Search for: intermethod-comparison
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    CytoGTA: a cytoscape plugin for identifying discriminative subnetwork markers using a game theoretic approach

    , Article PLoS ONE ; Volume 12, Issue 10 , 2017 ; 19326203 (ISSN) Farahmand, S ; 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... 

    Analytical and numerical evaluation of steady flow of blood through artery

    , Article Biomedical Research (India) ; Volume 24, Issue 1 , 2013 , Pages 88-98 ; 0970938X (ISSN) Sedaghatizadeh, N ; Barari, A ; Soleimani, S ; Mofidi, M ; Sharif University of Technology
    Steady blood flow through a circular artery with rigid walls is studied by COSSERAT Continuum Mechanical Approach. To obtain the additional viscosities coefficients, feed forward multi-layer perceptron (MLP) type of artificial neural networks (ANN) and the results obtained in previous empirical works is used. The governing filed equations are derived and solution to the Hagen-Poiseuilli flow of a COSSERAT fluid in the artery is obtained analytically by Homotopy Perturbation Method (HPM) and numerically using finite difference method. Comparison of analytical results with numerical ones showed excellent agreement. In addition microrotation and the velocity profile along the radius are... 

    Interictal EEG noise cancellation: GEVD and DSS based approaches versus ICA and DCCA based methods

    , Article IRBM ; Volume 36, Issue 1 , 2015 , Pages 20-32 ; 19590318 (ISSN) Hajipour Sardouie, S ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
    Elsevier Masson SAS  2015
    Denoising is an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications. For this purpose, Blind Source Separation (BSS) methods, such as Independent Component Analysis (ICA) and Decorrelated and Colored Component Analysis (DCCA), are commonly used. Although ICA and DCCA-based methods are powerful tools to extract sources of interest, the procedure of eliminating the effect of sources of non-interest is usually manual. It should be noted that some methods for automatic selection of artifact sources after BSS methods exist, although they imply a training supervised step. On the other hand, in cases where there are some a prioriinformation about the subspace of... 

    Spatial and temporal joint, partially-joint and individual sources in independent component analysis: Application to social brain fMRI dataset

    , Article Journal of Neuroscience Methods ; Volume 329 , 2020 Pakravan, M ; Shamsollahi, M. B ; Sharif University of Technology
    Elsevier B.V  2020
    absectionBackground Three types of sources can be considered in the analysis of multi-subject datasets: (i) joint sources which are common among all subjects, (ii) partially-joint sources which are common only among a subset of subjects, and (iii) individual sources which belong to each subject and represent the specific conditions of that subject. Extracting spatial and temporal joint, partially-joint, and individual sources of multi-subject datasets is of significant importance to analyze common and cross information of multiple subjects. New method: We present a new framework to extract these three types of spatial and temporal sources in multi-subject functional magnetic resonance... 

    Comparison of strengths of five internal fixation methods used after bilateral sagittal split ramus osteotomy: An in vitro study

    , Article Dental Research Journal ; Volume 17, Issue 4 , 2020 , Pages 258-265 Sarkarat, F ; Ahmady, A ; Farahmand, F ; Fateh, A ; Kahali, R ; Nourani, A ; Rakhshan, V ; Sharif University of Technology
    Wolters Kluwer Medknow Publications  2020
    Results on the strength and displacement of internal fixation methods for bilateral sagittal split ramus osteotomy are controversial, and some designs have not been adequately studied. Therefore, this study was conducted to compare techniques using bicortical or monocortical screws. Materials and Methods: In this in vitro study, 35 sheep hemi-mandibles were randomly assigned to five groups of seven each: fixation using (1) a 13 × 2 screw, (2) two 13 × 2 screws (arranged vertically), (3) three 13 × 2 screws, (4) 1 plate with 4 holes and four monocortical screws, and (5) a Y-shaped plate and five monocortical screws. Specimens underwent vertical forces until failure. Breakage forces and... 

