Results 131 to 140 of about 245,585 (258)

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Regularization Paths for Generalized Linear Models via Coordinate Descent

Journal of Statistical Software, 2010
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman, Trevor Hastie, Rob Tibshirani   +2 more
doaj  

Identification of Exhaled Volatile Organic Compounds Biomarkers for Lung Cancer Under Data‐Limited Conditions Using Data Augmentation and Multi‐View Feature Selection

Advanced Intelligent Discovery, EarlyView.
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren, Bohao Liu, Zhiyuan Qu, Xue Jiang, Heng Zhao, Danyao Qu, Ruizhi Ning, Chen Su, Taoping Liu, Guangjian Zhang, Weiwei Wu   +10 more
wiley   +1 more source

The Challenge of Handling Structured Missingness in Integrated Data Sources

Advanced Intelligent Discovery, EarlyView.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson, Marius Hofert, Eleni‐Rosalina Andrinopoulou, Niels Hagenbuch, Sarah McGough, Chris Harbron, Robin Mitra   +6 more
wiley   +1 more source

Maize yield estimation at different growth stage using weather variables by LASSO, elastic net and stepwise multiple linear regression techniques. [PDF]

Sci Rep
Vashisth A, Aravind KS.
europepmc   +1 more source

Predicting Postresection Colorectal Liver Metastases Recurrence Using Advanced Graph Neural Networks with Explainability and Causal Inference

Advanced Intelligent Systems, EarlyView.
This study introduces a framework that combines graph neural networks with causal inference to forecast recurrence and uncover the clinical and pathological factors driving it. It further provides interpretability, validates risk factors via counterfactual and interventional analyses, and offers evidence‐based insights for treatment planning ...
Jubair Ahmed, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Sami Azam   +3 more
wiley   +1 more source

Dynamic mode decomposition of resting-state fMRI revealing abnormal brain region features in schizophrenia. [PDF]

Front Comput Neurosci
Wang Y, Wang Y, Xu X, Pan X.
europepmc   +1 more source

RPSLearner: A Novel Approach Based on Random Projection and Deep Stacking Learning for Categorizing Non‐Small Cell Lung Cancer

Advanced Intelligent Systems, EarlyView.
Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley   +1 more source

Cross-ancestry information transfer framework improves protein abundance prediction and protein-trait association identification. [PDF]

Brief Bioinform
Zhai W, Sun L, Fang W, Dong Y, Cheng C, Liu Y, Zhou Y, Ji J, Wu L, Pan A, Gamazon ER, Pan XF, Zhou D.   +12 more
europepmc   +1 more source