Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization
نویسندگان
چکیده
منابع مشابه
Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization
MOTIVATION A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of t...
متن کاملIntegrative and Personalized QSAR Analysis in Cancer by Kernelized Bayesian Matrix Factorization
With data from recent large-scale drug sensitivity measurement campaigns, it is now possible to build and test models predicting responses for more than one hundred anticancer drugs against several hundreds of human cancer cell lines. Traditional quantitative structure-activity relationship (QSAR) approaches focus on small molecules in searching for their structural properties predictive of the...
متن کاملKernelized Matrix Factorization for Collaborative Filtering
Matrix factorization (MF) methods have shown great promise in collaborative filtering (CF). Conventional MF methods usually assume that the correlated data is distributed on a linear hyperplane, which is not always the case. Kernel methods are used widely in SVMs to classify linearly non-separable data, as well as in PCA to discover the non-linear embeddings of data. In this paper, we present a...
متن کاملNonparametric Bayesian Matrix Factorization by Power-EP
Many real-world applications can be modeled by matrix factorization. By approximating an observed data matrix as the product of two latent matrices, matrix factorization can reveal hidden structures embedded in data. A common challenge to use matrix factorization is determining the dimensionality of the latent matrices from data. Indian Buffet Processes (IBPs) enable us to apply the nonparametr...
متن کاملScalable Bayesian Matrix Factorization
Matrix factorization (MF) is the simplest and most well studied factor based model and has been applied successfully in several domains. One of the standard ways to solve MF is by finding maximum a posteriori estimate of the model parameters, which is equivalent to minimizing the regularized objective function. Stochastic gradient descent (SGD) is a common choice to minimize the regularized obj...
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: Bioinformatics
سال: 2016
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/btw433