Sharif Digital Repository

Ankylosing spondylitis (AS) is a systemic inflammatory disorder of joints and entheses. Recent studies have reported an increased prevalence of dementia in AS patients. However, data for exploring the association between dementia and AS remain uncertain. In this study, enriched pathways and differentially expressed genes (DEGs) were identified in whole blood transcription data of AS patients obtained from the gene expression omnibus (GEO) database; using gene set enrichment analysis (GSEA) and differential expression analysis. Four pathways, including oxidative phosphorylation, Alzheimer's, Parkinson's, and Huntington's diseases were significantly enriched in AS patients compared to the... 

This paper focuses on developing a new method that represents user-accessible correlation for the estimation of water-based nanofluids viscosity. For this, an evolutionary algorithm, namely Gene Expression Programming (GEP), was adapted based on a wide selection of literature published databanks including 819 water-based nanofluids viscosity points. The developed model utilized the base fluid viscosity as well as volume fraction and size of the nanoparticles as the inputs of the model. Several statistical parameters integrated with graphical plots were employed in order to assess the accuracy of the proposed GEP-based model. Results of the evaluation demonstrate fairly enough accuracy of the... 

In recent years, analyzing genome-wide expression profiles to find genetic markers has received much attention as a challenging field of research aiming at unveiling biological mechanisms behind complex disorders. The identification of reliable and reproducible markers has lately been achieved by integrating genome-scale functional relationships and transcriptome datasets, and a number of algorithms have been developed to support this strategy. In this paper, we present a promising and easily applicable tool to accomplish this goal, namely CytoGTA, which is a Cytoscape plug-in that relies on an optimistic game theoretic approach (GTA) for identifying subnetwork markers. Given transcriptomic... 

In this paper, the Extended Kalman filtering (EKF) approach has been used to infer gene regulatory networks using time-series gene expression data. Gene expression values are considered stochastic processes and the gene regulatory network, a dynamical nonlinear stochastic model. Using these values and a modified Kalman filtering approach, the model's parameters and consequently the interactions amongst genes are predicted. In this paper, each gene-gene interaction is modeled using a linear term, a nonlinear one, and a constant term. The linear and nonlinear term coefficients are included in the state vector together with the gene expressions' true values. Through the extended Kalman... 

Gelatin methacrylate (GelMA) was proved to be a promising bioink for corneal stromal cell delivery. However, GelMA has low mechanical properties which makes it difficult to be suturable and handled for clinical applicattion. In this study, three different ratios of 12.5 % GelMA and 10 % PEGDA were investigated for corneal stromal cells delivery. The mixture containing 75 % GelMA and 25 % PEGDA (75G25P) was found to have reasonable cell viability and suturing strength. Moreover, collagen nanofibers were incorporated into 75G25P hydrogel to improve the mechanical and biomimetic properties of the construct (75G25P-E). A hybrid structure was obtained by injecting the optimized bioink on the... 

Titanium carbonated hydroxyapatite (Ti/CHA) nanobiocomposites have extensive biological applications due to the excellent biocompatibility and similar characteristics to the human bone. Ti/CHA nanobiocomposite has good biological properties but it suffer from diverse characteristics especially in hardness, Young's modulus, apparent porosity and relative density. This investigation is an attempt to propose the predictive models using gene expression programming (GEP) to estimate these characteristics. In this regards, GEP is used to model and compare the effect of practical variables including pressure, Ti/CHA contents and sintering temperature on their monitored properties. To achieve this... 

The use of ZrO2 nanoparticles is common in the Cu matrix to manufacture Cu/ZrO2 nanocomposites. Many approaches and parameters produce Cu/ZrO2 nanocomposites with an appropriate wear rate and other properties. Hence, proposing an accurate and reliable model was imperative. The main goal of this paper is to find out the best models among the ant colony optimization (ACO), gene expression programming (GEP), artificial bee colony (ABC), and gray wolf optimization algorithm (GWOA) to predict the wear rate of/ZrO2 nanocomposites. To the best of our knowledge, this is the first research that predicts Cu/ZrO2 nanocomposites wear rate using group modeling. To develop models, 105 data were collected... 

Background: The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.Results: Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection... 

Motivation: Single-cell RNA-sequencing (scRNA-seq) offers the opportunity to dissect heterogeneous cellular compositions and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition designs. Results: Here, we present a single-cell Generative Adversarial Network (scGAN) to simultaneously acquire patterns from raw data while minimizing the confounding effect driven by technical artifacts or other factors inherent to the data. Specifically, scGAN models the data likelihood of the raw scRNA-seq counts by projecting each cell onto a latent embedding....