Results 31 to 40 of about 433,523 (290)

Machine Learning in Official Statistics

, 2018
In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and international statistical institutions and ...
Beck, Martin, Dumpert, Florian, Feuerhake, Joerg   +2 more
openaire   +2 more sources

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

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

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

Statistical Mechanics of Learning: A Variational Approach for Real Data [PDF]

Phys. Rev. Lett. 89 (10), 108302 (2002), 2002
Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone.
arxiv   +1 more source

Enhanced dose prediction for head and neck cancer artificial intelligence‐driven radiotherapy based on transfer learning with limited training data

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Training deep learning dose prediction models for the latest cutting‐edge radiotherapy techniques, such as AI‐based nodal radiotherapy (AINRT) and Daily Adaptive AI‐based nodal radiotherapy (DA‐AINRT), is challenging due to limited data.
Hui‐Ju Wang, Austen Maniscalco, David Sher, Mu‐Han Lin, Steve Jiang, Dan Nguyen   +5 more
wiley   +1 more source

Review of Charniak's "Statistical Language Learning" [PDF]

Computational Linguistics 21:1, 103-111, 1995
This article is an in-depth review of Eugene Charniak's book, "Statistical Language Learning". The review evaluates the appropriateness of the book as an introductory text for statistical language learning for a variety of audiences. It also includes an extensive bibliography of articles and papers which might be used as a supplement to this book for ...
arxiv  

Provable local learning rule by expert aggregation for a Hawkes network [PDF]

arXiv, 2023
We propose a simple network of Hawkes processes as a cognitive model capable of learning to classify objects. Our learning algorithm, named HAN for Hawkes Aggregation of Neurons, is based on a local synaptic learning rule based on spiking probabilities at each output node. We were able to use local regret bounds to prove mathematically that the network
arxiv  

The effect of multi‐leaf collimator leaf width on VMAT treatment plan quality

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Background The advent of volumetric modulated arc therapy (VMAT) in radiotherapy has made it one of the most commonly used techniques in clinical practice. VMAT is the delivery of intensity modulated radiation therapy (IMRT) while the gantry is in motion, and existing literature has shown it has decreased treatment delivery times and the ...
Gregory Sadharanu Peiris, Brendan Whelan, Nicholas Hardcastle, Suzie Lynn Sheehy   +3 more
wiley   +1 more source

From Curriculum Guidelines to Learning Objectives: A Survey of Five Statistics Programs [PDF]

arXiv, 2014
The 2000 ASA Guidelines for Undergraduate Statistics majors aimed to provide guidance to programs with undergraduate degrees in statistics as to the content and skills that statistics majors should be learning. With new guidelines forthcoming, it is important to help programs develop an assessment cycle of evaluation.
arxiv