Predicting sorption of organic pollutants on soils with interpretable machine learning [0.03%]
基于可解释机器学习预测有机污染物在土壤中的吸附量
Qian Wang,Jianmin Bian,Enze Ma et al.
Qian Wang et al.
The sorption of organic pollutants (OPs) on soils plays a critical role in determining the environmental fate and transport of these compounds, which has been extensively studied. However, the complex nonlinear relationships between adsorpt...
A transfer learning-enhanced deep learning framework for efficient and interpretable soil heavy metal pollution prediction under data scarcity and spatial heterogeneity [0.03%]
一种增强的深度学习框架用于数据稀缺和空间异质性条件下的土壤重金属污染高效且可解释性的预测
Bin Yang,Anqi He,Zhong Ren et al.
Bin Yang et al.
Large-scale soil heavy metal pollution risk estimation remains challenging due to data scarcity and spatial heterogeneity. Although traditional machine learning (ML) methods offer notable predictive capabilities, they often struggle with hi...
Validity, reliability, responsiveness and interpretability of the EFAS-DK PROM: an observational cohort study of Danish speaking foot and ankle patients [0.03%]
EFAS-DK患者报告结局量表的有效性、可靠性和反应度研究：一项观察性队列研究
Mick Nielsen,Jens Kurt Johansen,Anna Kathrine Pramming et al.
Mick Nielsen et al.
Background: This study is an external evaluation of the Patient Reported Outcome Measure (PROM) EFAS-DK developed by the European Foot and Ankle Society (EFAS). The evaluation included a test of the psychometric propertie...
Observational Study
Journal of patient-reported outcomes. 2025 Jun 13;9(1):67. DOI:10.1186/s41687-025-00897-y 2025
Leveraging deep learning to discover interpretable cellular spatial biomarkers for prognostic predictions based on hepatocellular carcinoma histology [0.03%]
基于肝细胞癌组织学的深度学习可解释性细胞空间生物标志物的发现用于预后预测
Huijuan Hu,Tianhua Tan,Yerong Liu et al.
Huijuan Hu et al.
The spatial structure of various cell types in the tumour microenvironment (TME) can provide valuable insights into disease progression. However, identifying the spatial organization of diverse cell types that significantly correlates with ...
Interpretable deep learning for gastric cancer detection: a fusion of AI architectures and explainability analysis [0.03%]
可解释的深度学习在胃癌检测中的应用：人工智能架构与可解释性分析的融合
Junjie Ma,Fang Yang,Rong Yang et al.
Junjie Ma et al.
Introduction: The rise in cases of Gastric Cancer has increased in recent times and demands accurate and timely detection to improve patients' well-being. The traditional cancer detection techniques face issues of explain...
Zahra Fazel,Camila P E de Souza,G Brian Golding et al.
Zahra Fazel et al.
Protein embeddings are the new main source of information about proteins, producing state-of-the-art solutions to many problems, including protein interaction prediction, a fundamental issue in proteomics. Understanding the embeddings and w...
Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability [0.03%]
基于放射组学的透明细胞肾细胞癌ISUP分级分类：一种具有SHAP增强型可解释性的机器学习方法
María Aymerich,Alejandra García-Baizán,Paolo Niccolò Franco et al.
María Aymerich et al.
Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer, and its prognosis is closely linked to the International Society of Urological Pathology (ISUP) grade. While histopathological evaluation remain...
A novel explainable AI framework for medical image classification integrating statistical, visual, and rule-based methods [0.03%]
一种新颖的医学图像分类可解释性AI框架：融合统计、视觉和规则基础方法
Naeem Ullah,Florentina Guzmán-Aroca,Francisco Martínez-Álvarez et al.
Naeem Ullah et al.
Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in high-stakes applications....
scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification [0.03%]
scATD：一种用于单细胞癌症药物抗性预测和生物标志物识别的高通量且可解释性的框架
Murong Zhou,Zeyu Luo,Yu-Hang Yin et al.
Murong Zhou et al.
Transfer learning has been widely applied to drug sensitivity prediction based on single-cell RNA sequencing, leveraging knowledge from large datasets of cancer cell lines or other sources to improve the prediction of drug responses. Howeve...
European advances in digital rheumatology: explainable insights and personalized digital health tools for psoriatic arthritis [0.03%]
数字化风湿病学在欧洲的发展：银屑病关节炎的可解释性见解和个性化数字健康工具
Leontios J Hadjileontiadis,Vasileios Charisis,Stelios Hadjidimitriou et al.
Leontios J Hadjileontiadis et al.
The shift from traditional to technology-based diagnosis and management of psoriatic arthritis (PsA) represents a significant evolution in patient care. Traditionally, PsA was diagnosed and managed through clinical evaluations, physical exa...
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