No abstract

BackgroundProstate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer.ResultsTo deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage.ConclusionsWe provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

No abstract

No abstract

No abstract

No abstract

Abstract:It is widely recognized that a firm's well-established corporate governance (CG) has a considerable impact on its corporate social responsibility (CSR) performance. How to determine the main trigger among CG's indicators for strengthening CSR performance is thus an urgent and complicated task due to its (i.e., CSR) multi-dimensional and numerous perspectives. In order to solve this critical problem, the study breaks down CSR into four dimensions and further examines the impact of CG's indicators on each CSR dimension by joint utilization of rough set theory (RST) and decision tree (DT). By doing so, users can realize which one CG indicator is the most essential to CSR performance. Managers can take the results as a reference to allocate valuable and scarce resources to the right place so as to enhance CSR performance in the future. To solidify our research finding, we transform the CSR forecasting model selection into a multiple criteria decision making (MCDM) task and execute a MCDM algorithm. By implementing the MCDM algorithm, users can achieve a much more reliable and consensus decision in today's highly turbulent economic environment. The proposed mechanism, examined by real cases, is a promising alternative for CSR performance forecasting.

The aim of this paper is to develop a mathematical model for a green product mix decision that incorporates capacity expansion features by using a mathematical programming approach. In order to satisfy customer orders when market demands exceed the company's production capacity, as well as maximise total profits, companies must study the feasibility of expanding capacity with regard to the production of parts. To place efficient controls on green product costs, we present the green product mix decision model for evaluating the benefits of expanding various types of capacity. By applying this model, companies that produce green products can make optimal decisions about further processing and capacity expansion.