Results 131 to 140 of about 1,527,983 (199)

A comparison between radiomic biological age and chronological age in estimating kidney function. [PDF]

Sci Rep
Alikhani R, Horbal SR, Pai MP.
europepmc   +1 more source

Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma

Molecular Oncology, EarlyView.
This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles.
Wen‐Jing Wu, Jianchao Wang, Fuqing Chen, Xuefeng Wang, Bin Lan, Ruyi Fu, Hong Wen, Fangfang Chen, Wei Hong, Tian‐Yu Tang, Ying He, Gang Chen, Jianyin Zhou, Hai‐Long Piao, Di Chen, Shu‐Yong Lin   +15 more
wiley   +1 more source

FunFEA: an R package for fungal functional enrichment analysis. [PDF]

BMC Bioinformatics
Charest J, Loebenstein P, Mach RL, Mach-Aigner AR.   +3 more
europepmc   +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

Association Between Body Iron Status and Biological Aging. [PDF]

Nutrients
Von Holle A, Ramamurthy S, Díaz Santana MV, Kresovich JK, Taylor JA, Xu Z, O'Brien KM, Sandler DP, Weinberg CR.   +8 more
europepmc   +1 more source

Colliding dynamical complex network models: biological attractors versus attractors from material physics. [PDF]

Biophys J, 2012
Ma'ayan A.
europepmc   +1 more source

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

Molecular 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, Philip König, Milena Mijović, Jessica Kalla, Anil Baskan, Loan Tran, Kristina Draganić, Pedro Morata Saldaña, Carlos Uziel Pérez Malla, Janette Pfneissl, Andreas Tiefenbacher, Julijan Kabiljo, Velina S. Atanasova, Lisa Wozelka‐Oltjan, Leonhard Müllauer, Michael Bergmann, Raheleh Sheibani‐Tezerji, Gerda Egger   +17 more
wiley   +1 more source

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction. [PDF]

Sci Rep
Wang X, Ji J.
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

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

Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study. [PDF]

JMIR Aging
Jeong CU, Leiby JS, Kim D, Choe EK.
europepmc   +1 more source