DiDyNet: a robust framework for differential dynamic network inference from longitudinal multi-omics data [0.03%]
基于纵向多组学数据的差分动态网络推断的稳健框架
Zhe Liu,Kesong Wu,Taesung Park
Zhe Liu
Motivation: Understanding disease dynamics from longitudinal multi-omics is hindered by traditional approaches that focus on univariate trajectories and static networks while ignoring temporal evolution. We developed DiDy...
lncAPNet enables the deciphering of lncRNA-mRNA connections in patient transcriptomic data [0.03%]
lncAPNet能够解锁患者转录组数据中lncRNA与mRNA的关联关系
Vasileios Vasileiou,George I Gavriilidis,Pedro Faria Zeni et al.
Vasileios Vasileiou et al.
Motivation: Long non-coding RNAs regulate gene expression through chromatin remodeling, transcriptional control, and post-transcriptional modulation, influencing physiological cell homeostasis but also disease onset. Yet ...
NitroGene: privacy-preserving collaborative genomic analysis using AWS nitro enclaves [0.03%]
NitroGene：使用AWS Nitro Enclaves进行隐私保护的协作基因组分析
Zakariya Ali,Arif Harmanci
Zakariya Ali
Motivation: Protecting participant privacy is a major challenge in large-scale biomedical collaborations. We present NitroGene, an open-source framework for privacy-preserving collaborative genomic analysis using AWS Nitr...
visPedigree: a comprehensive R package for tidying, analyzing, and visualizing breeding pedigrees [0.03%]
visPedigree：一个用于整理、分析和可视化育种谱系的综合R包
Sheng Luan,Jie Kong,Zhenglong Xia et al.
Sheng Luan et al.
Motivation: Pedigrees support relatedness control, mate allocation, inbreeding monitoring, and diversity assessment, but large breeding populations are often curated, analyzed, and visualized using separate tools and data...
Jack Edward Peplinski,Tallulah S Andrews
Jack Edward Peplinski
Motivation: The cell cycle is a crucial component of many biological processes, including cancer, tissue repair, and inflammation. However, due to the heterogeneity of this cycle it has been difficult to assess the extent...
Performance of deep learning methods for spatial gene expression prediction from histology images: a comprehensive assessment [0.03%]
基于组织学图像的深层学习方法在空间基因表达预测中的表现评估：全面评估研究报告
Jacky Xie,Xiao Tan,Yuanhao Jiang et al.
Jacky Xie et al.
Motivation: Spatial transcriptomics enables spatially-resolved measurement of gene expression in tissues, but its widespread adoption has been limited by high cost. Predicting gene expression profiles from histology image...
Calibrating tetranucleotide-frequency distances for metagenomic binning with right-skewed distribution models [0.03%]
基于右偏分布模型校准四核苷酸频率距离以进行元基因组分类划分
Omar Hajjaji,Al-Sayed Al-Soudy,Rachid Daoud et al.
Omar Hajjaji et al.
Summary: Metagenomic binning is a pivotal step in reconstructing metagenome-assembled genomes (MAGs) from complex microbial communities, and it critically depends on reliable measures of similarity between contigs. In man...
Nuria Alina Chandra,Yan Hu,Jason D Buenrostro et al.
Nuria Alina Chandra et al.
Decoding the cis-regulatory syntax that controls gene expression is essential for improving our understanding of cell differentiation and disease. Deep learning based sequence-to-activity (S2A) models learn to identify regulatory motifs and...
Fold or flop: quality assessment of AlphaFold predictions on whole proteomes [0.03%]
折或弃牌：全长蛋白质组AlphaFold预测的质量评估
Edoardo Sarti,Frédéric Cazals
Edoardo Sarti
Motivation: Reliability of AlphaFold2 predictions is mainly assessed using the predicted Local Distance Difference Test (pLDDT). For model organisms, 30-40% of residues fall into the low-confidence pLDDT range. Moreover, ...
Pooling multimodal cancer data across unaligned embedding spaces maintains tumor of origin signal [0.03%]
跨不对齐嵌入空间汇集多模态癌症数据可保持肿瘤原发部位信号
Raphael Kirchgaessner,Kaya Keutler,Shruthilayaa Sivakumar et al.
Raphael Kirchgaessner et al.
Summary: AI-based embeddings offer the possibilities of encoding complex biological data into low-dimensional spaces, called embedding spaces, that maintain the relationships between entities. Vector pooling is the proces...