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.

Effect of alpha-lipoic acid on asymmetric dimethylarginine and disability in multiple sclerosis patients: A randomized clinical trial.

BACKGROUND: Multiple Sclerosis (MS) is an inflammatory and demyelinating disease of the central nervous system. Oxidative stress plays a major role in the onset and progression of MS. Asymmetric dimethylarginine (ADMA) formation is dependent on oxidative stress status. OBJECTIVE: We examined whether alpha-lipoic acid (ALA) as a potent antioxidant could improve the Expanded Disability Status Scale (EDSS) and decrease plasma level of ADMA in multiple sclerosis patients. METHODS: In a randomized, double-blinded clinical trial conducted at Sina Hospital in Tehran, Iran, from September 2009 to July 2011, 24 patients with relapsing-remitting MS were divided into a treatment group receiving ALA (1200mg/day) for 12 weeks and a control group receiving placebo. Then patients' EDSS and Plasma levels of ADMA were measured at baseline and 12 weeks later. Statistical analysis was done by SPSS software version 16 using the K-S test, Chi square, Mann-Whitney U-test and Wilcoxon test. RESULTS: The plasma levels of ADMA in the intervention group were decreased significantly (p=0.04). Also, no patient had increased EDSS score in the supplement group, where 2 out of 12 patients in the placebo group experienced so. Comparing the serum level of ADMA between the two groups failed to show any significant change in the supplement group compared with the control group. CONCLUSION: Considering that ADMA is produced by oxidative stress in MS patients and leads to increase of inflammation, ALA may have the potential of beneficial effects in them, in part, by decreasing the plasma level of ADMA and stopping progression. TRIAL REGISTRATION: The trial was registered at the Iranian Registry of Clinical Trials (http://www.irct.ir) with the Irct ID: No. IRCT138812222602N2. FUNDING: The authors received no financial support for the research, authorship, and/or publication of this article.