Results 221 to 230 of about 2,065,553 (347)

DeepCCDS: Interpretable Deep Learning Framework for Predicting Cancer Cell Drug Sensitivity through Characterizing Cancer Driver Signals

Advanced Science, EarlyView.
DeepCCDS leverages prior knowledge and self‐supervised learning to model cancer driver signals for drug sensitivity prediction. It captures complex regulatory patterns enabling more biologically informed representations. The framework outperforms existing methods across datasets, offering improved accuracy and interpretability.
Jiashuo Wu, Jiyin Lai, Xilong Zhao, Ziyi Wang, Yongbao Zhang, Liqiang Wang, Yinchun Su, Yalan He, Siyuan Li, Ying Jiang, Junwei Han   +10 more
wiley   +1 more source

Using Nearest-Neighbor Distributions to Quantify Machine Learning of Materials' Microstructures. [PDF]

Entropy (Basel)
Rickman JM, Barmak K, Patrick MJ, Mensah GA.   +3 more
europepmc   +1 more source

A detailed analysis of game statistics of professional tennis players: An inferential and machine learning approach

Michal Bozděch, Dominik Puda, Pavel Grasgruber   +2 more
openalex   +1 more source

Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information [PDF]

Christoph Jansen
openalex   +1 more source

Dual‐Energy CT‐Based Assessment of Thrombotic Heterogeneity for Predicting Stroke Source and Response to Machine Thrombectomy: A Step Toward Visualization Thrombus Treatment

Advanced Science, EarlyView.
The research indicates that dual energy CT scanning can accurately predict the stroke source and clinical outcomes by analyzing thrombus heterogeneity. Based on histopathological analysis of thrombus components, these scans are better able to display thrombus details than standard CT scans, which is helpful for the development of visual navigation ...
Jingxuan Jiang, Sijia Wang, Fan Xiao, Hongmei Gu, Mingkang Wang, Hao Tian, Baohui Guan, Kai Sheng, Yijia Xiong, Huilin Zhao, Minda Li, Li Xu, Zheng Sun, Haiyan Du, Wenxian Du, Yuehua Li   +15 more
wiley   +1 more source

Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? [PDF]

PLoS One
Ehsanul Karim M, Lei Y.
europepmc   +1 more source

A combination of multivariate statistics and machine learning techniques in groundwater characterization and quality forecasting

Mahamuda Abu, Rabiu Musah, Musah Saeed Zango   +2 more
openalex   +1 more source

BrainFusion: a Low‐Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research

Advanced Science, EarlyView.
BrainFusion is a low‐code software framework for multimodal brain–computer interface (BCI)​​ and brain–body interaction research. It supports ​electroencephalography (EEG)​, functional near‐infrared spectroscopy (fNIRS), electromyography (EMG)​, and electrocardiography (ECG)​​ integration with standardized preprocessing, feature fusion, and model ...
Wenhao Li, Chenyang Gao, Zhaobo Li, Yunheng Diao, Jiaxin Li, Jiayi Zhou, Jing Zhou, Ying Peng, Guanchu Chen, Xuecheng Wu, Kai Wu   +10 more
wiley   +1 more source

Spatiotemporal patterns and climate-induced macroeconomic burden of malaria in sub-Saharan Africa. [PDF]

BMC Public Health
Yao T, Yang X, Ye H, Wang Y, Yu X, Huang Y, Xu M.   +6 more
europepmc   +1 more source

Frontotemporal dementia subtyping using machine learning, multivariate statistics, and neuroimaging

Amelie Metz, Yashar Zeighami, Simon Ducharme, Sylvia Villeneuve, Mahsa Dadar   +4 more
openalex   +1 more source