Results 111 to 120 of about 11,928,939 (371)

Enhancing patient engagement in cancer research: a focus on patient‐centric approaches to scientific discovery

Molecular Oncology, EarlyView.
Patient engagement involves actively including patients in healthcare decisions and research to ensure care and studies align with their needs. This approach improves outcomes, trust, and communication while fostering collaboration between patients and professionals.
Estela Cepeda, Marina Reguero, Vanesa Abón, Marta Puyol   +3 more
wiley   +1 more source

Twelve-month outcomes of a community-based, father-daughter physical activity program delivered by trained facilitators

International Journal of Behavioral Nutrition and Physical Activity
Background Dads and Daughters Exercising and Empowered (DADEE) is a program targeting fathers/father-figures to improve their daughters’ physical activity and well-being.
Lee M. Ashton, Anna T. Rayward, Emma R. Pollock, Stevie-Lee Kennedy, Myles D. Young, Narelle Eather, Alyce T. Barnes, Daniel R. Lee, Philip J. Morgan   +8 more
doaj   +1 more source

Adversarial Learning: A Critical Review and Active Learning Study

, 2017
This papers consists of two parts. The first is a critical review of prior art on adversarial learning, identifying some significant limitations of previous works.
Hu, Xinyi, Kesidis, George, Miller, David J., Qiu, Zhicong   +3 more
core   +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

Distributed Active Learning

IEEE Access, 2016
Active learning aims at obtaining high-accuracy models with as a few labeled data as possible, by iteratively and elaborately selecting most valuable data to query labels during the learning process, thereby the cost of labeling data can be reduced. Most
Pengcheng Shen, Chunguang Li, Zhaoyang Zhang   +2 more
doaj   +1 more source

Agnostic Active Learning Without Constraints [PDF]

, 2010
We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from ...
Beygelzimer, Alina, Hsu, Daniel, Langford, John, Zhang, Tong   +3 more
core   +3 more sources

ACTIVE LEARNING

, 2022
Active learning is "a method of learning in which students are actively or experientially involved in the learning process and where there are different levels of active learning, depending on student involvement."[1] Bonwell & Eison (1991) states that "students participate [in active learning] when they are doing something besides passively ...
openaire   +2 more sources

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

Time, the final frontier

Molecular Oncology, EarlyView.
This article advocates integrating temporal dynamics into cancer research. Rather than relying on static snapshots, researchers should increasingly consider adopting dynamic methods—such as live imaging, temporal omics, and liquid biopsies—to track how tumors evolve over time.
Gautier Follain, Michal Dibus, Omkar Joshi, Guillaume Jacquemet   +3 more
wiley   +1 more source

Active Learning with Neural Networks: Insights from Nonparametric Statistics [PDF]

arXiv, 2022
Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning have been recently reported in the literature, however, rigorous label complexity ...
arxiv