Results 41 to 50 of about 1,145,773 (286)

Kernel Density Estimated Linear Regression

Proceedings of the International Florida Artificial Intelligence Research Society Conference
Regression analysis is a cornerstone of predictive modeling, with linear regression and kernel regression standing as two of its most prominent paradigms.
Roshan Kalpavruksha, Rohan Kalpavruksha, Teryn Cha, Sung-Hyuk Cha   +3 more
doaj   +1 more source

Sequential linear regression with online standardized data. [PDF]

PLoS ONE, 2018
The present study addresses the problem of sequential least square multidimensional linear regression, particularly in the case of a data stream, using a stochastic approximation process.
Kévin Duarte, Jean-Marie Monnez, Eliane Albuisson   +2 more
doaj   +1 more source

Disruption of SETD3‐mediated histidine‐73 methylation by the BWCFF‐associated β‐actin G74S mutation

FEBS Letters, EarlyView.
The β‐actin G74S mutation causes altered interaction of actin with SETD3, reducing histidine‐73 methylation efficiency and forming two distinct actin variants. The variable ratio of these variants across cell types and developmental stages contributes to tissue‐specific phenotypical changes. This imbalance may impair actin dynamics and mechanosensitive
Anja Marquardt, Marcus S. Münchhoff, Jacqueline Krohn, Philip M. Palarz, Manuel H. Taft, Johannes N. Greve, Nataliya Di Donato, Falk F. R. Buettner, Dietmar J. Manstein   +8 more
wiley   +1 more source

Neutrosophic Correlation and Simple Linear Regression [PDF]

Neutrosophic Sets and Systems, 2014
Since the world is full of indeterminacy, the neutrosophics found their place into contemporary research. The fundamental concepts of neutrosophic set, introduced by Smarandache.
A. A. Salama, O. M. Khaled, K. M. Mahfouz   +2 more
doaj  

Consequences of ignoring clustering in linear regression

BMC Medical Research Methodology, 2021
Background Clustering of observations is a common phenomenon in epidemiological and clinical research. Previous studies have highlighted the importance of using multilevel analysis to account for such clustering, but in practice, methods ignoring ...
Georgia Ntani, Hazel Inskip, Clive Osmond, David Coggon   +3 more
doaj   +1 more source

Rewriting the dendritic cell code in cancer—from subset identity to immunotherapeutic design

FEBS Letters, EarlyView.
Dendritic cells (DCs) play central roles in cancer immunity but are often subverted by the tumor microenvironment. This review explores the diversity of DC subsets, their functional plasticity, and emerging therapeutic strategies to reprogram DCs for enhanced antitumor responses, including vaccines, in vivo targeting, and DC‐based immunotherapies ...
Estevão Carlos Silva Barcelos, Doriana Ricciuti, Giada Mondanelli, Marco Gargaro   +3 more
wiley   +1 more source

Cell‐free DNA aneuploidy score as a dynamic early response marker in prostate cancer

Molecular Oncology, EarlyView.
mFast‐SeqS‐based genome‐wide aneuploidy scores are concordant with aneuploidy scores obtained by whole genome sequencing from tumor tissue and can predict response to ARSI treatment at baseline and, at an early time point, to ARSI and taxanes. This assay can be easily performed at low cost and requires little input of cfDNA. Cell‐free circulating tumor
Khrystany T. Isebia, Anouk C. de Jong, Lisanne F. van Dessel, Vanja de Weerd, Corine Beaufort, Jean Helmijr, José Alberto Nakauma‐González, Job van Riet, Paul Hamberg, Daniel Vis, Michiel S. van der Heijden, Nick Beije, Martijn P. Lolkema, Teoman Deger, Saskia M. Wilting, Ronald de Wit, Maurice P. H. M. Jansen, John W. M. Martens   +17 more
wiley   +1 more source

Response to neoadjuvant chemotherapy in early breast cancers is associated with epithelial–mesenchymal transition and tumor‐infiltrating lymphocytes

Molecular Oncology, EarlyView.
Epithelial–mesenchymal transition (EMT) and tumor‐infiltrating lymphocytes (TILs) are associated with early breast cancer response to neoadjuvant chemotherapy (NAC). This study evaluated EMT and TIL shifts, with immunofluorescence and RNA sequencing, at diagnosis and in residual tumors as potential biomarkers associated with treatment response.
Françoise Derouane, Jérôme Ambroise, Cédric van Marcke, Mieke Van Bockstal, Martine Berlière, Christine Galant, Hélène Dano, Médina Lougué, Elena Benidovskaya, Guy Jerusalem, Vincent Bours, Claire Josse, Jérôme Thiry, Aurélie Daumerie, Caroline Bouzin, Cyril Corbet, François P. Duhoux   +16 more
wiley   +1 more source

Structural Change Analysis in Linear Regression Model.

Revista de Matemática: Teoría y Aplicaciones, 2010
Assuming that the observations are from normal distribution we obtain de distribution of the maximum likelihood ratio test if there is a change in the parameters at an unknown time and we find the maximum likehood estimators of the time change too.
Blanca Rosa Pérez Salvador, María Guadalupe García Salazar   +1 more
doaj   +1 more source

Clustered linear regression [PDF]

Knowledge-Based Systems, 2002
Clustered linear regression (CLR) is a new machine learning algorithm that improves the accuracy of classical linear regression by partitioning training space into subspaces. CLR makes some assumptions about the domain and the data set. Firstly, target value is assumed to be a function of feature values.
Ari, B., Güvenir H.A.
openaire   +5 more sources