From matching to facilitation: Reframing AI's role in clinical trial enrollment [0.03%]
从匹配到促进：重塑人工智能在临床试验入组中的作用
Adar Yaacov,Einat Levi,Nir Peled
Adar Yaacov
Yaacov, Levi, and Peled argue that AI-powered clinical trial matching has achieved near-expert technical accuracy yet fails to increase patient enrollment because the dominant bottleneck is systemic-involving logistics, workflow misalignmen...
Tina: A diffusion neural network for generating personalized AI models from text prompts [0.03%]
Tina：一种通过文本提示生成个性化AI模型的扩散神经网络
Zexi Li,Lingzhi Gao,Dongqi Cai et al.
Zexi Li et al.
Generative artificial intelligence (GenAI) has advanced rapidly across modalities, from text-to-text large language models to text-to-image and text-to-video diffusion models. Here, we investigate text-to-model generation: whether GenAI can...
MANNERS: A strategy for representation learning in multivariate datasets with high proportions of missing data [0.03%]
manners：处理高缺失值多变量数据集的表示学习策略
Louis Bellmann,Maximilian Nielsen,Philipp Breitfeld
Louis Bellmann
Missing data present a major challenge for deep learning, and various imputation techniques exist. However, imputation quality generally decreases as missing data rates increase. In time-series data from electronic health records, missing r...
Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics [0.03%]
基于遗传算法的脑动力学能量景观分析区域选择自动化方法研究
Koichiro Mori,Tomoyuki Hiroyasu,Satoru Hiwa
Koichiro Mori
Understanding brain dynamics is essential in cognitive neuroscience. Energy landscape analysis (ELA), which characterizes brain activity using a pairwise maximum entropy model, is a powerful tool for analyzing these dynamics but has traditi...
SHIELD: A weakly supervised graph attention neural network for decoding disease-relevant cell-cell interactions [0.03%]
基于弱监督图注意力神经网络的解码疾病相关细胞间相互作用的方法
Vivek Sehra,Benjamin Ruf,Gabriel Duval et al.
Vivek Sehra et al.
Multiplexed tissue imaging enables detailed study of cell-cell interactions in disease, yet systematic, interpretable, and supervised computational methods for inferring such interactions remain scarce. We present SHIELD (spatially enhanced...
EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework [0.03%]
基于分层感知对比学习框架的精细粒度酶功能预测方法研究
Guoxin Cao,Jian Ouyang,Xiangyi Xiong et al.
Guoxin Cao et al.
Accurate enzyme function annotation is a grand challenge due to the vast number of uncharacterized proteins and the difficulty of distinguishing subtle functions. We introduce EnzymeHunter, a deep-learning framework that achieves fine-grain...
Ben Y Reis,William G La Cava
Ben Y Reis
As AI adoption accelerates across human society, the problem of aligning AI models with human preferences remains a grand challenge. Currently, the AI alignment field is deeply divided between behavioral and representational approaches, res...
Wayne B Hayes,Kimia Yazdani,S M A Nahian et al.
Wayne B Hayes et al.
Link prediction is important across biological, social, and technological networks, but many methods either require domain-specific node attributes, do not scale to large graphs, or miss higher-order topology. We present BLANT-Predict, a to...
Multimodal spatial omics: From data acquisition to computational integration [0.03%]
多模态空间组学：从数据获取到计算整合
Esra Busra Isik,Yusuf Hakan Usta,Maryam Riazi et al.
Esra Busra Isik et al.
Recent developments in spatial omics technologies have enabled the generation of high-dimensional molecular data, including transcriptomics, proteomics, and epigenomics, within their spatial tissue context, either through co-profiling on th...
H3BERTa: A CDR-H3-specific language model for antibody repertoire analysis [0.03%]
H3BERTa：一种用于抗体库分析的CDR-H3特定语言模型
Chiara Rodella,Thomas Lemmin
Chiara Rodella
Antibodies are central to immune defense and therapeutic design, yet predicting functional sequences remains challenging. Deep learning models trained on full variable regions often struggle due to sparse experimental data, signal dilution ...