SEARCH · PubMed Health

Results for “Network inference”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25Linked to original sources

PubMed2003

Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells.

Dissection of regulatory networks that control gene transcription is one of the greatest challenges of functional genomics. Using human genomic sequences, models for binding sites of known transcription factors, and gene expression data, we demonstrate that the reverse engineering approach, which infers regulatory mechanisms from gene expression patterns, can reveal transcriptional networks in human cells. To date, such methodologies were successfully demonstrated only in prokaryotes and low eukaryotes. We developed computational methods for identifying putative binding sites of transcription factors and for evaluating the statistical significance of their prevalence in a given set of promoters. Focusing on transcriptional mechanisms that control cell cycle progression, our computational analyses revealed eight transcription factors whose binding sites are significantly overrepresented in promoters of genes whose expression is cell-cycle-dependent. The enrichment of some of these factors is specific to certain phases of the cell cycle. In addition, several pairs of these transcription factors show a significant co-occurrence rate in cell-cycle-regulated promoters. Each such pair indicates functional cooperation between its members in regulating the transcriptional program associated with cell cycle progression. The methods presented here are general and can be applied to the analysis of transcriptional networks controlling any biological process.

Binding Sites↗

PubMed2006

Lidar detection of underwater objects using a neuro-SVM-based architecture.

This paper presents a neural network architecture using a support vector machine (SVM) as an inference engine (IE) for classification of light detection and ranging (Lidar) data. Lidar data gives a sequence of laser backscatter intensities obtained from laser shots generated from an airborne object at various altitudes above the earth surface. Lidar data is pre-filtered to remove high frequency noise. As the Lidar shots are taken from above the earth surface, it has some air backscatter information, which is of no importance for detecting underwater objects. Because of these, the air backscatter information is eliminated from the data and a segment of this data is subsequently selected to extract features for classification. This is then encoded using linear predictive coding (LPC) and polynomial approximation. The coefficients thus generated are used as inputs to the two branches of a parallel neural architecture. The decisions obtained from the two branches are vector multiplied and the result is fed to an SVM-based IE that presents the final inference. Two parallel neural architectures using multilayer perception (MLP) and hybrid radial basis function (HRBF) are considered in this paper. The proposed structure fits the Lidar data classification task well due to the inherent classification efficiency of neural networks and accurate decision-making capability of SVM. A Bayesian classifier and a quadratic classifier were considered for the Lidar data classification task but they failed to offer high prediction accuracy. Furthermore, a single-layered artificial neural network (ANN) classifier was also considered and it failed to offer good accuracy. The parallel ANN architecture proposed in this paper offers high prediction accuracy (98.9%) and is found to be the most suitable architecture for the proposed task of Lidar data classification.

Algorithms↗

PubMed1996

Alteration and recovery of the spatial orientation of the collagen network of articular cartilage in adolescent rabbits following intra-articular chymopapain injection.

We have used polarized light (POL) to monitor changes in the organization of the articular cartilage collagen network and matrix proteoglycans (PGs) after intra-articular injection of chymopapain (CP). POL viewing of sirius red stained sections revealed a loss of normal birefringence suggesting an apparent collapse of the collagen network following intra-articular CP. After 21 days, knees injected with 2.0 mg CP showed no return of normal birefringence, however, normal birefringence was noted in knees injected with only 0.2 mg CP. POL viewing of toluidine blue stained sections revealed a severe loss of matrix PGs followed by PG restoration in animals injected with 0.2 mg CP. The most important inference from the data is that articular cartilage can recover from enzyme-induced alterations in the spatial collapse of its fibrillar network. This is an important finding since it has often been inferred that damage to the collagen network leads invariably to progressive articular cartilage destruction.

Animals↗

PubMed1994

Suitability of artificial neural networks for feature extraction from cardiotocogram during labour.

