Search for: gene-expression-profiling
CytoGTA: a cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach, Article PLoS ONE ; Volume 12, Issue 10 , 2017 ; 19326203 (ISSN) ; Foroughmand Araabi, M. H ; Goliaei, S ; Razaghi Moghadam, Z ; Sharif University of Technology
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...
Article Bioinformatics ; Volume 37, Issue 10 , 2021 , Pages 1345-1351 ; 13674803 (ISSN) ; Maitra, M ; Nagy, C ; Turecki, G ; Rabiee, H. R ; Li, Y ; Sharif University of Technology
Oxford University Press 2021
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....
Article Magnetic Resonance in Chemistry ; Vol. 52, issue. 7 , July , 2014 , p. 370-376 ; Majari-Kasmaee, L ; Mani-Varnosfaderani, A ; Kyani, A ; Rostami-Nejad, M ; Sohrabzadeh, K ; Naderi, N ; Zali, M. R ; Rezaei-Tavirani, M ; Tafazzoli, M ; Arefi-Oskouie, A ; Sharif University of Technology
The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment.Proton nuclear magnetic resonance spectroscopy ( 1H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for...
Analysis of gene expression profiles and protein-protein interaction networks in multiple tissues of systemic sclerosis, Article BMC Medical Genomics ; Volume 12, Issue 1 , 2019 ; 17558794 (ISSN) ; Sharifi Zarchi, A ; Nikaein, H ; Salehi, S ; Salamatian, B ; Elmi, N ; Gharibdoost, F ; Mahmoudi, M ; Sharif University of Technology
BioMed Central Ltd 2019
Background: Systemic sclerosis (SSc), a multi-organ disorder, is characterized by vascular abnormalities, dysregulation of the immune system, and fibrosis. The mechanisms underlying tissue pathology in SSc have not been entirely understood. This study intended to investigate the common and tissue-specific pathways involved in different tissues of SSc patients. Methods: An integrative gene expression analysis of ten independent microarray datasets of three tissues was conducted to identify differentially expressed genes (DEGs). DEGs were mapped to the search tool for retrieval of interacting genes (STRING) to acquire protein-protein interaction (PPI) networks. Then, functional clusters in PPI...
Article BMC Bioinformatics ; Volume 14, Issue SUPPL13 , 2013 ; 14712105 (ISSN) ; Rabiee, H. R ; Anooshahpour, M ; Sharif University of Technology
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...
Article Nature Methods ; Volume 13, Issue 4 , 2016 , Pages 310-322 ; 15487091 (ISSN) ; Heiser, L.M ; Cokelaer, T ; Linger, M ; Nesser, N. K ; Carlin, D. E ; Zhang, Y ; Sokolov, A ; Paull, E. O ; Wong, C. K ; Graim, K ; Bivol, A ; Wang, H ; Zhu, F ; Afsari, B ; Danilova, L. V ; Favorov, A. V ; Lee, W. S ; Taylor, D ; Hu, C. W ; Long, B. L ; Noren, D. P ; Bisberg, A. J ; Mills, G. B ; Gray, J. W ; Kellen, M ; Norman, T ; Friend, S ; Qutub, A. A ; Fertig, E. J ; Guan, Y ; Song, M ; Stuart, J. M ; Spellman, P. T ; Koeppl, H ; Stolovitzky, G ; Saez Rodriguez, J ; Mukherjee, S ; Afsari, B ; Al-Ouran, R ; Anton, B ; Arodz, T ; Askari Sichani, O ; Bagheri, N ; Berlow, N ; Bisberg, A. J ; Bivol, A ; Bohler, A ; Bonet, J ; Bonneau, R ; Budak, G ; Bunescu, R ; Caglar, M ; Cai, B ; Cai, C ; Carlin, D. E ; Carlon, A ; Chen, L ; Ciaccio, M. F ; Cokelaer, T ; Cooper, G ; Coort, S ; Creighton, C. J ; Daneshmand, S. M. H ; De La Fuente, A ; Di Camillo, B ; Danilova, L. V ; Dutta-Moscato, J ; Emmett, K ; Evelo, C ; Fassia, M. K. H ; Favorov, A. V ; Fertig, E. J ; Finkle, J. D ; Finotello, F ; Friend, S ; Gao, X ; Gao, J ; Garcia Garcia, J ; Ghosh, S ; Giaretta, A ; Graim, K ; Gray, J. W ; Großeholz, R ; Guan, Y ; Guinney, J ; Hafemeister, C ; Hahn, O ; Haider, S ; Hase, T ; Heiser, L. M ; Hill, S. M ; Hodgson, J ; Hoff, B ; Hsu, C. H ; Hu, C. W ; Hu, Y ; Huang, X ; Jalili, M ; Jiang, X ; Kacprowski, T ; Kaderali, L ; Kang, M ; Kannan, V ; Kellen, M ; Kikuchi, K ; Kim, D. C ; Kitano, H ; Knapp, B ; Komatsoulis, G ; Koeppl, H ; Krämer, A ; Kursa, M. B ; Kutmon, M ; Lee, W. S ; Li, Y ; Liang, X ; Liu, Z ; Liu, Y ; Long, B. L ; Lu, S ; Lu, X ; Manfrini, M ; Matos, M. R. A ; Meerzaman, D ; Mills, G. B ; Min, W ; Mukherjee, S ; Müller, C. L ; Neapolitan, R. E ; Nesser, N. K ; Noren, D. P ; Norman, T ; Oliva, B ; Opiyo, S. O ; Pal, R ; Palinkas, A ; Paull, E. O ; Planas Iglesias, J ; Poglayen, D ; Qutub, A. A ; Saez Rodriguez, J ; Sambo, F ; Sanavia, T ; Sharifi-Zarchi, A ; Slawek, J ; Sokolov, A ; Song, M ; Spellman, P. T ; Streck, A ; Stolovitzky, G ; Strunz, S ; Stuart, J. M ; Taylor, D ; Tegnér, J ; Thobe, K ; Toffolo, G. M ; Trifoglio, E ; Unger, M ; Wan, Q ; Wang, H ; Welch, L ; Wong, C. K ; Wu, J. J ; Xue, A. Y ; Yamanaka, R ; Yan, C ; Zairis, S ; Zengerling, M ; Zenil, H ; Zhang, S ; Zhang, Y ; Zhu, F ; Zi, Z ; Sharif University of Technology
Nature Publishing Group 2016
It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was...
