Results 241 to 250 of about 549,710 (321)

Biomarker‐Agnostic Detection of Ovarian Cancer from Blood Plasma Using a Machine Learning‐Driven Electronic Nose

Advanced Intelligent Systems, EarlyView.
This study introduces a biomarker‐agnostic diagnostic strategy for ovarian cancer, utilizing a machine learning‐enhanced electronic nose to analyze volatile organic compound signatures from blood plasma. By overcoming the dependence on specific biomarkers, this approach enables accurate detection, staging, and cancer type differentiation, offering a ...
Ivan Shtepliuk, Lingyin Meng, Christer Borgfeldt, Jens Eriksson, Donatella Puglisi   +4 more
wiley   +1 more source

Bayesian statistics: relevant for the brain? [PDF]

Curr Opin Neurobiol, 2014
Kording KP.
europepmc   +1 more source

Optisense: Computational Optimization for Strain Sensor Placement in Wearable Motion Tracking Systems

Advanced Intelligent Systems, EarlyView.
A computational framework for optimizing strain sensor placement in wearable motion tracking systems is presented. By combining dense strain mapping with a genetic algorithm, the method discovers counterintuitive yet highly effective configurations that reduce joint angle error by 32%.
Minu Kim, Juhyung Ahn, Jieun Seo, Steve Park, Joo Yong Sim   +4 more
wiley   +1 more source

Exploring Spatial Trends and Influencing Factors for Gastric Cancer Based on Bayesian Statistics: A Case Study of Shanxi, China. [PDF]

Int J Environ Res Public Health, 2018
Zhang G, Li J, Li S, Wang Y.
europepmc   +1 more source

Feature Disentangling and Combination Implemented by Spin–Orbit Torque Magnetic Tunnel Junctions

Advanced Intelligent Systems, EarlyView.
Spin–orbit torque magnetic tunnel junctions (SOT‐MTJs) enable efficient feature disentangling and integration in image data. A proposed algorithm leverages SOT‐MTJs as true random number generators to disentangle and recombine features in real time, with experimental validation on emoji and facial datasets.
Xiaohan Li, Yingqian Xu, Caihua Wan, Ran Zhang, Meike Hindenberg, Raik Hoffmann, Shiqiang Liu, Tianyi Zhang, Shilong Xiong, Dehao Kong, Jihao Xia, Fangshuo Gao, Zhuyang Nie, Thomas Kämpfe, Guoqiang Yu, Xiufeng Han   +15 more
wiley   +1 more source

Improved Method for Determining Absolute Phosphorylation Stoichiometry Using Bayesian Statistics and Isobaric Labeling. [PDF]

J Proteome Res, 2017
Lim MY, O'Brien J, Paulo JA, Gygi SP.
europepmc   +1 more source

Modeling Soot Oxidation and Gasification with Bayesian Statistics

, 2017
Alexander Jon Josephson, Neal Gaffin, Sean T. Smith, Thomas H. Fletcher, David O. Lignell   +4 more
openalex   +2 more sources

Metalearning‐Driven Inverse Optimization for Precision Microstructure Fabrication in Digital Light Processing Three‐Dimensional Printing

Advanced Intelligent Systems, EarlyView.
Metalearning‐based inverse optimization enables precise microscale three‐dimensional printing using a DLP system. Distorted structures from conventional printing are analyzed via neural network regression, which predicts optimal exposure time and mask design.
Jae Won Choi, Suhyeong Lim, Jeongsu Lee, Cheol Woo Ha   +3 more
wiley   +1 more source

Using Bayesian statistics to estimate the likelihood a new trial will demonstrate the efficacy of a new treatment. [PDF]

BMC Med Res Methodol, 2017
Biau DJ, Boulezaz S, Casabianca L, Hamadouche M, Anract P, Chevret S.   +5 more
europepmc   +1 more source

Bayesian Core: A Practical Approach to Bayesian Computational Statistics [PDF]

Joseph Hilbe
core   +1 more source