USING ASYMMETRIC DISTRIBUTIONS FOR MODELING GENE EXPRESSION DATA
نویسندگان
چکیده
منابع مشابه
Modeling Data using Directional Distributions
Traditionally multi-variate normal distributions have been the staple of data modeling in most domains. For some domains, the model they provide is either inadequate or incorrect because of the disregard for the directional components of the data. We present a generative model for data that is suitable for modeling directional data (as can arise in text and gene expression clustering). We use m...
متن کاملRobust Modeling of Differential Gene Expression Data Using Normal/Independent Distributions: A Bayesian Approach
In this paper, the problem of identifying differentially expressed genes under different conditions using gene expression microarray data, in the presence of outliers, is discussed. For this purpose, the robust modeling of gene expression data using some powerful distributions known as normal/independent distributions is considered. These distributions include the Student's t and normal distrib...
متن کاملClassification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest
Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and smal...
متن کاملModeling data using directional distributions: Part II
High-dimensional data is central to most data mining applications, and only recently has it been modeled via directional distributions. In [Banerjee et al., 2003] the authors introduced the use of the von Mises-Fisher (vMF) distribution for modeling high-dimensional directional data, particularly for text and gene expression analysis. The vMF distribution is one of the simplest directional dist...
متن کاملPrediction of blood cancer using leukemia gene expression data and sparsity-based gene selection methods
Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method. Materials and Methods: In this descriptive study, the microarray ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: REVISTA BRASILEIRA DE BIOMETRIA
سال: 2021
ISSN: 1983-0823
DOI: 10.28951/rbb.v39i2.466