Multi-dimensional model for determining the leading performance indicators of safety management systems.

Background: Health and safety performance measurements aimed to provide information on the progress and current situation of organizational strategies and activities.

Objectives: We developed a model to determine and select safety key performance indicators in order to assess safety management systems.

Methods: This study has been designed in six steps aiming at defining a model of leading performance indicators (LPIs) and selecting key performance indicators (KPIs) using the AHP method.

TIGRNCRN: Trustful inference of gene regulatory network using clustering and refining the network.

In this study, in order to deal with the noise and uncertainty in gene expression data, learning networks, especially Bayesian networks, that have the ability to use prior knowledge, were used to infer gene regulatory network. Learning networks are methods that have the structure of the network and a learning process to obtain relationships. One of the methods which have been used for measuring the relationship between genes is the correlation metrics, but the high correlated genes not necessarily mean that they have causal effect on each other.

Parallel Algorithms for Inferring Gene Regulatory Networks: A Review.

System biology problems such as whole-genome network construction from large-scale gene expression data are sophisticated and time-consuming. Therefore, using sequential algorithms are not feasible to obtain a solution in an acceptable amount of time. Today, by using massively parallel computing, it is possible to infer large-scale gene regulatory networks.

A robust gene regulatory network inference method base on Kalman filter and linear regression.

The reconstruction of the topology of gene regulatory networks (GRNs) using high throughput genomic data such as microarray gene expression data is an important problem in systems biology. The main challenge in gene expression data is the high number of genes and low number of samples; also the data are often impregnated with noise. In this paper, in dealing with the noisy data, Kalman filter based method that has the ability to use prior knowledge on learning the network was used.