Results 61 to 70 of about 636,159 (270)

Unlocking the potential of tumor‐derived DNA in urine for cancer detection: methodological challenges and opportunities

Molecular Oncology, EarlyView.
Urine is a rich source of biomarkers for cancer detection. Tumor‐derived material is released into the bloodstream and transported to the urine. Urine can easily be collected from individuals, allowing non‐invasive cancer detection. This review discusses the rationale behind urine‐based cancer detection and its potential for cancer diagnostics ...
Birgit M. M. Wever, Renske D. M. Steenbergen   +1 more
wiley   +1 more source

Clinical applications of next‐generation sequencing‐based ctDNA analyses in breast cancer: defining treatment targets and dynamic changes during disease progression

Molecular Oncology, EarlyView.
Circulating tumor DNA (ctDNA) offers a possibility for different applications in early and late stage breast cancer management. In early breast cancer tumor informed approaches are increasingly used for detecting molecular residual disease (MRD) and early recurrence. In advanced stage, ctDNA provides a possibility for monitoring disease progression and
Eva Valentina Klocker, Samantha Hasenleithner, Rupert Bartsch, Simon P. Gampenrieder, Daniel Egle, Christian F. Singer, Gabriel Rinnerthaler, Michael Hubalek, Katja Schmitz, Zsuzsanna Bago‐Horvath, Andreas Petzer, Sonja Heibl, Ellen Heitzer, Marija Balic, Michael Gnant   +14 more
wiley   +1 more source

Data‐driven performance metrics for neural network learning

International Journal of Adaptive Control and Signal Processing, EarlyView., 2023
Summary Effectiveness of data‐driven neural learning in terms of both local mimima trapping and convergence rate is addressed. Such issues are investigated in a case study involving the training of one‐hidden‐layer feedforward neural networks with the extended Kalman filter, which reduces the search for the optimal network parameters to a state ...
Angelo Alessandri, Mauro Gaggero, Marcello Sanguineti   +2 more
wiley   +1 more source

Learning in Open Adaptive Networks [PDF]

2016 IEEE 10th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2016
We propose a learn-and-adapt model for building efficient and resilient networks of cooperative agents. Agents are involved into three interconnected types of activities. Firstly, agents bid for handling random structured tasks. Secondly, agents learn the (exogenous) features of the random task source, by aggregating local information (such as success ...
Vincent Danos, Guoli Yang
openaire   +5 more sources

NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples

Molecular Oncology, EarlyView.
Consensus molecular subtypes (CMS1‐4) have been identified to study colorectal cancer heterogeneity and serve as potential biomarkers. In this study, we developed and evaluated NanoCMSer, a NanoString‐based classifier using 55 genes, optimized for FF and FFPE to facilitate the clinical evaluation of CMS subtyping.
Arezo Torang, Simone van de Weerd, Veerle Lammers, Sander van Hooff, Inge van den Berg, Saskia van den Bergh, Miriam Koopman, Jan N. IJzermans, Jeanine M. L. Roodhart, Jan Koster, Jan Paul Medema   +10 more
wiley   +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

Adaptive learning of compressible strings

Theoretical Computer Science, 2021
Accepted for publication in Theoretical Computer ...
Fici G., Prezza N., Venturini R.
openaire   +5 more sources

Adaptation to Unknown Situations as the Holy Grail of Learning-Based Self-Adaptive Systems: Research Directions [PDF]

arXiv, 2021
Self-adaptive systems continuously adapt to changes in their execution environment. Capturing all possible changes to define suitable behaviour beforehand is unfeasible, or even impossible in the case of unknown changes, hence human intervention may be required.
arxiv  

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

Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine

FEBS Open Bio, EarlyView.
This review highlights how foundation models enhance predictive healthcare by integrating advanced digital twin modeling with multiomics and biomedical data. This approach supports disease management, risk assessment, and personalized medicine, with the goal of optimizing health outcomes through adaptive, interpretable digital simulations, accessible ...
Sakhaa Alsaedi, Xin Gao, Takashi Gojobori   +2 more
wiley   +1 more source