Data-Driven and Knowledge-Based Algorithms for Gene Network Reconstruction on High-Dimensional Data.

Previous efforts in gene network reconstruction have mainly focused on data-driven modeling, with little attention paid to knowledge-based approaches. Leveraging prior knowledge, however, is a promising paradigm that has been gaining momentum in network reconstruction and computational biology research communities. This paper proposes two new algorithms for reconstructing a gene network from expression profiles with and without prior knowledge in small sample and high-dimensional settings. First, using tools from the statistical estimation theory, particularly the empirical Bayesian approach, the current research estimates a covariance matrix via the shrinkage method. Second, estimated covariance matrix is employed in the penalized normal likelihood method to select the Gaussian graphical model. This formulation allows the application of prior knowledge in the covariance estimation, as well as in the Gaussian graphical model selection. Experimental results on simulated and real datasets show that, compared to state-of-the-art methods, the proposed algorithms achieve better results in terms of both PR and ROC curves. Finally, the present work applies its method on the RNA-seq data of human gastric atrophy patients, which was obtained from the EMBL-EBI database. The source codes and relevant data can be downloaded from: https://github.com/AbbaszadehO/DKGN.

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. Recently, establishing gene regulatory networks from large-scale datasets have drawn the noticeable attention of researchers in the field of parallel computing and system biology. In this paper, we attempt to provide a more detailed overview of the recent parallel algorithms for constructing gene regulatory networks. Firstly, fundamentals of gene regulatory networks inference and large-scale datasets challenges are given. Secondly, a detailed description of the four parallel frameworks and libraries including CUDA, OpenMP, MPI, and Hadoop is discussed. Thirdly, parallel algorithms are reviewed. Finally, some conclusions and guidelines for parallel reverse engineering are described.

Does concrete composition affect photoneutron production inside radiation therapy bunkers?

PURPOSE: Different types of concretes are used for bunker construction for radiation therapy. As neutron production occurs in high-energy photon beams, the purpose of this study was to investigate the effect of different concretes on photoneutron doses at an isocenter and maze entrance door. MATERIALS AND METHODS: The 18-MV photon beam of a Varian 2100 C/D linear accelerator and a radiation therapy bunker were simulated using the MCNPX Monte Carlo code. Different commercially available concretes were used in photoneutron calculations for the simulated bunker. RESULTS: Higher neutron doses of the water phantom were seen for barytes and galena concretes, while there was no significant (less than 1%) difference between the neutron dose of the phantom for all other concretes. Also, the neutron fluence at the inner and outer maze entrance varied up to 36% depending on the concretes' atomic compositions. CONCLUSION: It can be concluded that application of high-density concretes in order to use limited space or for other purposes may cause higher neutron doses in the maze entrance door and consequently may impose stricter requirements for neutron shielding of maze entrance doors.