Results 101 to 110 of about 1,497,290 (270)

Sample size determination [PDF]

, 1964
Arthur E. Mace
openalex   +1 more source

Comparing self‐reported race and genetic ancestry for identifying potential differentially methylated sites in endometrial cancer: insights from African ancestry proportions using machine learning models

Molecular Oncology, EarlyView.
Integrating ancestry, differential methylation analysis, and machine learning, we identified robust epigenetic signature genes (ESGs) and Core‐ESGs in Black and White women with endometrial cancer. Core‐ESGs (namely APOBEC1 and PLEKHG5) methylation levels were significantly associated with survival, with tumors from high African ancestry (THA) showing ...
Huma Asif, J. Julie Kim
wiley   +1 more source

Adaptive Clinical Trials and Sample Size Determination in the Presence of Measurement Error and Heterogeneity

Stats
Adaptive clinical trials offer a flexible approach for refining sample sizes during ongoing research to enhance their efficiency. This study delves into improving sample size recalculation through resampling techniques, employing measurement error and ...
Hassan Farooq, Sajid Ali, Ismail Shah, Ibrahim A. Nafisah, Mohammed M. A. Almazah   +4 more
doaj   +1 more source

The determination of sample size in treatment-control comparisons for chronic disease studies in which drop-out or non-adherence is a problem [PDF]

, 1967
M. Anthony Schork, Richard D. Remington
openalex   +1 more source

Some Empirical Evidence on the Effectiveness of Antimerger Relief in the United States [PDF]

The paper seeks to fill the void in the empirical economics literature of U.S. antimerger policy. A new empirical measure of relief secured in a Section 7 case is proposed. The extent of obtained relief is modeled as a fraction of the competitive overlap
Mikhail Kouliavtsev
core  

Nanoscale spin-polarization in dilute magnetic semiconductor (In,Mn)Sb

, 2008
Results of point contact Andreev reflection (PCAR) experiments on (In,Mn)Sb are presented and analyzed in terms of current models of charge conversion at a superconductor-ferromagnet interface.
A. Geresdi, A. Halbritter, B. Jankó, G. Mihály, J. K. Furdyna, M. Csontos, Sz. Csonka, T. Wojtowicz, T. Wojtowicz, X. Liu   +9 more
core   +1 more source

A large‐scale retrospective study in metastatic breast cancer patients using circulating tumour DNA and machine learning to predict treatment outcome and progression‐free survival

Molecular Oncology, EarlyView.
There is an unmet need in metastatic breast cancer patients to monitor therapy response in real time. In this study, we show how a noninvasive and affordable strategy based on sequencing of plasma samples with longitudinal tracking of tumour fraction paired with a statistical model provides valuable information on treatment response in advance of the ...
Emma J. Beddowes, Mario Ortega Duran, Solon Karapanagiotis, Alistair Martin, Meiling Gao, Riccardo Masina, Ramona Woitek, James Tanner, Fleur Tippin, Justine Kane, Jonathan Lay, Anja Brouwer, Stephen‐John Sammut, Suet‐Feung Chin, Davina Gale, Dana W. Y. Tsui, Sarah‐Jane Dawson, Nitzan Rosenfeld, Maurizio Callari, Oscar M. Rueda, Carlos Caldas   +20 more
wiley   +1 more source

Multidimensional OMICs reveal ARID1A orchestrated control of DNA damage, splicing, and cell cycle in normal‐like and malignant urothelial cells

Molecular Oncology, EarlyView.
Loss of the frequently mutated chromatin remodeler ARID1A, a subunit of the SWI/SNF cBAF complex, results in less open chromatin, alternative splicing, and the failure to stop cells from progressing through the cell cycle after DNA damage in bladder (cancer) cells. Created in BioRender. Epigenetic regulators, such as the SWI/SNF complex, with important
Rebecca M. Schlösser, Florian Krumbach, Eyleen Corrales, Geoffroy Andrieux, Christian Preisinger, Franziska Liss, Alexandra Golzmann, Melanie Boerries, Kerstin Becker, Ruth Knüchel, Stefan Garczyk, Bernhard Lüscher   +11 more
wiley   +1 more source

Determination of Sample Size for Distribution-free Tolerance Limits [PDF]

, 1970
William C. Guenther
openalex   +1 more source

Escape from TGF‐β‐induced senescence promotes aggressive hallmarks in epithelial hepatocellular carcinoma cells

Molecular Oncology, EarlyView.
Chronic TGF‐β exposure drives epithelial HCC cells from a senescent state to a TGF‐β resistant mesenchymal phenotype. This transition is characterized by the loss of Smad3‐mediated signaling, escape from senescence, enhanced invasiveness and metastatic potential, and upregulation of key resistance modulators such as MARK1 and GRM8, ultimately promoting
Minenur Kalyoncu, Dilara Demirci, Sude Eris, Bengisu Dayanc, Ece Cakiroglu, Merve Basol, Merve Uysal, Gulcin Cakan‐Akdogan, Fang Liu, Mehmet Ozturk, Gökhan Karakülah, Serif Senturk   +11 more
wiley   +1 more source