Results 51 to 60 of about 962,551 (312)

RKIP overexpression reduces lung adenocarcinoma aggressiveness and sensitizes cells to EGFR‐targeted therapies

open access: yesMolecular Oncology, EarlyView.
RKIP, a metastasis suppressor protein, modulates key oncogenic pathways in lung adenocarcinoma. In silico analyses linked low RKIP expression to poor survival. Functional studies revealed RKIP overexpression reduces tumor aggressiveness and enhances sensitivity to EGFR‐targeted therapies, while its loss promotes resistance.
Ana Raquel‐Cunha   +10 more
wiley   +1 more source

Characterizing the salivary RNA landscape to identify potential diagnostic, prognostic, and follow‐up biomarkers for breast cancer

open access: yesMolecular Oncology, EarlyView.
This study explores salivary RNA for breast cancer (BC) diagnosis, prognosis, and follow‐up. High‐throughput RNA sequencing identified distinct salivary RNA signatures, including novel transcripts, that differentiate BC from healthy controls, characterize histological and molecular subtypes, and indicate lymph node involvement.
Nicholas Rajan   +9 more
wiley   +1 more source

Comparison of Confidence Intervals for the Poisson Mean

open access: yesRevstat Statistical Journal, 2012
We perform a comparative study among nineteen methods of interval estimation of the Poisson mean, in the intervals (0,2), [2,4] and (4,50], using as criteria coverage, expected length of confidence intervals, balance of noncoverage probabilities, E(P ...
V.V. Patil , H.V. Kulkarni
doaj   +1 more source

The influence of ROS1 fusion partners and resistance mechanisms in ROS1‐TKI‐treated non‐small cell lung cancer patients

open access: yesMolecular Oncology, EarlyView.
This real‐world study of ROS1+ NSCLC highlights fusion diversity, treatment outcomes with crizotinib and lorlatinib, and in vitro experiments with resistance mechanisms. G2032R drives strong resistance to ROS1‐targeted TKIs, especially lorlatinib. Fusion partner location does not affect overall survival to crizotinib or lorlatinib. Findings support the
Fenneke Zwierenga   +8 more
wiley   +1 more source

Testing Statistical Hypotheses Based on Fuzzy Confidence Intervals

open access: yesAustrian Journal of Statistics, 2016
A fuzzy test for testing statistical hypotheses about an imprecise parameter is proposed for the case when the available data are also imprecise. The proposed method is based on the relationship between the acceptance region of statistical tests at level
Jalal Chachi   +2 more
doaj   +1 more source

Tumor‐agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell‐free DNA fragmentomics

open access: yesMolecular Oncology, EarlyView.
We evaluated circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer using DNA methylation, cell‐free DNA fragment lengths, and 5′ end motifs. Machine learning models were trained to estimate ctDNA levels from each feature and their combination.
Morten Lapin   +10 more
wiley   +1 more source

Gut microbiota diversity is prognostic in metastatic hormone receptor‐positive breast cancer patients receiving chemotherapy and immunotherapy

open access: yesMolecular Oncology, EarlyView.
In this exploratory study, we investigated the relationship between the gut microbiota and outcome in patients with metastatic hormone receptor‐positive breast cancer, treated in a randomized clinical trial with chemotherapy alone or chemotherapy in combination with immune checkpoint blockade.
Andreas Ullern   +7 more
wiley   +1 more source

Confidence intervals using contrasts for regression model [PDF]

open access: yesSongklanakarin Journal of Science and Technology (SJST), 2009
A graph of confidence intervals can be used to report results from a regression model with explanatory variables asfactors. In this paper we describe a method for computing and displaying confidence intervals using weighted sum contraststo compare ...
Phattrawan Tongkumchum, Don McNeil
doaj  

Comparison of the Frequentist MATA Confidence Interval with Bayesian Model-Averaged Confidence Intervals

open access: yesJournal of Probability and Statistics, 2015
Model averaging is a technique used to account for model uncertainty, in both Bayesian and frequentist multimodel inferences. In this paper, we compare the performance of model-averaged Bayesian credible intervals and frequentist confidence intervals ...
Daniel Turek
doaj   +1 more source

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