RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data [0.03%]
风险路径：纵向数据多步生物医学预测的可解释深度学习方法
Nina de Lacy,Michael Ramshaw,Wai Yin Lam
Nina de Lacy
Many diseases are the end outcomes of multifactorial risks that interact and increment over months or years. Time-series AI methods have attracted increasing interest given their ability to operate on native time-series data to predict dise...
GastritisMIL: An interpretable deep learning model for the comprehensive histological assessment of chronic gastritis [0.03%]
胃炎MIL：慢性胃炎全面组织学评估的可解释深度学习模型
Kun Xia,Yihuang Hu,Shuntian Cai et al.
Kun Xia et al.
The comprehensive histological assessment of chronic gastritis is imperative for guiding endoscopic follow-up strategies and surveillance of early-stage gastric cancer, yet rapid and objective assessment remains challenging in clinical work...
Noam Kolt,Michal Shur-Ofry,Reuven Cohen
Noam Kolt
The study of complex adaptive systems, pioneered in physics, biology, and the social sciences, offers important lessons for artificial intelligence (AI) governance. Contemporary AI systems and the environments in which they operate exhibit ...
Graph-sequence enhanced transformer for template-free prediction of natural product biosynthesis [0.03%]
基于图序列增强变换器的天然产物从零预测biosynthesis译为“从头合成”或“生物合成”，此处建议采用“生物合成”。因此，完整的翻译可以是：“无模板自由预测天然产物生物合成的图序列增强变换器”
Shan Cong,Meng Zhang,Yu Song et al.
Shan Cong et al.
Natural products (NPs) play a vital role in drug discovery, with many FDA-approved drugs derived from these compounds. Despite their significance, the biosynthetic pathways of NPs remain poorly characterized due to their inherent complexity...
HistoChat: Instruction-tuning multimodal vision language assistant for colorectal histopathology on limited data [0.03%]
基于有限数据的结直肠组织病理学多模态视觉语言助手的指令微调:HistoChat
Usman Afzaal,Ziyu Su,Usama Sajjad et al.
Usman Afzaal et al.
Artificial intelligence (AI) has the potential to greatly enhance diagnostic pathology, including the analysis of tissue samples to detect diseases such as colorectal cancer. This study explores how large language models (LLMs) and multimod...
Andrew L Hufton
Andrew L Hufton
Visualization of associative exploration of temporal concepts via frequent patterns [0.03%]
基于频繁模式的时态概念关联探索可视化
Tali Malenboim,Nir Grinberg,Robert Moskovitch
Tali Malenboim
Most studies on temporal pattern visualization have focused on a single pattern and its metrics and supporting instances. However, the output of a mining process is typically an enumeration tree of frequent temporal patterns. A key challeng...
Dimensionality and dynamics for next-generation artificial neural networks [0.03%]
下一代人工神经网络的维度及其动态特性研究
Ge Wang,Feng-Lei Fan
Ge Wang
The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advan...
BioLLM: A standardized framework for integrating and benchmarking single-cell foundation models [0.03%]
BioLLM：单细胞基础模型的集成和基准测试的标准框架
Ping Qiu,Qianqian Chen,Hua Qin et al.
Ping Qiu et al.
The application and evaluation of single-cell foundation models (scFMs) present significant challenges due to heterogeneous architectures and coding standards. To address this, we introduce BioLLM (biological large language model), a unifie...
Focused learning by antibody language models using preferential masking of non-templated regions [0.03%]
基于非模板区域优先屏蔽的抗体语言模型聚焦学习方法
Karenna Ng,Bryan Briney
Karenna Ng
Existing antibody language models (AbLMs) are pre-trained using a masked language modeling (MLM) objective with uniform masking probabilities. While these models excel at predicting germline residues, they often struggle with mutated and no...