Fetal condition during labour is inferred from a continuous display of fetal heart rate and uterine contractions called the cardiotocogram (CTG). The CTG requires a considerable expertise for correct interpretation, which is not always available. We are developing an intelligent system to support clinical decision-making during labour. The system's performance depends on its ability to classify features from the CTG similarly to experts. Artificial neural networks (NNs) can be taught by experts for such tasks, and so may be particularly suitable. We found NNs suitable for feature extraction when the problem was reduced to small well defined tasks, and numerical algorithms were used to pre-process the raw data before application to the NNs. A NN with optimised dimensions was used in this way to classify the magnitude of decelerations, a feature clinicians find particularly difficult. The NN was compared with the algorithm used in a commercial antenatal monitor and six reviewers which included two CTG experts. The experts were consistent (89.7% and 97.0%) and agreed well with each other (81.0%), whereas the non-experts were less consistent and agreed less well. The NN agreed well with the experts (75.0% and 81.9%) but the algorithm agreed poorly (56.5% and 68.9%). It was found that the algorithm's performance could be improved (72.1% and 76.7%) when modified to use additional information. Our earlier attempts to fully classify the raw CTG using a single NN were unsuccessful because of the large number of data patterns. A simplified approach to classify the magnitude and timing of decelerations was also unsuitable when contraction data was of poor quality or absent.(ABSTRACT TRUNCATED AT 250 WORDS)

Algorithms↗

PubMed1994

The relevance of drug injectors' social and risk networks for understanding and preventing HIV infection.

Focusing on the social environment as well as the individual should both enhance our understanding of HIV transmission and assist in the development of more effective prevention programs. Networks are an important aspect of drug injectors' social environment. We distinguish between (1) risk networks (the people among whom HIV risk behaviors occur) as vectors of disease transmission, and (2) social networks (the people among whom there are social interactions with a mutual orientation to one another) as generators and disseminators of social influence. These concepts are applied to analyses of data from interviews with drug injectors in two studies. In the first study drug injectors' risk networks converge with their social networks: 70% inject or share syringes with a spouse or sex partner, a running partner, or with friends or others whom they know. Qualitative data from interviews with injectors in the second study also show that the social relationships between drug injectors and members of their risk network are often based on long-standing and multiplex relationships, such as those based on kinship, friendship, marital and sexual ties, and economic activity. In the first study the vast majority of injectors, over 90%, have social ties with non-injectors. Injectors with more frequent social contacts with non-injectors engage in lower levels of injecting risk behavior. Risk settings may function as risk networks: injectors in this study who inject at shooting galleries are more likely than those who do not to rent used syringes, borrow used syringes and inject with strangers. Since the adoption of a network approach is relatively new, a number of issues require further attention. These include: how to utilize social networks among drug injectors to reduce risk through peer pressure; how to promote risk reduction by encouraging ties between injectors and non-injectors; and how to integrate biographical and historical change into understanding network processes. Appropriate methodologies to study drug injectors' networks should be developed, including techniques to reach hidden populations, computer software for managing and analyzing network data bases, and statistical methods for drawing inferences from data gathered through dependent sampling designs.

Adult↗

PubMed2006

Microbioreactor arrays with integrated mixers and fluid injectors for high-throughput experimentation with pH and dissolved oxygen control.

We have developed an integrated array of microbioreactors, with 100 microL working volume, comprising a peristaltic oxygenating mixer and microfluidic injectors. These integrated devices were fabricated in a single chip and can provide a high oxygen transfer rate (k(L)a approximately 0.1 s(-1)) without introducing bubbles, and closed loop control over dissolved oxygen and pH (+/-0.1). The system was capable of supporting eight simultaneous Escherichia coli fermentations to cell densities greater than 13 g-dcw L(-1) (1 cm OD(650 nm) > 40). This cell density was comparable to that achieved in a 4 litre reference fermentation, conducted with the same strain, in a bench scale stirred tank bioreactor and is more than four times higher than cell densities previously achieved in microbioreactors. Bubble free oxygenation permitted near real time optical density measurements which could be used to observe subtle changes in the growth rate and infer changes in the state of microbial genetic networks. Our system provides a platform for the study of the interaction of microbial populations with different environmental conditions, which has applications in basic science and industrial bioprocess development. We leverage the advantages of microfluidic integration to deliver a disposable, parallel bioreactor in a single chip, rather than robotically multiplexing independent bioreactors, which opens a new avenue for scaling small scale bioreactor arrays with the capabilities of bench scale stirred tank reactors.

Ammonium Hydroxide↗

PubMed2005

Geographic differentiation in the house fly estimated by microsatellite and mitochondrial variation.

