Results 61 to 70 of about 1,435,261 (265)
Least Squares Optimization: From Theory to Practice
Robotics, 2020 Nowadays, Nonlinear Least-Squares embodies the foundation of many Robotics and Computer Vision systems. The research community deeply investigated this topic in the last few years, and this resulted in the development of several open-source solvers to ...
Giorgio Grisetti +5 more
doaj +1 more source
A synthetic benzoxazine dimer derivative targets c‐Myc to inhibit colorectal cancer progression
Molecular Oncology, EarlyView.Benzoxazine dimer derivatives bind to the bHLH‐LZ region of c‐Myc, disrupting c‐Myc/MAX complexes, which are evaluated from SAR analysis. This increases ubiquitination and reduces cellular c‐Myc. Impairing DNA repair mechanisms is shown through proteomic analysis.
Nicharat Sriratanasak +8 more
wiley +1 more source
Local Regularization Assisted Orthogonal Least Squares Regression
, 2006 A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well.
Chen, S.
core +1 more source
Molecular Oncology, EarlyView.Adaptaquin selectively kills glioma stem cells while sparing differentiated brain cells. Transcriptomic and proteomic analyses show Adaptaquin disrupts iron and cholesterol homeostasis, with iron chelation amplifying cytotoxicity via cholesterol depletion, mitochondrial dysfunction, and elevated reactive oxygen species.
Adrien M. Vaquié +16 more
wiley +1 more source
A novel interpretation of least squares solution
International Journal of Mathematics and Mathematical Sciences, 1992 We show that the well-known least squares (LS) solution of an overdetermined system of linear equations is a convex combination of all the non-trivial solutions weighed by the squares of the corresponding denominator determinants of the Cramer's rule ...
Jack-Kang Chan
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Marginal Screening for Partial Least Squares Regression
IEEE Access, 2017 Partial least squares (PLS) regression is a versatile modeling approach for high-dimensional data analysis. Recently, PLS-based variable selection has attracted great attention due to high-throughput data reduction and modeling interpretability.
Naifei Zhao, Qingsong Xu, Hong Wang
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New Developments in Sparse PLS Regression
Frontiers in Applied Mathematics and Statistics, 2021 Methods based on partial least squares (PLS) regression, which has recently gained much attention in the analysis of high-dimensional genomic datasets, have been developed since the early 2000s for performing variable selection.
Jérémy Magnanensi +6 more
doaj +1 more source
Implicitly Constrained Semi-Supervised Least Squares Classification
, 2015 We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the ...
B Widrow +16 more
core +1 more source
Molecular Oncology, EarlyView.Clinical trials on PARP inhibitors in urothelial carcinoma (UC) showed limited efficacy and a lack of predictive biomarkers. We propose SLFN5, SLFN11, and OAS1 as UC‐specific response predictors. We suggest Talazoparib as the better PARP inhibitor for UC than Olaparib.
Jutta Schmitz +15 more
wiley +1 more source
Least Squares Consensus for Matching Local Features
Information, 2019 This paper presents a new approach to estimate the consensus in a data set. Under the framework of RANSAC, the perturbation on data has not been considered sufficiently.
Qingming Zhang, Buhai Shi, Haibo Xu
doaj +1 more source