ability to assign individuals on the basis of their multilocus genotypes, using a data collection of 430 honeybees and 8 microsatellite loci. This data set includes various taxonomical levels (populations within the same subspecies, various subspecies belonging to the same evolutionary lineage, and the 3 lineages of the species). Qualitative genotypic data have been submitted to 2 types of transformation (simple coding and coding plus factorial correspondence analysis), and they have been partitioned in 2 sets, a training set of 300 individuals and a testing set of 103 individuals. Two procedures ("leave one out" and "hold out") were applied to evaluate the quality of prediction. Compared to discriminant analysis, neural networks performed better in terms of correctly classified individuals at any taxonomical level. For instance, with the simple coding and the hold out procedure, the proportions of correctly assigned individuals from the testing set were 66.2%, 82.3% and 100% at the populations, subspecies and lineage level, respectively. The potential use of neural networks in populations genetics is discussed.

Animals↗
PubMed2003

Data mining and computationally intensive methods: summary of Group 7 contributions to Genetic Analysis Workshop 13.

The Framingham Heart Study data, as well as a related simulated data set, were generously provided to the participants of the Genetic Analysis Workshop 13 in order that newly developed and emerging statistical methodologies could be tested on that well-characterized data set. The impetus driving the development of novel methods is to elucidate the contributions of genes, environment, and interactions between and among them, as well as to allow comparison between and validation of methods. The seven papers that comprise this group used data-mining methodologies (tree-based methods, neural networks, discriminant analysis, and Bayesian variable selection) in an attempt to identify the underlying genetics of cardiovascular disease and related traits in the presence of environmental and genetic covariates. Data-mining strategies are gaining popularity because they are extremely flexible and may have greater efficiency and potential in identifying the factors involved in complex disorders. While the methods grouped together here constitute a diverse collection, some papers asked similar questions with very different methods, while others used the same underlying methodology to ask very different questions. This paper briefly describes the data-mining methodologies applied to the Genetic Analysis Workshop 13 data sets and the results of those investigations.

Bayes Theorem↗
PubMedDate not supplied

Data mining techniques applied to medical information.

Knowledge discovery from the dramatically increased data of an auto-stored medical information system is still in its infancy. The purpose of this study is to use widely available and easily operated techniques that can satisfy general users in extracting specific knowledge to make the medical information system more functional. Data mining techniques, including data visualisation, correlation analysis, discriminant analysis, and neural networks supervised classification, were applied to heart disease databases. These techniques can help to identify high risk patients, define the most important factors (variables) in heart disease, and build a multivariate relationship model to show the relationship between any two variables in a way that such relationships are easy to view. Simple visualization techniques were utilised to construct this model, which corresponds with current medical knowledge. Two nonparametric (distribution assumption free) classification tools were employed to identify high risk heart disease patients. Both the neural networks supervised classification methods and the discriminant analysis method produced reliable classification rates for heart disease patients. However, neural networks yielded a higher percentage of correct classifications (averaging 89%) than discriminant analysis (79%). Data visualisation and correlation analysis resulted in similar conclusions regarding the most important factors in heart disease. These data mining tools provide simple and effective methods of extracting knowledge from general medical information. The treatment of missing data is also discussed.

Computer Graphics↗
PubMed2026

Exploring the mechanism of Acanthopanax in treating vertigo: A network pharmacology and molecular docking study.

Acanthopanax has therapeutic efficacy against vertigo; however, the underlying mechanism remains unclear. This study aimed to elucidate the mechanism by which Acanthopanax treats vertigo through integrated network pharmacology and molecular docking techniques, and retrieved all target genes of Acanthopanax for vertigo treatment from July to October 2025. Vertigo-related target genes were subsequently identified from public databases, including GeneCards and Online Mendelian Inheritance in Man. The intersection between Acanthopanax-derived targets and vertigo-related targets was analyzed to identify candidate target genes. Using the STRING platform, we constructed protein-protein interaction networks for the identified candidate targets and mined the core functional modules within these networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on candidate targets via the clusterProfiler package. A carp bile poisoning-liver injury target-pathway network was constructed via Cytoscape 3.8.2 software, network topology analysis was conducted, and the core components and targets were screened. The results found that A total of 295 candidate targets for the treatment of vertigo caused by Eleutherococcus senticosus were identified. Pathway enrichment analysis revealed that Eleutherococcus senticosus treatment for vertigo may be closely associated with pathways related to IL-17, TNF, phosphoinositide 3-kinase (PI3K)-Akt, p53, HIF-1, and Forkhead box O signaling. The core targets for the treatment of A. senticosus vertigo include TP53, AKT1, STAT3, TNF, and JUN. Network pharmacology and molecular docking studies suggest that A. senticosus may treat vertigo by regulating targets such as JUN, TNF, AKT1, STAT3, and STAT3 through pathways such as the IL-17, TNF, phosphoinositide 3-kinase-Akt, p53, HIF-1, and Forkhead box O signaling pathways. These mechanisms warrant further investigation in future o and in vitro studies.

Molecular Docking Simulation↗