Results 201 to 210 of about 34,016,939 (394)

How to link agricultural productivity, water availability and water demand in a risk context: a model for managing hydrological risks

, 1970
M. Gil-Sevilla, Alberto Garrido, Almudena Gómez‐Ramos   +2 more
openalex   +2 more sources

Elucidating prognostic significance of purine metabolism in colorectal cancer through integrating data from transcriptomic, immunohistochemical, and single‐cell RNA sequencing analysis

Molecular Oncology, EarlyView.
Low expression of five purine metabolism‐related genes (ADSL, APRT, ADCY3, NME3, NME6) was correlated with poor survival in colorectal cancer. Immunohistochemistry analysis showed that low NME3 (early stage) and low ADSL/NME6 (late stage) levels were associated with high risk.
Sungyeon Kim, Myunghee Kang, Soyeon Jeong, Jisup Kim, Kyoung Oh Kim, Won‐Suk Lee, Jeong‐Heum Baek, Jung Ho Kim, Seungyoon Nam   +8 more
wiley   +1 more source

Highly multiplexed digital PCR assay for simultaneous quantification of variant allele frequencies and copy number alterations of KRAS and GNAS in pancreatic cancer precursors

Molecular Oncology, EarlyView.
Combining melting curve analysis enhances the multiplexing capability of digital PCR. Here, we developed a 14‐plex assay to simultaneously measure single nucleotide mutations and amplifications of KRAS and GNAS, which are common driver genes in pancreatic cancer precursors. This assay accurately quantified variant allele frequencies in clinical samples
Junko Tanaka, Tatsuo Nakagawa, Yusuke Ono, Yoshio Kamura, Takeshi Ishida, Hidemasa Kawabata, Kenji Takahashi, Hiroki Sato, Andrew S. Liss, Yusuke Mizukami, Takahide Yokoi   +10 more
wiley   +1 more source

Mutations and prognosis in myelodysplastic syndromes: karyotype‐adjusted analysis of targeted sequencing in 300 consecutive cases and development of a genetic risk model

American journal of hematology/oncology, 2018
N. Gangat, M. Mudireddy, T. Lasho, C. Finke, M. Nicolosi, N. Szuber, M. Patnaik, A. Pardanani, C. Hanson, R. Ketterling, A. Tefferi   +10 more
semanticscholar   +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

Interdependence between CHD risk factors examined using linear models with and without transformation of data [PDF]

, 1988
Anna Bartkowiak, Sylwia Łukasik, K Chwistecki, M Mrukowicz   +3 more
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

An Integrated Approach to Importance Sampling and Machine Learning for Efficient Monte Carlo Estimation of Distortion Risk Measures in Black Box Models [PDF]

arXiv
Distortion risk measures play a critical role in quantifying risks associated with uncertain outcomes. Accurately estimating these risk measures in the context of computationally expensive simulation models that lack analytical tractability is fundamental to effective risk management and decision making. In this paper, we propose an efficient important
arxiv  

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

Toward a model for improved targeting of aged at risk of institutionalization.

, 1989
William G. Weissert, Cynthia M. Cready
openalex   +2 more sources