Gene flow over very large geographic scales has been investigated in few species. Examples include Drosophila melanogaster, Drosophila subobscura, Drosophila simulans, and the Mediterranean fruit fly (Ceratitis capitata). The cosmopolitan house fly, a highly vagile, fecund, colonizing species offers an additional exemplar. Genotypes at seven microsatellite loci were scored in 14 widely separated natural house fly populations from the Nearctic, neotropics, Afrotropics, Palearctic, and Asia. Allelic diversities and heterozygosities differed significantly among populations. Averaged over all populations, Weir and Cockerham's theta = 0.13 and RST = 0.20. Pairwise genetic distance measures were uncorrelated with geographic distance. Microsatellite frequencies were compared with mitochondrial data from 13 of the same populations in which theta = 0.35 and Nei's GST = 0.72. Mitochondrial variation indicated up to threefold greater indices of genetic differentiation than the microsatellites. We were unable to draw any biogeographical inferences from these results or from tree or network topologies constructed from the genetic data. It is likely that high microsatellite diversities, mutation rates, and homoplasy greatly compromised their usefulness in estimating gene flow. House fly colonization dynamics include a large number of primary and secondary colonizations coupled with substantial genetic drift, but no detectable bottlenecks.

Animals↗

PubMed2005

Systems biology for cancer.

PURPOSE OF REVIEW: Significant insight can be gained into complex biologic mechanisms of cancer via a combined computational and experimental systems biology approach. This review highlights some of the major systems biology efforts that were applied to cancer in the past year. RECENT FINDINGS: Two main approaches to computational systems biology are discussed: mechanistic dynamical simulations and inferential data mining. Significant developments have occurred in both areas. For example, mechanistic simulations of the EGFR pathway are promoting understanding of cancer, and Bayesian inference approaches allow for the reconstruction of regulatory networks. In addition, the article reports on advancements in experimental systems biology for determining protein-protein interactions and quantifying protein expression to generate the necessary data for computational modeling and inferential data mining. Emerging approaches will further improve the ability to bridge the gap between in vitro systems and in vivo human biology. Technologies paving the way include in vitro models that better reflect in vivo tumors, microfabricated devices of human physiology, and improved animal models. SUMMARY: An important challenge facing the field is how better to translate in vitro discoveries to the clinic. Computational systems biology approaches that use omic data to predict biology along with novel experimental systems that better represent human in vivo biology will prove useful in bridging this gap. Although still early, the potential application of systems biology and the future evolution of the field will significantly affect understanding of cancer disease mechanisms and the ability to devise effective therapeutics.

Computational Biology↗

PubMed1982

Glutaraldehyde effect on the osmotic fragility and rate of haemolysis of human erythrocytes: a kinetic study.

The effect of glutaraldehyde treatment on the osmotic fragility and rate of haemolysis of human erythrocytes was studied in the temperature domain 22-42 degrees C at different aldehyde concentrations from 0.010 to 0.040% (w/v). The osmotic fragility linearly decreases at increasing temperatures for all glutaraldehyde concentrations with approximately the same slope as for the untreated erythrocytes. Rather similar effects are produced on the rate of haemolysis. It was not possible to define unique activation energies. The maximal haemolysis becomes smaller after the treatment with increasing concentrations of glutaraldehyde but more erythrocytes are no longer able to tolerate normally non-haemolysing osmotic stretching, this suggesting an artificial "ageing" of erythrocytes. The formation of a membrane protein skeleton as a result of cross-linking is inferred, while the changes in the cytoskeletal spectrin network seem to be less important.

Aldehydes↗

PubMed2004

Feature inference and the causal structure of categories.

The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian networks. In five experiments we found that Bayes' nets generally predicted participants' feature inferences quite well. However, we also observed a pervasive violation of one of the defining principles of Bayes' nets-the causal Markov condition-because the presence of characteristic features invariably led participants to infer yet another characteristic feature. We argue that this effect arises from a domain-general bias to assume the presence of underlying mechanisms associated with the category. Specifically, people take an exemplar to be a "well functioning" category member when it has most or all of the category's characteristic features, and thus are likely to infer a characteristic value on an unobserved dimension.

Analysis of Variance↗

PubMed2006

An artificial neural network approach to the drug efficacy of interferon treatments.

