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Seyed Morteza Najibi

Bio: Seyed Morteza Najibi is an academic researcher from Shiraz University. The author has contributed to research in topics: Order statistic & Goodness of fit. The author has an hindex of 5, co-authored 19 publications receiving 138 citations. Previous affiliations of Seyed Morteza Najibi include Persian Gulf University & Lund University.

Papers
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Journal ArticleDOI
TL;DR: In this article, the authors introduce a new family of continuous distributions and study the mathematical properties of the new family, which are based on the ratio of two truncated moments and hazard function.
Abstract: We introduce a new family of continuous distributions and study the mathematical properties of the new family. Some useful characterizations based on the ratio of two truncated moments and hazard function are also presented. We estimate the model parameters by the maximum likelihood method and assess its performance based on biases and mean squared errors in a simulation study framework.

77 citations

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TL;DR: In this article, a new class of models called the transmuted odd log-logistic-G family is proposed and studied and the method of maximum likelihood is used to estimate the unknown parameters.
Abstract: A new class of models called the transmuted odd log-logistic-G family IS proposed and studied. The method of maximum likelihood is used to estimate the unknown parameters. The performance of the ma...

24 citations

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TL;DR: A nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles using trigonometric spline which is more efficient compared to existing methods and provides a novel and unique perspective to two important and challenging problems in protein structure research.
Abstract: Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

22 citations

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TL;DR: The results revealed that NODDI can improve detection of microstructural changes in cortical and subcortical GM in patients with MRI negative epilepsy.

16 citations

Journal ArticleDOI
TL;DR: In this paper, a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain is developed.
Abstract: This article develops a method for simultaneous estimation of density functions for a collection of populations of protein backbone angle pairs using a data-driven, shared basis that is constructed by bivariate spline functions defined on a triangulation of the bivariate domain. The circular nature of angular data is taken into account by imposing appropriate smoothness constraints across boundaries of the triangles. Maximum penalized likelihood is used to fit the model and an alternating blockwise Newton-type algorithm is developed for computation. A simulation study shows that the collective estimation approach is statistically more efficient than estimating the densities individually. The proposed method was used to estimate neighbor-dependent distributions of protein backbone dihedral angles (i.e., Ramachandran distributions). The estimated distributions were applied to protein loop modeling, one of the most challenging open problems in protein structure prediction, by feeding them into an angular-sampling-based loop structure prediction framework. Our estimated distributions compared favorably to the Ramachandran distributions estimated by fitting a hierarchical Dirichlet process model; and in particular, our distributions showed significant improvements on the hard cases where existing methods do not work well.

14 citations


Cited by
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Journal ArticleDOI
TL;DR: This paper proposes an end‐to‐end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction, which improves the prediction performance over the previous state‐of‐the‐art methods.
Abstract: Motivation Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number. Results We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms. Availability and implementation The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre. Contact xin.gao@kaust.edu.sa. Supplementary information Supplementary data are available at Bioinformatics online.

202 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a review on the comparative synthesis of TiO2 NPs with different characteristics and their wide range of applications and highlight the regulating features and future standpoints for the measurable enrichment in TiO 2 NPs product and perspectives of the reliable application.

122 citations

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TL;DR: The applications of NODDI in clinical research are reviewed and future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice are discussed.

91 citations

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TL;DR: Ghosh and Hamedani as discussed by the authors studied at the Federal University of Technology Parana, Brazil and the University of Sistan and Baluchestan, Zahedan, Iran, respectively.
Abstract: Thiago G. Ramires Indranil Ghosh e and G.G. Hamedani f aDepartment of Statistics, Mathematics and Insurance, Benha University, Egypt bDepartment of Statistics, Persian Gulf University, Bushehr, Iran cFaculty of Mathematics, University of Sistan and Baluchestan, Zahedan, Iran. dDepartment of mathematic, Federal University of Technology Parana, Brazil eDepartment of Mathematics and Statistics, University of North Carolina Wilmington, USA fDepartment of Mathematics, Statistics and Computer Science, Marquette University,USA

80 citations

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TL;DR: This review article highlights the recent research progress on the structure and function of core protein in HBV replication cycle, the mode of action of CpAMs, as well as the current status and perspectives on the discovery and development of coreprotein-targeting antivirals.

54 citations