Results 121 to 130 of about 113,531 (269)

NQO1‐Mediated Anoikis Resistance and Immune Evasion Define a High‐Risk Multi‐Omic Subtype for Precision Management of T1 High‐Grade Bladder Cancer

Advanced Science, EarlyView.
Multi‐omic profiling of T1 high‐grade bladder cancer identifies a high‐risk subtype (T1HG1) driven by NQO1, which couples anoikis resistance with immune evasion. NQO1 orchestrates macrophage–T cell crosstalk suppression via CXCL9 modulation. Pharmacological NQO1 inhibition with skullcapflavone II enhances cisplatin efficacy, representing a promising ...
Bin Guo, Chunru Xu, Shufan Fu, Qiang Cheng, Juan Li, Linkuo Shang, Gaojie Li, Yang Yang, Ying Wang, Yanqing Gong, Shengwei Xiong, Jian Fan, Changwei Yuan, Mei Zhang, Yifan Zuo, Elena Papaleo, Yue Shi, Yuan Liang, Xuesong Li, Hongzhao Li, Weimin Ci   +20 more
wiley   +1 more source

Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence

Applied Sciences
Automated machine learning (AutoML) is recognized for its efficiency in facilitating model development due to its ability to perform tasks autonomously, without constant human intervention.
Dagoberto Castellanos-Nieves, Luis García-Forte   +1 more
doaj   +1 more source

Cross‐Modal Denoising and Integration of Spatial Multi‐Omics Data with CANDIES

Advanced Science, EarlyView.
In this paper, we introduce CANDIES, which leverages a conditional diffusion model and contrastive learning to effectively denoise and integrate spatial multi‐omics data. We conduct extensive evaluations on diverse synthetic and real datasets, CANDIES shows superior performance on various downstream tasks, including denoising, spatial domain ...
Ye Liu, Wanpeng Zou, Yuekai Li, Jiayi Wang, Mingxuan Cai, Hongmin Cai   +5 more
wiley   +1 more source

Metaheuristic-Based Hyperparameter Optimization for Machine Learning Classification: An Applied Experimental Study

Iraqi Journal for Computers and Informatics
The selection of hyperparameters is a key factor in the predictive performance and the overall generalization of machine learning models. In real-life scenarios, poor hyperparameter selection tends to result in suboptimal performance, despite the use of ...
ahmed majid
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

Integrating Radiomics and Computational Pathology to Predict Early Recurrence of Pancreatic Ductal Adenocarcinoma and Uncover Its Biological Basis in Tumor Microenvironment

Advanced Science, EarlyView.
Accurate prediction of early recurrence in pancreatic ductal adenocarcinoma is vital for optimizing treatment. A novel, integrated radiomics‐pathology machine learning model successfully forecasts recurrence risks by analyzing preoperative CT images and computational pathology.
Sihang Cheng, Fuze Cong, Shenbo Zhang, Rui Lv, Wenjia Zhang, Xinyi Ke, Juncheng Wu, Zhonghe Zhao, Kui Zhao, Di Dong, Ruofan Zhang, Zhengyu Jin, Max Seidensticker, Zhiwei Wang, Huanwen Wu, Xianlin Han, Nan Hong, Huadan Xue   +17 more
wiley   +1 more source

Hyperparameter Optimization in Machine Learning

Foundations and Trends® in Machine Learning
Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies.
Franceschi, Luca, Donini, Michele, Perrone, Valerio, Klein, Aaron, Archambeau, Cédric, Seeger, Matthias, Pontil, Massimiliano, Frasconi, Paolo   +7 more
openaire   +2 more sources

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring

Advanced Science, EarlyView.
This study presents a semantic representation framework for clinically interpretable cardiac monitoring from contactless radio signals. It formulates radio semantic learning as an information‐bottleneck problem and approximates the objective via intra‐modal compression and cross‐modal alignment, structuring radio measurements into meaningful semantic ...
Jinbo Chen, Haoyu Wang, Guixin Xu, Yuqin Yuan, Xinmeng Cai, Zhengwu Wei, Dongheng Zhang, Qibin Sun, Tianwei Zhang, Cuntai Guan, Yan Chen   +10 more
wiley   +1 more source

A hybrid optimization and data-driven approach to understand the role of the risk-aversion profile parameter in portfolio optimization problems with shorting constraints

Operations Research Perspectives
This study contributes to the optimization literature with an approach that would help investors understand how the risk-aversion profile hyperparameter affects excess returns, risk, and Sharpe ratio curves in portfolio optimization problems with short ...
Mariano Carbonero-Ruz, Francisco Fernández-Navarro, Antonio M. Durán-Rosal, Javier Pérez-Rodríguez   +3 more
doaj   +1 more source