Article 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08, Vancouver, BC, 20 August 2008 through 25 August 2008 ; 2008 , Pages 4684-4687 ; 9781424418152 (ISBN) ; Davoudi, H ; Fatemizadeh, E ; Sharif University of Technology
IEEE Computer Society 2008
In this paper, a heuristic method for determining the optimal number of clusters is proposed. Four clustering algorithms, namely K-means, Growing Neural Gas, Simulated Annealing based technique, and Fuzzy C-means in conjunction with three well known cluster validity indices, namely Davies-Bouldin index, Calinski-Harabasz index, Maulik-Bandyopadhyay index, in addition to the proposed index are used. Our simulations evaluate capability of mentioned indices in some artificial and medical datasets. © 2008 IEEE
Hyperthermia of breast cancer tumor using graphene oxide-cobalt ferrite magnetic nanoparticles in mice, Article Journal of Drug Delivery Science and Technology ; Volume 65 , 2021 ; 17732247 (ISSN) ; Balasi, Z. M ; Ahadian, M. M ; Mortezazadeh, T ; Shams, F ; Hosseinzadeh, S ; Sharif University of Technology
Editions de Sante 2021
Herein, the graphene oxide (GO)/cobalt ferrite nanoparticles were used to apply the heat treatment on the breast cancer cell line of MCF7. The synthesized nanoparticles were evaluated before in vitro and in vivo studies, using transmission electron microscopy (TEM), X-ray diffraction (XRD), vibrating sample magnetometer (VSM), X-ray photoelectron spectroscopy (XPS), thermal property and relaxivity measurement. The nanoparticles showed a diameter of 5 nm with the ferrimagnetic property. Also, the nanoparticles were well distributed on the GO nanosheets. The related peaks of cobalt ferrite nanoparticles were approved by using XRD and XPS assays. During the in vitro investigations, IC50 with...
MicroRNA profiling reveals important functions of miR-125b and let-7a during human retinal pigment epithelial cell differentiation, Article Experimental Eye Research ; Volume 190 , 2020 ; Satarian, L ; Moradi, S ; Sharifi Zarchi, A ; Günther, S ; Kamal, A ; Totonchi, M ; Mowla, S. J ; Braun, T ; Baharvand, H ; Sharif University of Technology
Academic Press 2020
Retinal pigment epithelial (RPE) cells are indispensable for eye organogenesis and vision. To realize the therapeutic potential of in vitro-generated RPE cells for cell-replacement therapy of RPE-related retinopathies, molecular mechanisms of RPE specification and maturation need to be investigated. So far, many attempts have been made to decipher the regulatory networks involved in the differentiation of human pluripotent stem cells into RPE cells. Here, we exploited a highly-efficient RPE differentiation protocol to determine global expression patterns of microRNAs (miRNAs) during human embryonic stem cell (hESC) differentiation into RPE using small RNA sequencing. Our results revealed a...
Defining microRNA signatures of hair follicular stem and progenitor cells in healthy and androgenic alopecia patients, Article Journal of Dermatological Science ; Volume 101, Issue 1 , 2021 , Pages 49-57 ; 09231811 (ISSN) ; Nilforoushzadeh, M. A ; Youssef, K. K ; Sharifi Zarchi, A ; Moradi, S ; Khosravani, P ; Aghdami, R ; Taheri, P ; Hosseini Salekdeh, G ; Baharvand, H ; Aghdami, N ; Sharif University of Technology
Elsevier Ireland Ltd 2021
Background: The exact pathogenic mechanism causes hair miniaturization during androgenic alopecia (AGA) has not been delineated. Recent evidence has shown a role for non-coding regulatory RNAs, such as microRNAs (miRNAs), in skin and hair disease. There is no reported information about the role of miRNAs in hair epithelial cells of AGA. Objectives: To investigate the roles of miRNAs affecting AGA in normal and patient's epithelial hair cells. Methods: Normal follicular stem and progenitor cells, as well as follicular patient's stem cells, were sorted from hair follicles, and a miRNA q-PCR profiling to compare the expression of 748 miRNA (miRs) in sorted cells were performed. Further, we...
Article International Journal of Molecular Sciences ; Volume 22, Issue 13 , 2021 ; 16616596 (ISSN) ; Sharifi Zarchi, A ; Rahmani, S ; Nafissi, N ; Mowla, S. J ; Lauria, A ; Oliviero, S ; Matin, M. M ; Sharif University of Technology
miR-29b2 and miR-29c play a suppressive role in breast cancer progression. C1orf132 (also named MIR29B2CHG) is the host gene for generating both microRNAs. However, the region also expresses longer transcripts with unknown functions. We employed bioinformatics and experimental approaches to decipher C1orf132 expression and function in breast cancer tissues. We also used the CRISPR/Cas9 technique to excise a predicted C1orf132 distal promoter and followed the behavior of the edited cells by real-time PCR, flow cytometry, migration assay, and RNA-seq techniques. We observed that C1orf132 long transcript is significantly downregulated in triple-negative breast cancer. We also identified a...