INTRODUCTION: Interferon taken alone or in combination with ribavirin can be used for the treatment of persons with chronic hepatitis C. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the treatments. In this work, our goal is to develop a prediction model resulting from the analysis of chronic hepatitis C patients' single nucleotide polymorphisms, viral genotype, viral load, age and gender, to predict the responsiveness of interferon combination treatment. MATERIALS AND METHODS: We collected blood samples from 523 chronic hepatitis C patients that had received interferon and ribavirin combination therapy. Based on the current treatment strategy for chronic hepatitis C patients, we focused our search for candidate genes involved in pathways related to interferon signaling and immunomodulation. With artificial neural network algorithms, we then developed pattern recognition methodologies to achieve predictions among the patients. The artificial neural network model was trained by an algorithm with an adaptive momentum and learning rate. RESULTS: There were seven single nucleotide polymorphisms selected from six candidate genes including adenosine deaminase-RNA-specific, caspase 5, interferon consensus sequence binding protein 1, interferon-induced protein 44, phosphoinositide-3-kinase catalytic gamma polypeptide and transporter 2 ATP-binding cassette subfamily B genes. We further applied the artificial neural network algorithms with these seven single nucleotide polymorphisms, viral genotype, viral load, age and gender information to build tools for predicting the responsiveness of interferon. Based on the fivefold cross-validation method to evaluate the performance, the model achieved a high success rate of prediction. CONCLUSION: We demonstrated that a trained artificial neural network model is a promising method for providing the inference from factors such as single nucleotide polymorphisms, viral genotype, viral load, age and gender to the responsiveness of interferon.

Aging↗

PubMed1991

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Algorithms↗

PubMed2002

Complex patterns of colonization and refugia revealed for European grayling Thymallus thymallus, based on complete sequencing of the mitochondrial DNA control region.

The complete mitochondrial DNA (mtDNA) control region (1043 base pairs) and 162-bp of flanking transfer RNA genes were sequenced in 316 European grayling, Thymallus thymallus, from 44 populations throughout the Western European range of the species. A total of 58 haplotypes were revealed with pairwise divergence ranging from 0.001 to 0.038. An inferred intraspecific phylogenetic tree revealed two well-supported clades within the Danube basin, one highly divergent clade in the Adriatic basin, and one large, diverse group representing most other populations. A deeply divergent haplotype fixed in the Loire basin in central France, more groups of haplotypes from distinct Danubian tributaries, and a relatively ancestral haplotype fixed in former tributaries of the Elbe in Denmark all suggest a complex pattern of interglacial and postglacial expansions originating from disjunct refugia throughout central Europe. Despite some evidence of human-mediated stock transfers, parsimony-network-based nested-clade analysis (NCA) supported specific inferences relating to corridors of postglacial expansion such as the lower Rhine (Moselle) and Elbe systems (Danish populations) serving as sources for expansion into the Baltic to the north as well as the upper Rhine and Danube to the south; and specific Rhine populations (Doller, Orbe and Reuss) serving as sources for colonization of the Rhone. The multiple divergent clades representing populations in the upper Danube, as well as the deeply divergent haplotypes found in the Adriatic and Loire basins (> 5% divergence from Asian outgroups) support the theory that European grayling have had a long history in Western Europe, pre-dating Pleistocene glacial cycles. The patterns of mtDNA divergence shown here support a perspective of rich inter- and intrabasin genetic diversity that should be protected from current trends to translocate brood stocks for rearing and release in response to declining populations, especially in southern European basins.

Animals↗

PubMed2005

GenSo-FDSS: a neural-fuzzy decision support system for pediatric ALL cancer subtype identification using gene expression data.

OBJECTIVE: Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood, representing nearly one third of all pediatric cancers. Currently, the treatment of pediatric ALL is centered on tailoring the intensity of the therapy applied to a patient's risk of relapse, which is linked to the type of leukemia the patient has. Hence, accurate and correct diagnosis of the various leukemia subtypes becomes an important first step in the treatment process. Recently, gene expression profiling using DNA microarrays has been shown to be a viable and accurate diagnostic tool to identify the known prognostically important ALL subtypes. Thus, there is currently a huge interest in developing autonomous classification systems for cancer diagnosis using gene expression data. This is to achieve an unbiased analysis of the data and also partly to handle the large amount of genetic information extracted from the DNA microarrays. METHODOLOGY: Generally, existing medical decision support systems (DSS) for cancer classification and diagnosis are based on traditional statistical methods such as Bayesian decision theory and machine learning models such as neural networks (NN) and support vector machine (SVM). Though high accuracies have been reported for these systems, they fall short on certain critical areas. These included (a) being able to present the extracted knowledge and explain the computed solutions to the users; (b) having a logical deduction process that is similar and intuitive to the human reasoning process; and (c) flexible enough to incorporate new knowledge without running the risk of eroding old but valid information. On the other hand, a neural fuzzy system, which is synthesized to emulate the human ability to learn and reason in the presence of imprecise and incomplete information, has the ability to overcome the above-mentioned shortcomings. However, existing neural fuzzy systems have their own limitations when used in the design and implementation of DSS. Hence, this paper proposed the use of a novel neural fuzzy system: the generic self-organising fuzzy neural network (GenSoFNN) with truth-value restriction (TVR) fuzzy inference, as a fuzzy DSS (denoted as GenSo-FDSS) for the classification of ALL subtypes using gene expression data. RESULTS AND CONCLUSION: The performance of the GenSo-FDSS system is encouraging when benchmarked against those of NN, SVM and the K-nearest neighbor (K-NN) classifier. On average, a classification rate of above 90% has been achieved using the GenSo-FDSS system.

