Sparse Matrix Factorization of Gene Expression Data
نویسندگان
چکیده
Motivation: Gene expression data consists of expression level reads for thousands of genes across dozens of experimental conditions, time points, cell types or repeated experiments. The goal of unsupervised modeling of such data is to find some underlying organization, structure or redundancy in the data, such as similarity or dependency between genes or between experiments. Such structure can then be used to assist in biological study of the gene expression patterns, or as a pre-processing step for classification and prediction tasks.
منابع مشابه
Voice-based Age and Gender Recognition using Training Generative Sparse Model
Abstract: Gender recognition and age detection are important problems in telephone speech processing to investigate the identity of an individual using voice characteristics. In this paper a new gender and age recognition system is introduced based on generative incoherent models learned using sparse non-negative matrix factorization and atom correction post-processing method. Similar to genera...
متن کاملProbabilistic sparse matrix factorization with an application to discovering gene functions in mouse mRNA expression data
We address the problem of sparse matrix factorization using a generative model. Our algorithm, probabilistic sparse matrix factorization (PSMF), is a probabilistic extension of a previous hard-decision algorithm for this problem (Srebro and Jaakkola 2001). PSMF allows for varying levels of sensor noise in the data, uncertainty in the hidden prototypes used to explain the data, and uncertainty a...
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 کاملImproving molecular cancer class discovery through sparse non-negative matrix factorization
MOTIVATION Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over ot...
متن کاملIterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...
متن کاملA social recommender system based on matrix factorization considering dynamics of user preferences
With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems...
متن کامل