Journal ArticleDOI
PLS-regression: a basic tool of chemometrics
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TLDR
PLS-regression (PLSR) as mentioned in this paper is the PLS approach in its simplest, and in chemistry and technology, most used form (two-block predictive PLS) is a method for relating two data matrices, X and Y, by a linear multivariate model.About:
This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 2001-10-28. It has received 7861 citations till now. The article focuses on the topics: Partial least squares regression.read more
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Comparative QSARs for antimalarial endochins: Importance of descriptor-thinning and noise reduction prior to feature selection
Probir Kumar Ojha,Kunal Roy +1 more
TL;DR: It has been found that models developed from variable selection by stepwise regression followed by GFA and G/PLS are the best two models for QSAR models.
Journal ArticleDOI
Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.
TL;DR: Recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research, focusing on studies that used structural magnetic resonance imaging (MRI) and studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI.
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A regionalized national universal kriging model using Partial Least Squares regression for estimating annual PM2.5 concentrations in epidemiology.
Paul D. Sampson,Mark A. Richards,Adam A. Szpiro,Silas Bergen,Lianne Sheppard,Timothy V. Larson,Joel D. Kaufman +6 more
TL;DR: A regionalized national universal kriging model for annual average fine particulate matter monitoring data across the U.S. with a very high level of cross-validated accuracy of prediction with an overall R2 of 0.88 and well-calibrated predictive intervals is introduced.
Journal ArticleDOI
Machine learning for renewable energy materials
TL;DR: Achieving the 2016 Paris agreement goal of limiting global warming below 2 °C and securing a sustainable energy future require materials innovations in renewable energy technologies.
Journal ArticleDOI
Rationally designed families of orthogonal RNA regulators of translation.
Vivek K. Mutalik,Lei S. Qi,Joao C. Guimaraes,Joao C. Guimaraes,Julius B. Lucks,Julius B. Lucks,Adam P. Arkin +6 more
TL;DR: Rationally designed variants of the RNA-IN-RNA-OUT antisense RNA-mediated translation system from the insertion sequence IS10 are used to quantify >500 RNA-RNA interactions in Escherichia coli and integrate the data set with sequence-activity modeling to identify the thermodynamic stability of the duplex and the seed region as the key determinants of specificity.
References
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Book
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
TL;DR: In this article, the authors present a method for detecting and assessing Collinearity of observations and outliers in the context of extensions to the Wikipedia corpus, based on the concept of Influential Observations.
Book
A User's Guide to Principal Components
TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.
Journal ArticleDOI
A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation
Bradley Efron,Gail Gong +1 more
TL;DR: This paper reviewed the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule, at a relaxed mathematical level, omitting most proofs, regularity conditions and technical details.