Results 71 to 80 of about 7,571,754 (304)

Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning

Bioengineering, 2022
There are three primary challenges in the automatic diagnosis of arrhythmias by electrocardiogram (ECG): the significant variation among individual patients, the multiple pathologies in the ECG signal and the high cost in annotating clinical ECG with the
Jie Yang, Jinfeng Li, Kun Lan, Anruo Wei, Han Wang, Shigao Huang, Simon Fong   +6 more
doaj   +1 more source

Epigenetic heterogeneity and plasticity in therapy‐induced tumor states through single‐cell multi‐omics

Molecular Oncology, EarlyView.
Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim, Hwiyeong Lee, Jin Hong, Daechan Park   +3 more
wiley   +1 more source

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

A federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean

, 2021
Learning based credit prediction has attracted great interest from academia and industry. Different institutions hold a certain amount of credit data with limited users to build model.
Li, Lin, Wang, Guowei, Zhang, Jiawei, Tao, Xiaohui, Bi, Jiaxiu, Long, Sijie   +5 more
core   +1 more source

An Efficient Stacking Model of Multi-Label Classification Based on Pareto Optimum

IEEE Access, 2019
Nowadays, multi-label data are ubiquitous in real-world applications, in which each instance is associated with a set of labels. Multi-label learning has attracted significant attentions from researchers and plenty of algorithms have been proposed. Among
Wei Weng, Chin-Ling Chen, Shun-Xiang Wu, Yu-Wen Li, Juan Wen   +4 more
doaj   +1 more source

Directed evolution of enzymes at the crossroads of tradition and innovation

FEBS Open Bio, EarlyView.
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova, Andrej Mirossay, Erik Sedlak   +2 more
wiley   +1 more source

Dynamic Online Label Distribution Feature Selection Based on Label Importance and Label Correlation

Applied Sciences
Existing feature selection methods mainly target single-label learning and multi-label learning, and only a few algorithms are optimized for label distribution learning.
Weiliang Chen, Xiao Sun, Fuji Ren
doaj   +1 more source

A Novel IGBT Health Evaluation Method Based on Multi-Label Classification

IEEE Access, 2019
The IGBT health evaluation of power semiconductor devices is usually based on the threshold evaluation method, which is usually a single characteristic parameter evaluation system. This kind of evaluation method cannot reflect the internal correlation of
Ruikun Quan, Hui Li, Yaogang Hu, Pei Gao
doaj   +1 more source

Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer

Aging and Cancer, EarlyView.
Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss, Alexis C. Wardell, Allison M. Deal, Kirsten A. Nyrop, Allison Ross, Yara G. Abdou, Jeffrey D. Aldrich, Lisa A. Cary, E. Claire Dees, Katherine E. Reeder‐Hayes, Emily M. Ray, Natalia Mitin, Denis Tsygankov   +12 more
wiley   +1 more source

Experiments with multi-view multi-instance learning for supervised image classification [PDF]

, 2011
In this paper we empirically investigate the benefits of multi-view multi-instance (MVMI) learning for supervised image classification. In multi-instance learning, examples for learning contain bags of feature vectors and thus data from different views ...
Mayo, Michael, Frank, Eibe
core