Compound Identification Using Penalized Linear Regression on Metabolomics
نویسندگان
چکیده
منابع مشابه
Compound Identification Using Penalized Linear Regression on Metabolomics.
Compound identification is often achieved by matching the experimental mass spectra to the mass spectra stored in a reference library based on mass spectral similarity. Because the number of compounds in the reference library is much larger than the range of mass-to-charge ratio (m/z) values so that the data become high dimensional data suffering from singularity. For this reason, penalized lin...
متن کاملCompound identification using penalized linear regression
s Service (CAS) registry number. In the simulation studies, we consider the mass spectra extracted from the NIST Chemistry WebBook (NIST library) as a reference library and the repetitive library as query (experimental) data. In addition, since we assume that the NIST library has the mass spectrum information for all the
متن کاملSparse Brain Network using Penalized Linear Regression
Sparse partial correlation is a useful connectivity measure for brain networks, especially, when it is hard to compute the exact partial correlation due to the small-n large-p situation. In this paper, we consider a sparse linear regression model with a l1-norm penalty for estimating sparse brain connectivity based on the partial correlation. For the numerical experiments, we construct the spar...
متن کاملPenalized Function-on-Function Regression
A general framework for smooth regression of a functional response on one or multiple functional predictors is proposed. Using the mixed model representation of penalized regression expands the scope of function-on-function regression to many realistic scenarios. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple functional predicto...
متن کاملOutlier Detection Using Nonconvex Penalized Regression
This paper studies the outlier detection problem from the point of view of penalized regressions. Our regression model adds one mean shift parameter for each of the n data points. We then apply a regularization favoring a sparse vector of mean shift parameters. The usual L1 penalty yields a convex criterion, but we find that it fails to deliver a robust estimator. The L1 penalty corresponds to ...
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: Journal of Modern Applied Statistical Methods
سال: 2016
ISSN: 1538-9472
DOI: 10.22237/jmasm/1462076340