    QSAR analysis of platelet-derived growth inhibitors using GA-ANN and shuffling crossvalidation

    , Article QSAR and Combinatorial Science ; Volume 27, Issue 6 , 2008 , Pages 750-757 ; 1611020X (ISSN) Jalali Heravi, M ; Asadollahi Baboli, M ; Sharif University of Technology
    Quantitative Structure - Activity Relationship (QSAR) models for the inhibition action of some 1-phenylbenzimidazoles on platelet-derived growth are constructed using Genetic Algorithm and Artificial Neural Network (GA-ANN) method. The statistical parameters of R2 and root-mean-square error are 0.82 and 0.21, respectively using this method. These parameters show a considerable improvement compared to the stepwise multiple linear regression combined with ANN (stepwise MLR-ANN). Ten-fold shuffling crossvalidations are carried out to select the most important descriptors. Five descriptors of index of Balaban (J), average molecular weight (AMW), 3D-Wiener index (W3D), mean atomic van der Waals... 

    Control performance enhancement of gas turbines in the minimum command selection strategy

    , Article ISA Transactions ; Volume 112 , 2021 , Pages 186-198 ; 00190578 (ISSN) Eslami, M ; Banazadeh, A ; Sharif University of Technology
    ISA - Instrumentation, Systems, and Automation Society  2021
    Three novel methods, named α, ζ and ϵ, are suggested in this paper to recover the performance loss during switching in the gas turbine control systems. The Minimum Command Selection (MCS) in the gas turbine control systems prompts this performance loss. Any step towards more productivity with less aging factors have a great impact on the gas turbine's lifetime profit and vice versa. Although many hardware upgrades have been studied and applied to accomplish this, in many cases a low-risk manipulation in the software may yield equivalent achievement. State of the art gas turbine control systems are supposed to handle various forms of disturbances, several operation modes and relatively high... 

    A simple 2-step purification process of α-amylase from bacillus subtilis: optimization by response surface methodology

    , Article International Journal of Biological Macromolecules ; Volume 192 , 2021 , Pages 64-71 ; 01418130 (ISSN) Ataallahi, E ; Naderi-Manesh, H ; Roostaazad, R ; Yeganeh, S ; Sharif University of Technology
    Elsevier B.V  2021
    Purification of extracellular α-amylase from Bacillus subtilis was carried out via fractional precipitation by acetone and ion exchange chromatography. These steps provide fast precipitation as well as purification of α-amylase to improve enzyme purity, activity and stability. Compared with two-phase methods in which the yield was less than 1, this method resulted in a yield of more than 3. Moreover, 95% of acetone was recovered that enhanced the economy of the downstream process. Using the data provided by 2D electrophoresis, purification was done by a single step ion exchange chromatography. The enzyme exhibited a molecular mass (SDS-PAGE) of 50KD and the pI of 5. Maximum “yield” and... 

    Disease diagnosis with a hybrid method SVR using NSGA-II

    , Article Neurocomputing ; Vol. 136 , 2014 , pp. 14-29 Zangooei, M. H ; Habibi, J ; Alizadehsani, R ; Sharif University of Technology
    Early diagnosis of any disease at a lower cost is preferable. Automatic medical diagnosis classification tools reduce financial burden on health care systems. In medical diagnosis, patterns consist of observable symptoms and the results of diagnostic tests, which have various associated costs and risks. In this paper, we have experimented and suggested an automated pattern classification method for classifying four diseases into two classes. In the literature on machine learning or data mining, regression and classification problems are typically viewed as two distinct problems differentiated by continuous or categorical dependent variables. There are endeavors to use regression methods to... 

    Optimization of culture medium and modeling of curdlan production from Paenibacillus polymyxa by RSM and ANN

    , Article International Journal of Biological Macromolecules ; Vol. 70, issue , Jul , 2014 , p. 463-473 Rafigh, S. M ; Yazdi, A. V ; Vossoughi, M ; Safekordi, A. A ; Ardjmand, M ; Sharif University of Technology
    Paenibacillus polymyxa ATCC 21830 was used for the production of curdlan gum for first time. A Box-Behnken experimental design was applied to optimize six variables of batch fermentation culture each at three levels. Statistical analyses were employed to investigate the direct and interactive effects of variables on curdlan production. Optimum cultural conditions were temperature (50. °C), pH (7), fermentation time (96. h), glucose (100. g/L), yeast extract (3. g/L) and agitation speed (150. rpm). The yield of curdlan production was 6.89. g/L at optimum condition medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to model cultural conditions of curdlan... 

    MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules

    , Article Journal of Medical Engineering and Technology ; Vol. 38, issue. 4 , 2014 , p. 211-219 Amini, N ; Fatemizadeh, E ; Behnam, H ; Sharif University of Technology
    Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to... 

    A novel stability and kinematics-driven trunk biomechanical model to estimate muscle and spinal forces

    , Article Medical Engineering and Physics ; Vol. 36, issue. 10 , 2014 , p. 1296-1304 Hajihosseinali, M ; Arjmand, N ; Shirazi-Adl, A ; Farahmand, F ; Ghiasi, M. S ; Sharif University of Technology
    An anatomically detailed eighteen-rotational-degrees-of-freedom model of the human spine using optimization constrained to equilibrium and stability requirements is developed and used to simulate several symmetric tasks in upright and flexed standing postures. Predictions of this stability and kinematics-driven (S. +. KD) model for trunk muscle forces and spine compressive/shear loads are compared to those of our existing kinematics-driven (KD) model where both translational and rotational degrees-of-freedom are included but redundancy is resolved using equilibrium conditions alone. Unlike the KD model, the S. +. KD model predicted abdominal co-contractions that, in agreement with... 

    2D computational fluid dynamic modeling of human ventricle system based on fluid-solid interaction and pulsatile flow

    , Article Basic and Clinical Neuroscience ; Volume 4, Issue 1 , 2013 , Pages 64-75 ; 2008126X (ISSN) Masoumi, N ; Framanzad, F ; Zamanian, B ; Seddighi, A. S ; Moosavi, M. H ; Najarian, S ; Bastani, D ; Sharif University of Technology
    Many diseases are related to cerebrospinal fluid (CSF) hydrodynamics. Therefore, understanding the hydrodynamics of CSF flow and intracranial pressure is helpful for obtaining deeper knowledge of pathological processes and providing better treatments. Furthermore, engineering a reliable computational method is promising approach for fabricating in vitro models which is essential for inventing generic medicines. A Fluid-Solid Interaction (FSI)model was constructed to simulate CSF flow. An important problem in modeling the CSF flow is the diastolic back flow. In this article, using both rigid and flexible conditions for ventricular system allowed us to evaluate the effect of surrounding brain... 

    Constructing brain functional networks from EEG: Partial and unpartial correlations

    , Article Journal of Integrative Neuroscience ; Volume 10, Issue 2 , 2011 , Pages 213-232 ; 02196352 (ISSN) Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial... 

    Learning low-rank kernel matrices for constrained clustering

    , Article Neurocomputing ; Volume 74, Issue 12-13 , 2011 , Pages 2201-2211 ; 09252312 (ISSN) Baghshah, M. S ; Shouraki, S. B ; Sharif University of Technology
    Constrained clustering methods (that usually use must-link and/or cannot-link constraints) have been received much attention in the last decade. Recently, kernel adaptation or kernel learning has been considered as a powerful approach for constrained clustering. However, these methods usually either allow only special forms of kernels or learn non-parametric kernel matrices and scale very poorly. Therefore, they either learn a metric that has low flexibility or are applicable only on small data sets due to their high computational complexity. In this paper, we propose a more efficient non-linear metric learning method that learns a low-rank kernel matrix from must-link and cannot-link... 

    Novel class detection in data streams using local patterns and neighborhood graph

    , Article Neurocomputing ; Volume 158 , June , 2015 , Pages 234-245 ; 09252312 (ISSN) ZareMoodi, P ; Beigy, H ; Kamali Siahroudi, S ; Sharif University of Technology
    Elsevier  2015
    Data stream classification is one of the most challenging areas in the machine learning. In this paper, we focus on three major challenges namely infinite length, concept-drift and concept-evolution. Infinite length causes the inability to store all instances. Concept-drift is the change in the underlying concept and occurs in almost every data stream. Concept-evolution, in fact, is the arrival of novel classes and is an undeniable phenomenon in most real world data streams. There are lots of researches about data stream classification, but most of them focus on the first two challenges and ignore the last one. In this paper, we propose new method based on ensembles whose classifiers use... 