Algorithms↗

PubMed2004

Extending the mutual information measure to rank inferred literature relationships.

BACKGROUND: Within the peer-reviewed literature, associations between two things are not always recognized until commonalities between them become apparent. These commonalities can provide justification for the inference of a new relationship where none was previously known, and are the basis of most observation-based hypothesis formation. It has been shown that the crux of the problem is not finding inferable associations, which are extraordinarily abundant given the scale-free networks that arise from literature-based associations, but determining which ones are informative. The Mutual Information Measure (MIM) is a well-established method to measure how informative an association is, but is limited to direct (i.e. observable) associations. RESULTS: Herein, we attempt to extend the calculation of mutual information to indirect (i.e. inferable) associations by using the MIM of shared associations. Objects of general research interest (e.g. genes, diseases, phenotypes, drugs, ontology categories) found within MEDLINE are used to create a network of associations for evaluation. CONCLUSIONS: Mutual information calculations can be effectively extended into implied relationships and a significance cutoff estimated from analysis of random word networks. Of the models tested, the shared minimum MIM (MMIM) model is found to correlate best with the observed strength and frequency of known associations. Using three test cases, the MMIM method tends to rank more specific relationships higher than counting the number of shared relationships within a network.

Humans↗

PubMed2006

Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction.

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are demonstrated to be competent when applied individually to a variety of problems. Recently, there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have been evolved. In this study, a new approach based on an adaptive neuro-fuzzy inference system (ANFIS) was presented for epileptic seizure detection. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Some conclusions concerning the impacts of features on the detection of epileptic seizures were obtained through analysis of the ANFIS. The results are highly promising, and a comparative analysis suggests that the proposed modeling approach outperforms ANN model in terms of training performances and classification accuracies. The results confirmed that the proposed ANFIS model has some potential in epileptic seizure detection. The ANFIS model achieved accuracy rates which were higher than that of the stand-alone neural network model.

Electroencephalography↗

PubMed2004

An extended transcriptional regulatory network of Escherichia coli and analysis of its hierarchical structure and network motifs.

Recent studies of genome-wide transcriptional regulatory network (TRN) revealed several intriguing structural and dynamic features of gene expression at a system level. Unfortunately, the network under study is often far from complete. A critical question is thus how much the network is incomplete and to what extent this would affect the results of analysis. Here we compare the Escherichia coli TRN built by Shen-Orr et al. (Nature Genet., 31, 64-68) with two TRNs reconstructed from RegulonDB and Ecocyc respectively and present an extended E.coli TRN by integrating information from these databases and literature. The scale of the extended TRN is about twice as large as the previous ones. The new network preserves the multi-layer hierarchical structure which we recently reported but has more layers. More global regulators are inferred. While the feed forward loop (FFL) is confirmed to be highly representative in the network, the distribution of the different types of FFLs is different from that based on the incomplete network. In contrast to the notion of motif aggregation and formation of homologous motif clusters, we found that most FFLs interact and form a giant motif cluster. Furthermore, we show that only a small portion of the genes is solely regulated by only one FFL. Many genes are regulated by two or more interacting FFLs or other more complicated network motifs together with transcriptional factors not belonging to any network motifs, thereby forming complex regulatory circuits. Overall, the extended TRN represents a more solid basis for structural and functional analysis of genome-wide gene regulation in E.coli.

Databases, Genetic↗

PubMed1997

Reasoning requirements for diagnosis of heart disease.

Over the past dozen years, the Heart Disease Program (HDP) has been developed to assist physicians in reasoning about cardiovascular disorders. Driven by several evaluations, the inference mechanism has progressed from a logic based model, to a Bayesian Probability Network (BPN) and finally a pseudo-Bayesian network with temporal and severity reasoning. Though aspects of cardiovascular reasoning are handled well by BPNs, temporal reasoning, homeostatic feedback mechanisms and effects of disease severities require additional inference strategies. This article discusses how these reasoning problems are handled, and deals with closely linked issues in building the user interface to collect detailed cardiovascular data and provide clear explanations of diagnoses.

Artificial Intelligence↗