BioRAGent: natural language biomedical querying with retrieval-augmented multiagent systems [0.03%]
基于检索增强的多智能体系统进行自然语言生物医学查询 BioRAGent
Manlian Bi,Zhijie Bao,Dongna Xie et al.
Manlian Bi et al.
Understanding the roles of genes, phenotypes, and diseases is crucial for advancing biomedical research. However, efficient and accessible retrieval of biomedical knowledge remains a challenge due to the complexity of the relevant data. We ...
OmniDoublet: a method for doublet detection in multimodal single-cell sequencing data [0.03%]
OmniDoublet：一种用于模式单细胞测序数据双联检测的方法
Lian Liu,Jiayi Ren,Xiaoxu Zhou et al.
Lian Liu et al.
Doublets in single-cell sequencing data, caused by the simultaneous capture of two or more cells within a single reaction volume, introduce biases that compromise downstream analysis. Existing doublet detection methods primarily focus on si...
Microllm: a structured information extraction tool using large language models and named entity recognition in microbiology [0.03%]
基于大型语言模型和微生物学命名实体识别的结构化信息提取工具
Jing Lu,Fei Li,Lei Feng et al.
Jing Lu et al.
Understanding the characteristics of individual microorganisms is crucial for deciphering microbial community structures and enabling effective system manipulation. Large-scale analysis in microbial bioinformatics is hindered by the scarcit...
The promises and pitfalls of automated variant interpretation: a comprehensive review [0.03%]
自动化变异解读的承诺与陷阱：全面回顾
Mireia Costa,Alberto García S,Ana León et al.
Mireia Costa et al.
The interpretation of DNA variants enables personalized medicine through precise diagnosis and treatment selection. To address the challenges of manual interpretation, a wide range of automated tools has been created. This study evaluates t...
Spatial transcriptomic data denoising and domain identification by a community strength-augmented graph autoencoder [0.03%]
基于社区增强图自编码器的空间转录组数据去噪与领域识别
Ke Huang,Wenqian Tu,Lihua Zhang
Ke Huang
The rapid development of spatial sequencing technologies has generated large amounts of spatial transcriptomic data, which provide an opportunity to explore complex tissue structures and functional domains. However, such data often suffer f...
MODA: a graph convolutional network-based multi-omics integration framework for unraveling hub molecules and disease mechanisms [0.03%]
基于图卷积网络的多组学整合框架MODA用于解析核心分子和疾病机制
Jinhui Zhao,Yanyan Zhou,Han Bao et al.
Jinhui Zhao et al.
Advances in omics technologies provide unprecedented opportunities for systems biology, yet integrating multi-omics data remains challenging due to its complexity, heterogeneity, and the sparsity of prior knowledge networks. Here, we introd...
Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models [0.03%]
基于分子模拟和蛋白质语言模型的弱监督高效蛋白质突变效应预测方法
Teppei Deguchi,Nur Syatila Ab Ghani,Yoichi Kurumida et al.
Teppei Deguchi et al.
Machine learning-based protein mutational effect prediction is widely used in protein engineering and pathogenicity prediction, but training data scarcity remains a major challenge due to high costs of experimental measurements. A previous ...
Novel methods for temporally varying gene identification in longitudinal studies reveal bleeding and clotting pathway activation caused by blood draws [0.03%]
纵向研究中识别时变基因的新方法揭示了抽血引起凝血和出血途径的激活
Wei Chen,Yi Chai,Qi Jiang et al.
Wei Chen et al.
Recent advances in sequencing technology enable the capture of gene expression dynamics through longitudinal study designs. However, the field lacks robust analytical tools for these high-dimensional datasets. To address this, we developed ...
Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease [0.03%]
多组学数据整合的瓶颈——作为限制性驱动因素的常用数据资源——动脉粥样硬化心血管疾病的高影响力用例
Stephanie Bezzina Wettinger,Kanita Karaduzovic-Hadziabdic,Ritienne Attard et al.
Stephanie Bezzina Wettinger et al.
Despite striking successes in identifying novel biomarkers for improved patient stratification and predicting disease progression, numerous challenges remain in the effective integration and exploitation of multiomic data in biomedical appl...
Quantifying antibiotic resistome risks across environmental niches: the L-ARRAP for long-read metagenomic profiling [0.03%]
基于长读长宏基因组测序的抗生素抗性谱风险量化模型(L-ARRAP)
Yongxin Li,Yue Gao,Xiaohui Liu et al.
Yongxin Li et al.
The global dissemination of antibiotic resistance genes (ARGs) represents a critical challenge to One Health. Existing ARG risk assessment tools (e.g. MetaCompare, ARRI) are constrained by short-read sequencing data, limiting their utility ...