Results 31 to 40 of about 1,129,295 (286)

Machine learning renormalization group for statistical physics

Machine Learning: Science and Technology, 2023
Abstract We develop a machine-learning renormalization group (MLRG) algorithm to explore and analyze many-body lattice models in statistical physics. Using the representation learning capability of generative modeling, MLRG automatically learns the optimal renormalization group (RG) transformations from self-generated spin configurations
Wanda Hou, Yi-Zhuang You
openaire   +3 more sources

Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

PLoS ONE, 2023
Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset.
Alexander A. Huang, Samuel Y. Huang
semanticscholar   +1 more source

Application of statistical machine learning in biomarker selection

Scientific Reports, 2023
AbstractIn the recent JAVELIN Bladder 100 phase 3 trial, avelumab plus best supportive care significantly prolonged overall survival relative to best supportive care alone as first-line maintenance therapy following first-line platinum-based chemotherapy in patients with advanced urothelial cancer (aUC).
Ritwik Vashistha, Zubdahe Noor, Shibasish Dasgupta, Jie Pu, Shibing Deng   +4 more
openaire   +3 more sources

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Analytical and Bioanalytical Chemistry, 2023
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and ...
Diego P. dos Santos, M. Sena, M. R. Almeida, I. Mazali, A. Olivieri, Javier E. L. Villa   +5 more
semanticscholar   +1 more source

Predicting the Onset of Diabetes with Machine Learning Methods

Journal of Personalized Medicine, 2023
The number of people suffering from diabetes in Taiwan has continued to rise in recent years. According to the statistics of the International Diabetes Federation, about 537 million people worldwide (10.5% of the global population) suffer from diabetes ...
Chun-Yang Chou, Ding-Yang Hsu, Chun-Hung Chou   +2 more
semanticscholar   +1 more source

From omics to AI—mapping the pathogenic pathways in type 2 diabetes

FEBS Letters, EarlyView.
Integrating multi‐omics data with AI‐based modelling (unsupervised and supervised machine learning) identify optimal patient clusters, informing AI‐driven accurate risk stratification. Digital twins simulate individual trajectories in real time, guiding precision medicine by matching patients to targeted therapies.
Siobhán O'Sullivan, Lu Qi, Pierre Zalloua   +2 more
wiley   +1 more source

A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms

Applied Sciences, 2023
In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science ...
Shahneela Pitafi, Toni Anwar, Zubair Sharif   +2 more
semanticscholar   +1 more source

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

Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes

Nature Communications, 2020
The microstructure of a composite electrode determines how individual battery particles are charged and discharged in a lithium-ion battery. It is a frontier challenge to experimentally visualize and, subsequently, to understand the electrochemical ...
Zhisen Jiang, Jizhou Li, Yang Yang, Linqin Mu, Chenxi Wei, Xiqian Yu, P. Pianetta, K. Zhao, P. Cloetens, Feng Lin, Yijin Liu   +10 more
semanticscholar   +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