Results 21 to 30 of about 643 (115)

High Endothelial Venules in Small Cell Lung Cancer: Prognostic Subtypes and Therapeutic Implications for Immunoradiotherapy

International Journal of Cancer, EarlyView.
ABSTRACT Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine malignancy with an immunosuppressive tumor microenvironment, and current immunotherapy provides limited benefit. This highlights the need to identify microenvironmental features enabling effective immune responses.
Jiatian Fang, Zilian Wang, Hui Wang, Jianxin Xue, Kai Kang   +4 more
wiley   +1 more source

Application of MR Cytometry in Predicting Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Comparative Study

Journal of Magnetic Resonance Imaging, EarlyView.
ABSTRACT Background MR cytometry imaging is developed to characterize in vivo microstructures. Its clinical value in predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for breast cancer remains unknown. Purpose To implement MR cytometry to predict pCR for breast cancer and compare the performance of different ...
Jie Ding, Diwei Shi, Shiyun Sun, Jinlong Zheng, Wentao Yang, Caixia Fu, Dazhi Chen, Junzhong Xu, Hua Guo, Chao You, Yajia Gu   +10 more
wiley   +1 more source

Symbolic Regression‐Guided Feature Engineering for Predicting Magnetization in Cu‐Based Alloys Under Data‐Scarce Conditions

Materials Genome Engineering Advances, EarlyView.
A symbolic regression approach (SISSO) with physics‐informed feature engineering achieves high‐accuracy prediction of magnetic properties in Cu‐based alloys under data‐scarce conditions. The framework offers an interpretable and transferable strategy for accelerated alloy design.
Buyang Ma, Jiejie Li, Qiuju Zhang, Fenghua Chen, Bo Li, Ziqi Tian, Liang Chen   +6 more
wiley   +1 more source

Opening the Black Box of Nonprofit Reputation and Volunteer Attraction With Supervised Machine Learning

Nonprofit Management and Leadership, EarlyView.
ABSTRACT With the aim to explore the potential of machine learning for nonprofit research, this article contrasts traditional linear regression with four contemporary supervised machine learning approaches. Concretely, we predict (1) reputation ratings and (2) the total number of volunteers for 4021 non‐profit organizations in the U.S.
Moritz Schmid, Hannes Lampe, Jurgen Willems   +2 more
wiley   +1 more source

Machine learning‐driven advances in carbon‐based quantum dots: Opportunities accompanied by challenges

Responsive Materials, EarlyView.
Machine learning provides a unifying framework to connect structure, fluorescence properties, and applications of carbon‐based quantum dots. This review highlights how data‐driven strategies enable fluorescence regulation, reveal underlying mechanisms, and accelerate the rational design of functional carbon dots.
Liangfeng Chen, Siwei Yang, Xiangqian Cui, Ziheng Liu, Hang Wang, Yongqiang Li, Peng He, Miao Wang, Guqiao Ding   +8 more
wiley   +1 more source

Driving SDG‐Oriented Economic Development in Emerging Economies: Machine Learning Evidence From Energy, Human Capital, and Governance

Sustainable Development, EarlyView.
ABSTRACT This study examines the sustainability‐related determinants of economic development within an SDG‐oriented framework by integrating carbon productivity, energy efficiency, education, life expectancy, governance, and corruption control into a machine learning (ML) approach.
Busra Agan Celik, Serdar Celik
wiley   +1 more source

Regularized reduced rank regression for mixed predictor and response variables

British Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract In this paper, we introduce the Generalized Mixed Regularized Reduced Rank Regression model (GMR4), an extension of the GMR3 model designed to improve performance in high‐dimensional settings. GMR3 is a regression method for a mix of numeric, binary and ordinal response variables, while also allowing for mixed‐type predictors through optimal ...
Lorenza Cotugno, Mark de Rooij, Roberta Siciliano   +2 more
wiley   +1 more source

A Horse Race of Machine‐Learning Methods to Predict Banking Crises

International Finance, EarlyView.
ABSTRACT To examine if one machine‐learning model can consistently elucidate financial vulnerabilities, both over time and across levels of development, this paper applies 13 machine‐learning algorithms to evaluate comparative forecasting performance across several banking crises.
Emile du Plessis
wiley   +1 more source

Regularization Approaches for High‐Dimensional Cox Models: A Case Study in Triple‐Negative Breast Cancer

International Statistical Review, EarlyView.
Summary In high‐dimensional survival analysis, effective variable selection is crucial both for model interpretation and predictive performance. This paper investigates Cox regression with lasso and adaptive lasso penalties in genomic datasets where covariates far outnumber observations.
Pilar González‐Barquero, Rosa E. Lillo, Álvaro Méndez‐Civieta   +2 more
wiley   +1 more source

Radiomics Analysis for Predicting Medication‐Related Osteonecrosis of the Jaw Using Computed Tomography

Oral Diseases, EarlyView.
ABSTRACT Objectives To determine whether pretreatment radiomics can predict medication‐related osteonecrosis of the jaw (MRONJ). Methods Patients with mandibular MRONJ at Hiroshima University Hospital (2009–2024) who underwent MDCT before initiating bone resorption inhibitors were retrospectively identified. Records from 2009 to 2020 were also reviewed
Masaru Konishi
wiley   +1 more source