    MCRC software: A tool for chemometric analysis of two-way chromatographic data

    , Article Chemometrics and Intelligent Laboratory Systems ; Volume 104, Issue 2 , 2010 , Pages 155-171 ; 01697439 (ISSN) Jalali Heravi, M ; Parastar, H ; Kamalzadeh, M ; Tauler, R ; Jaumot, J ; Sharif University of Technology
    This paper describes the development and implementation of MCRC software, chemometric software for Multivariate Curve Resolution of two-way Chromatographic data. MCRC software is developed for chemometric analysis of chromatographic data; however, it may also be used for other types of multivariate data. It consists of five groups of techniques for preprocessing, chemical rank determination, local rank analysis, multivariate resolution and peak integration. This software has the ability of the analysis of complex multi-component chromatographic signals of gas chromatography-mass spectrometry (GC-MS) and high performance liquid chromatography-diode array detection (HPLC-DAD). The software... 

    Assessment of the co-elution problem in gas chromatography-mass spectrometry using non-linear optimization techniques

    , Article Chemometrics and Intelligent Laboratory Systems ; Volume 101, Issue 1 , 2010 , Pages 1-13 ; 01697439 (ISSN) Jalali Heravi, M ; Parastar, H ; Sharif University of Technology
    Multivariate curve resolution based on the minimization of an objective function (MCR-FMIN) defined directly from the non-fulfillment of constraints was applied for the first time as a deconvolution method to separate co-eluted gas chromatographic-mass spectrometric (GC-MS) signals. Simulated and real (standard real mixture and limon oil) GC-MS data were used to evaluate the feasibility of this method. The MCR-FMIN solutions have been obtained based on the rotation of principal component analysis (PCA) solutions using the non-linear optimization algorithms. Calculation of the initial values of R rotation matrix using model free analysis methods such as fixed-size moving window-evolving... 

    Kernel-based metric learning for semi-supervised clustering

    , Article Neurocomputing ; Volume 73, Issue 7-9 , 2010 , Pages 1352-1361 ; 09252312 (ISSN) Soleymani Baghshah, M ; Bagheri Shouraki, S ; Sharif University of Technology
    Distance metric plays an important role in many machine learning algorithms. Recently, there has been growing interest in distance metric learning for semi-supervised setting. In the last few years, many methods have been proposed for metric learning when pairwise similarity (must-link) and/or dissimilarity (cannot-link) constraints are available along with unlabeled data. Most of these methods learn a global Mahalanobis metric (or equivalently, a linear transformation). Although some recently introduced methods have devised nonlinear extensions of linear metric learning methods, they usually allow only limited forms of distance metrics and also can use only similarity constraints. In this... 

    Assessment and comparison of اomogeneity and conformity indexes in step-and-shoot and compensator-based intensity modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D CRT) in prostate cancer

    , Article Journal of Medical Signals and Sensors ; Volume 7, Issue 2 , 2017 , Pages 102-107 ; 22287477 (ISSN) Salimi, M ; Shirani Tak Abi, K ; Nedaie, H. A ; Hassani, H ; Gharaati, H ; Samei, M ; Shahi, R ; Zarei, H ; Sharif University of Technology
    Intensity modulated radiation therapy (IMRT) and three-dimensional conformal radiation therapy (3D CRT) are two treatment modalities in prostate cancer, which provide acceptable dose distribution in tumor region with sparing the surrounding normal tissues. IMRT is based on inverse planning optimization; in which, intensity of beams is modified by using multileaf collimators and also compensators with optimum shapes in step and shoot (SAS) and compensator-based method, respectively. In the recent study, some important parameters were compared in two IMRT and 3D CRT methods. Prescribed dose was 80 Gy for both IMRT procedures and 70 Gy for 3D CRT. Treatment plans of 15 prostate cancer...