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Genetics of severe combined immunodeficiency

Genes & Diseases 2020年第1期7卷 52-61页

作者： Rajni Kumrah Pandiarajan Vignesh Pratap Patra Ankita Singh Gummadi Anjani poonam saini Madhubala Sharma Anit Kaur Amit Rawat Allergy Immunology Unit Department of PediatricsAdvanced Pediatrics CentrePostgraduate Institute of Medical Education and ResearchChandigarhIndia

Severe Combined Immunodeficiency(SCID)is an inherited group of rare,lifethreatening disorders due to the defect in T cell development and *** manifestations are characterised by recurrent and severe bacterial,viral,and fungal opportunistic infections that start from early infancy *** stem cell transplantation(HSCT)is the treatment of *** pattern of inheritance of SCID may be X-linked or autosomal *** the diagnosis of SCID is usually established by flow cytometry-based tests,genetic diagnosis is often needed for genetic counselling,prognostication,and modification of pre-transplant chemotherapeutic *** review aims to highlight the genetic aspects of SCID.

关键词： Adenosine deaminase Flow cytometry Genetics Newborn screening Severe combined immunodeficiency

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Role of Feature Selection on Leaf Image Classification

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Journal of Data Analysis and Information Processing 2015年第4期3卷 175-183页

作者： Arun Kumar Vinod Patidar Deepak Khazanchi poonam saini Department of Computer Science and Engineering Sir Padampat Singhania University Udaipur India Department of Physics Sir Padampat Singhania University Udaipur India College of Information Sciences and Technology University of Nebraska Omaha NE USA

The digital images have been studied for image classification, enhancement, image compression and image segmentation purposes. In the present work, it is proposed to study the effects of feature selection algorithm on the predictive classification accuracy of algorithms used for discriminating the different plant leaf images. The process involves extracting the important texture features from the digital images and then subjecting them to feature selection and further classification process. The leaf image features have been extracted by using Gabor texture features and these Gabor features are subjected to Random Forest feature selection algorithm for extracting important texture features. The four classification algorithms like K-Nearest Neighbour, J48, Classification and Regression Trees and Random Forest have been used for classification purpose. This study shows that there is a net improvement in the predictive classification accuracy values, when classification algorithms have been applied on selected features over the complete set of features.

关键词： Leaf Image Feature Selection Algorithm Random Forest Gabor Texture Features

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