Creation of a structured solar cell material dataset and performance prediction using large language models [0.03%]
基于大规模语言模型的太阳能电池材料数据集构建及性能预测
Tong Xie,Yuwei Wan,Yufei Zhou et al.
Tong Xie et al.
Materials scientists usually collect experimental data to summarize experiences and predict improved materials. However, a crucial issue is how to proficiently utilize unstructured data to update existing structured data, particularly in ap...
Peter S Park,Simon Goldstein,Aidan O&#x;Gara et al.
Peter S Park et al.
This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement of false beliefs in the pursuit of some outcome other than the truth. We first survey empirical exampl...
MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance [0.03%]
Mustang：基于跨样本转录相似性的多样本空间转录组数据分析
Seyednami Niyakan,Jianting Sheng,Yuliang Cao et al.
Seyednami Niyakan et al.
Spatially resolved transcriptomics has revolutionized genome-scale transcriptomic profiling by providing high-resolution characterization of transcriptional patterns. Here, we present our spatial transcriptomics analysis framework, MUSTANG ...
Fabio Crameri,Sari Hason
Fabio Crameri
Color is crucial in scientific visualization, yet it is often misused. Addressing this, we think accessible and accurate techniques, such as color-blind friendly palettes and perceptually even gradients, are vital. Accountability and basic ...
Incorporating simulated spatial context information improves the effectiveness of contrastive learning models [0.03%]
融入模拟空间上下文信息可提高对比学习模型的效果
Lizhen Zhu,James Z Wang,Wonseuk Lee et al.
Lizhen Zhu et al.
Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for se...
Unveiling value patterns via deep reinforcement learning in heterogeneous data analytics [0.03%]
基于深度强化学习的异构数据分析中的价值模式挖掘
Yanzhi Wang,Jianxiao Wang,Feng Gao et al.
Yanzhi Wang et al.
Artificial intelligence has substantially improved the efficiency of data utilization across various sectors. However, the insufficient filtering of low-quality data poses challenges to uncertainty management, threatening system stability. ...
DeepDecon accurately estimates cancer cell fractions in bulk RNA-seq data [0.03%]
DeepDecon：准确估计批量RNA序数据中癌细胞分数
Jiawei Huang,Yuxuan Du,Andres Stucky et al.
Jiawei Huang et al.
Understanding the cellular composition of a disease-related tissue is important in disease diagnosis, prognosis, and downstream treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technique have allowed the measurement of g...
Sarah M Burbach,Bryan Briney
Sarah M Burbach
Existing antibody language models are limited by their use of unpaired antibody sequence data. A recently published dataset of ∼1.6 × 106 natively paired human antibody sequences offers a unique opportunity to evaluate how antibody langua...
Using a deep generation network reveals neuroanatomical specificity in hemispheres [0.03%]
利用深度生成网络揭示大脑半球的神经解剖特异性
Gongshu Wang,Ning Jiang,Yunxiao Ma et al.
Gongshu Wang et al.
Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry....
Predicting drug response through tumor deconvolution by cancer cell lines [0.03%]
基于癌细胞系的肿瘤去卷积预测药物反应
Yu-Ching Hsu,Yu-Chiao Chiu,Tzu-Pin Lu et al.
Yu-Ching Hsu et al.
Large-scale cancer drug sensitivity data have become available for a collection of cancer cell lines, but only limited drug response data from patients are available. Bridging the gap in pharmacogenomics knowledge between in vitro and in vi...