Corrigendum to "Drug repositioning with metapath guidance and adaptive negative sampling enhancement" [J. Biomed. Inform. 171 (2025) 104916] [0.03%]
“基于元路径指导和自适应负样本增强的药物再定位”一文的勘误通知[J. Biomed. Inform. 171 (2025) 104916]
Yaozheng Zhou,Xingyu Shi,Lingfeng Wang et al.
Yaozheng Zhou et al.
Published Erratum
Journal of biomedical informatics. 2025 Nov 18:104953. DOI:10.1016/j.jbi.2025.104953 2025
Predicting drug-target interactions based on multivariate information fusion and graph contrast learning [0.03%]
基于多维信息融合和图对比学习的药物靶点相互作用预测
Siying Yang,Ping-An He,Pan Zeng et al.
Siying Yang et al.
Drug-target interaction (DTI) prediction is of great significant in stimulating innovation and research in the medical field. In recent years, traditional experimental methods for predicting DTIs have proven to be time-consuming and costly....
Multi-objective optimization formulation for Alzheimer's disease trial patient selection [0.03%]
阿尔茨海默病临床试验受试者选择的多目标优化方法
Alireza Moayedikia,Sara Fin,Uffe Kock Wiil
Alireza Moayedikia
Objective: Clinical trial recruitment faces critical challenges with screen failure rates exceeding 80% in Alzheimer's disease (AD) trials. Traditional patient selection relies on expert consensus without systematic evalu...
Turning dialogues into event data: Lessons from GPT-based recognition of nursing actions [0.03%]
从对话生成事件数据：基于GPT的护理活动识别方法及启示
Iris Beerepoot,Sjaak Brinkkemper,Elke Huntink et al.
Iris Beerepoot et al.
Objective: To assess the feasibility of using a large language model (LLM) to generate structured event logs from conversational data in home-based nursing care, with the goal of reducing the documentation burden and enab...
Unveiling novel bladder cancer associations from multicentred primary and secondary care electronic health records by machine learning: a case-control study [0.03%]
基于机器学习的电子健康记录的多中心膀胱癌病例对照研究
Xu Wang,Andrea Preston,Jonathan Aning et al.
Xu Wang et al.
Objective: The rising incidence and mortality in bladder cancer (BC) underscore the importance of identifying associated features. Current reliance on haematuria as a primary indicator for BC proves inadequate. While mini...
SemNovel - A new approach to detecting semantic novelty of biomedical publications using embeddings of large language models [0.03%]
SemNovel——利用大型语言模型嵌入检测生物医学文献语义新颖性的新方法
Xueqing Peng,Yutong Xie,Huan He et al.
Xueqing Peng et al.
Objective: The rapid growth of scientific literature necessitates robust methods to identify novel contributions. However, there is currently no widely-recognized measurement of novelty in biomedical research. Existing ap...
TopicForest: embedding-driven hierarchical clustering and labeling for biomedical literature [0.03%]
基于嵌入的生物医学文献层次聚类和标注方法
Chia-Hsuan Chang,Brian Ondov,Bin Choi et al.
Chia-Hsuan Chang et al.
Objective: The rapid expansion of biomedical literature necessitates effective approaches for organizing and interpreting complex research topics. Existing embedding-based topic modeling techniques provide flat clusters a...
A method for characterizing disease progression from acute kidney injury to chronic kidney disease [0.03%]
从急性肾损伤到慢性肾脏疾病的疾病进展的表征方法
Yilu Fang,Jordan G Nestor,Casey N Ta et al.
Yilu Fang et al.
Objective: Patients with acute kidney injury (AKI) are at high risk of developing chronic kidney disease (CKD), but identifying those at greatest risk remains challenging. We used electronic health record (EHR) data to dy...
Pre-coding skin cancer from free-text pathology reports using noise-robust neural networks [0.03%]
基于抗噪稳健神经网络的皮肤癌编码研究
Tapio Niemi,Gautier Defossez,Simon Germann et al.
Tapio Niemi et al.
Objective: Population-based cancer registries receive numerous free-text pathology reports from which cancer cases are manually coded according to international standards. Skin cancer is the most frequent cancer in Caucas...
A joint learning framework for analyzing data from national geriatric centralized networks: A new toolbox deciphering real-world complexity [0.03%]
一个联合学习框架，用于分析来自国家老年集中网络的数据：一个新的工具箱解码现实世界的复杂性
Biyi Shen,Yilin Zhang,Thomas G Travison et al.
Biyi Shen et al.
Objective: We propose JLNet, along with a companion R software package, as a systematic joint learning framework for analyzing data from national geriatric centralized networks, such as Medicare Claims. JLNet addresses ke...