A latent factor model with a mixture of sparse and dense factors to model gene expression data with confounding effects
نویسندگان
چکیده
factors to model gene expression data with confounding effects Chuan Gao, Christopher D Brown, Barbara E Engelhardt1,3,∗ 1 Institute for Genome Sciences & Policy, Duke University, Durham, NC, USA 2 Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA 3 Department of Biostatistics & Bioinformatics and Department of Statistical Science, Duke University, Durham, NC, USA ∗ E-mail: [email protected]
منابع مشابه
Mammalian Eye Gene Expression Using Support Vector Regression to Evaluate a Strategy for Detecting Human Eye Disease
Background and purpose: Machine learning is a class of modern and strong tools that can solve many important problems that nowadays humans may be faced with. Support vector regression (SVR) is a way to build a regression model which is an incredible member of the machine learning family. SVR has been proven to be an effective tool in real-value function estimation. As a supervised-learning appr...
متن کاملGene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method
Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...
متن کاملبهکارگیری متغیرهای پنهان در مدل رگرسیون لجستیک برای حذف اثر همخطی چندگانه در تحلیل برخی عوامل مرتبط با سرطان پستان
Background and Objectives: Logistic regression is one of the most widely used generalized linear models for analysis of the relationships between one or more explanatory variables and a categorical response. Strong correlations among explanatory variables (multicollinearity) reduce the efficiency of model to a considerable degree. In this study we used latent variables to reduce the effects of ...
متن کاملEvaluation of the Gene Expression of Tumor Necrotic Factor Alpha and Interlukine-6 in Rat Model of Diabetes Type 2 Treated with Silver Nanoparticles Synthetized by Galega Officinalis Extract
Background: Hypoglycaemic effects of Galega officinalis and silver nanoparticles are established. In the present study, the effects of silver nanoparticles synthetized by Galega officinalis extract were investigated on gene expression of TNF-α, IL-6 and serum levels of liver enzymes in diabetes type 2. Methods: In the present study 20 male Wistar rats in 4 group(n= 5 in each group) weighing 1...
متن کاملA Mixture Model for Learning Sparse Representations
In a latent variable model, an overcomplete representation is one in which the number of latent variables is at least as large as the dimension of the data observations. Overcomplete representations have been advocated due to robustness in the presence of noise, the ability to be sparse, and an inherent flexibility in modeling the structure of data [9]. In this report, we modify factor analysis...
متن کامل