Results 61 to 70 of about 332,886 (299)

Flow Enabled Target Capture Halbach‐based magnetic enrichment increases circulating tumor cell capture from blood in metastatic cancer patients

Molecular Oncology, EarlyView.
Pair‐wise comparison of the CellSearch and FETCH enrichment technologies for circulating tumor cells (CTCs) from metastatic breast, prostate, and small cell lung cancer patients shows an increased capture of CTCs using FETCH enrichment. The clinical implementation of circulating tumor cells (CTCs) as a predictive tool for therapy efficacy in the ...
Michiel Stevens, Anouk Mentink, Tom Niessink, Frank Coumans, Leonie Mekenkamp, Ruchi Bansal, Jeroen Hiltermann   +6 more
wiley   +1 more source

Prompt Learning Based Parameter-efficient Code Generation [PDF]

Jisuanji kexue
Automatic code generation is one of the effective ways to improve the efficiency of software development.Existing research often regards code generation as a sequence-to-sequence task,and the process of fine-tuning of large-scale pre-trained language ...
XU Yiran, ZHOU Yu
doaj   +1 more source

Deficient Excitation in Parameter Learning

CoRR
16 pages,9 ...
Ganghui Cao, Shimin Wang, Martin Guay, Jinzhi Wang, Zhisheng Duan, Marios M. Polycarpou   +5 more
openaire   +2 more sources

Longitudinal genome‐wide aneuploidy measurements in circulating cell‐free DNA to predict lack of benefit from pembrolizumab in patients with metastatic urothelial cancer

Molecular Oncology, EarlyView.
Many patients with urothelial cancer do not benefit from treatment with pembrolizumab, while at risk of severe side effects. Changes in the levels of circulating tumor DNA early during treatment, measured by a simple and affordable assay that can be easily implemented in the clinic, can be used as a prognostic tool to identify these patients.
Youssra Salhi, Stavros Makrodimitris, Sandra van Wilpe, Iris te Paske, Vanja de Weerd, Corine M. Beaufort, Hans M. Westgeest, Jens Voortman, Maureen J. B. Aarts, Maud Rijnders, Astrid A. M. van der Veldt, Ronald de Wit, Niven Mehra, Saskia M. Wilting, Debbie G. J. Robbrecht   +14 more
wiley   +1 more source

Statistical Parameter Learning for Belief Networks with Fixed Structure [PDF]

, 1999
In this report, we address the problem of parameter learning for belief networks with fixed structure based on empirical observations. Both complete and incomplete (data) observations are included.
Li, Hongjun
core  

Inference and Model Parameter Learning for Image Labeling by Geometric Assignment [PDF]

, 2020
Image labeling is a fundamental problem in the area of low-level image analysis. In this work, we present novel approaches to maximum a posteriori (MAP) inference and model parameter learning for image labeling, respectively.
Hühnerbein, Ruben
core   +1 more source

Exploring Parameter Space in Reinforcement Learning

Paladyn, 2010
This paper discusses parameter-based exploration methods for reinforcement learning. Parameter-based methods perturb parameters of a general function approximator directly, rather than adding noise to the resulting actions.
Rückstieß Thomas, Sehnke Frank, Schaul Tom, Wierstra Daan, Sun Yi, Schmidhuber Jürgen   +5 more
doaj   +1 more source

Learning Resolution Parameters for Graph Clustering [PDF]

The World Wide Web Conference, 2019
Finding clusters of well-connected nodes in a graph is an extensively studied problem in graph-based data analysis. Because of its many applications, a large number of distinct graph clustering objective functions and algorithms have already been proposed and analyzed. To aid practitioners in determining the best clustering approach to use in different
Nate Veldt, David F. Gleich, Anthony Wirth   +2 more
openaire   +2 more sources

Liquid biopsy‐based diagnostic evaluation of hypermethylated CpG sites for ovarian cancer diagnosis

Molecular Oncology, EarlyView.
This schematic outlines the workflow from biomarker identification to duplex MethyLight assay validation for epithelial ovarian cancer diagnosis using cfDNA‐based liquid biopsy. Initial screening of hypermethylated CpG candidates (cg02957270, cg10061138 cg00480298, COL2A1) was performed in tissue using ARMS‐PCR, COBRA, qPCR and image analysis. Selected
Deepa Bisht, Sameer Gupta, Amrita Chaurasia, Manisha Sachan   +3 more
wiley   +1 more source

Probabilistic graphical models parameter learning with transferred prior and constraints [PDF]

, 2018
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially when training data are hard to acquire. Two approaches have been used to address this challenge: 1) introducing expert judgements and 2) transferring ...
Neil, M, Zhou, Y, Fenton, N, Hospedales, TM   +3 more
core