Results 61 to 70 of about 22,279 (217)

Artificial intelligence strategies for predicting kinase inhibitor resistance: A comprehensive review of methods, challenges, and future perspectives

Journal of Intelligent Medicine, EarlyView.
Abstract Kinase inhibitors are essential in targeted cancer therapy, yet resistance often emerges through secondary mutations, activation of compensatory signaling pathways, or drug‐efflux mechanisms. Artificial intelligence (AI) provides a workflow‐based strategy rather than a list of unrelated tools for predicting and addressing kinase‐inhibitor ...
Faris Hassan, Mohanad Ali Deifallah, Alaa Zaghloul, Rania Elgohary   +3 more
wiley   +1 more source

Proteomics of Nitrotyrosine: Integrating Mass Spectrometry and Immunodetection in Redox‐Driven Pathology

Mass Spectrometry Reviews, EarlyView.
ABSTRACT Nitrooxidative stress, driven by excess reactive nitrogen species like peroxynitrite, contributes to the pathogenesis of many chronic diseases. Among its molecular footprints, 3‐nitrotyrosine (3NT) has emerged as a biologically relevant marker of protein nitration.
Brîndușa Alina Petre
wiley   +1 more source

Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer

Frontiers in Energy Research
Given the complexity and dynamic nature of short-term load sequence data, coupled with prevalent errors in traditional forecasting methods, this study introduces a novel approach for short-term load forecasting.
Kaiyuan Hou, Xiaotian Zhang, Junjie Yang, Jiyun Hu, Guangzhi Yao, Jiannan Zhang   +5 more
doaj   +1 more source

Identification of weld sub-surface defects by radiographic images using texture features [PDF]

E3S Web of Conferences
Non-Destructive Testing (NDT) is important to detect sub-surface defects in the weldments to ensure the quality of weld joints. The weld radiographs are digitized using a high-resolution digital camera.
Ramana E.V., Penekalapati Sai Varun, Kumar Namala Kiran   +2 more
doaj   +1 more source

A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples [PDF]

, 2023
Deliang Sun, Xiaoqing Wu, Haijia Wen, Qingyu Gu   +3 more
openalex   +1 more source

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Med Research, EarlyView.
The integration of AI/ML into nanomedicine offers a transformative approach to therapeutic design and optimization. Unlike conventional empirical methods, AI/ML models (such as classification, regression, and neural networks) enable the analysis of complex clinical and formulation datasets to predict optimal nanoparticle characteristics and therapeutic
Rohan Chand Sahu, Sanchit Arora, Dinesh Kumar, Ashish Kumar Agrawal   +3 more
wiley   +1 more source

Wind Speed Multi-Mode Ensemble Forecasting for Wind Farms Based on Machine Learning

南方能源建设
[Introduction] With the extensive construction of wind farms, the combination of researches on different machine learning algorithms and meteorological forecasting modes has received widespread attention.
Sheng GAO, Peihua XU, Zhenghong CHEN
doaj   +1 more source

An Interpretable TCN– Transformer Framework for Lithium‐Ion Battery State of Health Estimation Using SHAP Analysis

Quality and Reliability Engineering International, EarlyView.
ABSTRACT Accurate state of health (SOH) estimation of Li‐ion batteries is essential for ensuring safety, reliability, and prolonging battery lifespan in energy storage systems and electric vehicles. This study proposes a hybrid temporal convolutional network (TCN)–transformer framework that effectively captures both short‐term temporal dynamics and ...
Fusen Guo, Zhibo Zhang, Xuzhe Ma, Lin Li, Hailing Zhou, Chaojie Li, Huadong Mo   +6 more
wiley   +1 more source

Machine Learning on Systematically Curated Data Reveals Key Determinants of Magnetic Hyperthermia Performance

Small, EarlyView.
This study presents a machine‐learning (ML) framework to predict the specific absorption rate (SAR) of superparamagnetic iron oxide nanoparticles (SPIONs) for magnetic hyperthermia. A curated dataset comprising 30 intrinsic and extrinsic features revealed strong nonlinear dependencies.
Edgar Régulo Vega‐Carrasco, Shaquib Rahman Ansari, Jiaxi Zhao, Yael del Carmen Suárez‐López, Per Larsson, Alexandra Teleki   +5 more
wiley   +1 more source

A multimodal deep learning framework for predicting sunitinib response in advanced clear cell renal cell carcinoma

VIEW, EarlyView.
In this study, we constructed a prognostic model for ccRCC patients treated with Sunitinib through a deep learning‐based multimodal approach. Our CGPR model further confirms the feasibility of integrating multimodal and provides guidance for future tumor patients to receive precision therapy.
Xi Tian, Yuchen Jiang, Shuxuan Zhu, Xiaohang Liu, Aihetaimujiang Anwaier, Shiqi Ye, Kun Chang, Yuanyuan Qu, Ya‐Jia Gu, Hailiang Zhang, Dingwei Ye, Wenhao Xu   +11 more
wiley   +1 more source