Trust, Reproducibility, and Progress: The Roles of Independent Blind Prediction and Assessment and Benchmarking in Computational Biology [0.03%]
信任、可重复性和进步：独立盲测及其他评估和基准方法在计算生物学中的作用
Gaia Andreoletti,Serghei Mangul,Predrag Radivojac et al.
Gaia Andreoletti et al.
When evaluations aren't trustworthy, entire research programs can chase mirages. Objective benchmarks and independent assessment have repeatedly catalyzed progress across computational biology, from protein structure prediction to variant i...
The Evolving Cyberinfrastructure at the National Institutes of Health to Support Data and AI in Biomedical Research [0.03%]
支持生物医学研究数据和人工智能的国立卫生研究院不断发展的网络基础设施
Ojas A Ramwala,Nick Weber,Sean D Mooney
Ojas A Ramwala
Technological advancements have made biomedicine rich in data. With the generation of enormous volumes of biomedical and clinical data, it has become imperative to support biomedical computing investigators to utilize this wealth of biologi...
Applications of AI & ML in Biomanufacturing of Cell and Gene Therapies [0.03%]
AI与ML在细胞和基因疗法生物制造中的应用
Eric Neumann,Karen Weisinger,Tom Londo
Eric Neumann
This workshop highlights how AI/ML technologies are beginning to be applied to biomanufacturing and bioengineering of cell and gene therapies (CGT). AI/ML have demonstrated their utility in biocomputing and biomedical research applications,...
AI for Health: Leveraging Artificial Intelligence to Revolutionize Healthcare [0.03%]
医疗中的人工智能：利用人工智能重塑医疗行业
Ruowang Li,Brian D Davison,Tiffani Bright et al.
Ruowang Li et al.
The following sections are included: Introduction to the Workshop; Session Scope, Organizing Team. © 2025 The Authors Open Access chapter published by W...
Workshop Introduction: Advances of AI Methods in Single Cell Spatial Omics [0.03%]
工作坊介绍：单细胞空间组学的AI方法进展
Lana Garmire,Xiuwei Zhang,Joshua Levy
Lana Garmire
The workshop "Advances of AI Methods in Single Cell Spatial Omics" will highlight recent developments in applying AI and machine learning to spatial transcriptomics, proteomics, and metabolomics. Featuring invited speakers and contributed t...
DRIVE-KG: Enhancing variant-phenotype association discovery in understudied complex diseases using heterogeneous knowledge graphs [0.03%]
基于异构知识图谱增强罕见病变异表型关联发现
Ananya Rajagopalan,Tram Anh Nguyen,Lindsay A Guare et al.
Ananya Rajagopalan et al.
Multi-omics data are instrumental in obtaining a comprehensive picture of complex biological systems. This is particularly useful for women's health conditions such as endometriosis, which has been historically understudied despite having a...
A random-walk-based learning framework to uncover novel gene candidates for Alzheimer's disease therapy [0.03%]
一种基于随机游走的深度学习框架用于揭示新的阿兹海默症治疗基因候选者
Alena Orlenko,Binglan Li,Neda Khanjani et al.
Alena Orlenko et al.
Identifying repurposable therapeutic targets for Alzheimer's disease (AD) remains challenging due to various clinical and biological factors. This study aimed to identify candidate genes for AD therapy. We hypothesize that gene and disease-...
Discovery of Disease Relationships via Transcriptomic Signature Analysis Powered by Agentic AI [0.03%]
基于智能体的人工智能的转录组标志分析发现疾病关系
Ke Chen,Haohan Wang
Ke Chen
Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for discovering disease relationships by analyzing over 1,300 di...
Network optimal retrieval of sparse perturbations for steady-state control [0.03%]
稳态控制的稀疏扰动网络优化检索方法
Krithika Krishnan,Tiange Shi,Satyam Kumar et al.
Krithika Krishnan et al.
Prioritizing targeted perturbation experiments remains a central challenge in systems biology, where experimental constraints limit network manipulation. We introduce NORSP (Network Optimal Retrieval of Sparse Perturbations). This novel com...
REPEL - Random Embedding Perturbation for Enhanced Learning of Protein Function [0.03%]
基于随机嵌入扰动的蛋白质功能增强学习方法(REPEL)
Di Zhou,Lenore J Cowen,Kaiyi Wu et al.
Di Zhou et al.
Protein function prediction from multiplex protein-protein association networks is a crucial approach to extending functional annotation. Current methods use embeddings of the heterogeneous network data that aim to place related proteins ne...