Results 221 to 230 of about 12,433,729 (358)

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

open access: yesMolecular 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

Pandemic preparedness: why humanities and social sciences matter. [PDF]

open access: yesFront Public Health
Frampton S   +12 more
europepmc   +1 more source

Methods matter: Exploring how expectations influence common actions

open access: yesiScience
Summary: Behavior in controlled laboratory studies is not always representative of what people do in daily life. This has prompted a recent shift toward conducting studies in natural settings.
Andrea Ghiani, David Mann, Eli Brenner
doaj  

The atypical KRASQ22K mutation directs TGF‐β response towards partial epithelial‐to‐mesenchymal transition in patient‐derived colorectal cancer tumoroids

open access: yesMolecular Oncology, EarlyView.
TGF‐β has a complex role in cancer, exhibiting both tumor‐suppressive and tumor‐promoting properties. Using a series of differentiated tumoroids, derived from different stages and mutational background of colorectal cancer patients, we replicate this duality of TGF‐β in vitro. Notably, the atypical but highly aggressive KRASQ22K mutation rendered early‐
Theresia Mair   +17 more
wiley   +1 more source

The BSSO Foundry: A community of practice for ontologies in the behavioural and social sciences. [PDF]

open access: yesWellcome Open Res
Hastings J   +12 more
europepmc   +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

open access: yesMolecular 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   +20 more
wiley   +1 more source

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