Differential C3NET reveals disease networks of direct physical interactions.

BACKGROUND: Genes might have different gene interactions in different cell conditions, which might be mapped into different networks. Differential analysis of gene networks allows spotting condition-specific interactions that, for instance, form disease networks if the conditions are a disease, such as cancer, and normal. This could potentially allow developing better and subtly targeted drugs to cure cancer. Differential network analysis with direct physical gene interactions needs to be explored in this endeavour. RESULTS: C3NET is a recently introduced information theory based gene network inference algorithm that infers direct physical gene interactions from expression data, which was shown to give consistently higher inference performances over various networks than its competitors. In this paper, we present, DC3net, an approach to employ C3NET in inferring disease networks. We apply DC3net on a synthetic and real prostate cancer datasets, which show promising results. With loose cutoffs, we predicted 18583 interactions from tumor and normal samples in total. Although there are no reference interactions databases for the specific conditions of our samples in the literature, we found verifications for 54 of our predicted direct physical interactions from only four of the biological interaction databases. As an example, we predicted that RAD50 with TRF2 have prostate cancer specific interaction that turned out to be having validation from the literature. It is known that RAD50 complex associates with TRF2 in the S phase of cell cycle, which suggests that this predicted interaction may promote telomere maintenance in tumor cells in order to allow tumor cells to divide indefinitely. Our enrichment analysis suggests that the identified tumor specific gene interactions may be potentially important in driving the growth in prostate cancer. Additionally, we found that the highest connected subnetwork of our predicted tumor specific network is enriched for all proliferation genes, which further suggests that the genes in this network may serve in the process of oncogenesis. CONCLUSIONS: Our approach reveals disease specific interactions. It may help to make experimental follow-up studies more cost and time efficient by prioritizing disease relevant parts of the global gene network.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Inferring the conservative causal core of gene regulatory networks

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from large-scale expression data is an important problem that received much attention in recent years. These networks have the potential to gain insights into causal molecular interactions of biological processes. Hence, from a methodological point of view, reliable estimation methods based on observational data are needed to approach this problem practically.</p> <p>Results</p> <p>In this paper, we introduce a novel gene regulatory network inference (GRNI) algorithm, called C3NET. We compare C3NET with four well known methods, ARACNE, CLR, MRNET and RN, conducting in-depth numerical ensemble simulations and demonstrate also for biological expression data from <it>E. coli </it>that C3NET performs consistently better than the best known GRNI methods in the literature. In addition, it has also a low computational complexity. Since C3NET is based on estimates of mutual information values in conjunction with a maximization step, our numerical investigations demonstrate that our inference algorithm exploits causal structural information in the data efficiently.</p> <p>Conclusions</p> <p>For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. Our method can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.</p

Yükseköğretimde işyeri ruhsallığının öğretim görevlilerinin görüşlerine göre modellenmesi = Modelling of workplace spirituality in higher education according to lecturers’ opinions

yapan ve ölçüt örnekleme ile belirlenen kriterleri karşılayan öğretim görevlilerine rastgeledağıtılarak 100 öğretim görevlisine uygulanmıştır. Anket çalışmasından elde edilensonuçlar, betimleyici istatistik analizleri ile detayl

On the trellis structures of geometric augmented product codes

A new simple decomposable code construction technique, Geometric Augmented Product (GAP), is recently proposed in [12, 20]. It generates codes with the full information rate for all of the minimum Hamming distance-4 binary linear block codes of even length greater than or equal to 8. Additionally, some optimal Hamming distance-8 and higher distance codes are obtained with the proposed scheme. This paper elaborates the generic trellis structure of GAP codes. It is shown that the trellis structures provide lower decoding complexity in comparison to the trellises of some other well-known block codes and it is suitable for adaptive decoder applications because of its parallel and identical generic trellis structure

Comparison of Estimation Methods for Missing Value Imputation of Gene Expression Data

Control and correction process of missing values (imputation of MVs) is the first stage of the preprocessing of microarray datasets. This paper focuses on a comparison of most reliable and up to date estimation methods to control and correct the missing values. Imputation of MVs has a very high priority because of its impact on next pre-processing and post-processing stages of microarray data analysis namely, quality control, normalization, differential gene expression, classification, clustering, and pathway analysis, etc. Normalized root mean square error (NRMSE) value is used to evaluate the performances of most popular five methods (k-nearest neighbors, Bayesian principal component analysis, local least squares, mean and median). When NRMSE values of methods were compared, it has observed that local least squares (LLS) and Bayesian principal component analysis (BPCA) methods outperformed all other methods in all percentages of MVs (1%, 5%, 10%, and 20%). BPCA method has given the best results in all percentages of MVs over the number of probes or genes, whereas LLS method has given the best results in all percentages of MVs over the number of samples. The advantage of these two methods over others is that they are least affected by the complexity of the data set

Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures

Gene regulatory network inference (GRNI) algorithms are essential for efficiently utilizing large-scale microarray datasets to elucidate biochemical interactions among molecules in a cell. Recently, the combination of network-based error measures complemented with an ensemble approach became popular for assessing the inference performance of the GRNI algorithms. For this reason, we developed a software package to facilitate the usage of such metrics. In this paper, we present netmes , an R software package that allows the assessment of GRNI algorithms. The software package netmes is available from the R -Forge web site https://r-forge.r-project.org/projects/netmes/