A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data [0.03%]
从ChIP-exo数据识别基序的加权两步序列比对框架
Yang Li,Yizhong Wang,Cankun Wang et al.
Yang Li et al.
In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike trad...
Valentin Liévin,Christoffer Egeberg Hother,Andreas Geert Motzfeldt et al.
Valentin Liévin et al.
Although large language models often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether closed- and open-s...
From scraped to published [0.03%]
从抓取到发布
Alejandra Alvarado,Andrew L Hufton
Alejandra Alvarado
DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images [0.03%]
DRAC 2022：用于超广角光学相干断层扫描血管成像图像糖尿病视网膜病变分析的公开基准数据集
Bo Qian,Hao Chen,Xiangning Wang et al.
Bo Qian et al.
We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we pro...
Spaco: A comprehensive tool for coloring spatial data at single-cell resolution [0.03%]
Spaco：单细胞分辨率下空间数据着色的综合工具
Zehua Jing,Qianhua Zhu,Linxuan Li et al.
Zehua Jing et al.
Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires in situ annotation and labeling of cells. Effective spatial visualization of these data demands appropriate coloriza...
TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models [0.03%]
TCGA-Reports：用于评估基于文本的AI模型的机器可读病理报告资源
Jenna Kefeli,Nicholas Tatonetti
Jenna Kefeli
In cancer research, pathology report text is a largely untapped data source. Pathology reports are routinely generated, more nuanced than structured data, and contain added insight from pathologists. However, there are no publicly available...
The DIRAC framework: Geometric structure underlies roles of diversity and accuracy in combining classifiers [0.03%]
DIRAC框架：几何结构下结合分类器中多样性与准确性作用的底层机制
Matthew J Sniatynski,John A Shepherd,Lynne R Wilkens et al.
Matthew J Sniatynski et al.
Combining classification systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Similar to improving binary classification with fusion, fusing ranking systems most commonly increases Pearson or Spe...
Yosra Magdi Mekki
Yosra Magdi Mekki
Yosra Mekki suggests that doctors should have the ability to develop their own machine-learning models. She proposes an approach with the "spotlight" on physicians, to create user-friendly frameworks that allow doctors to develop customized...
FRAMM: Fair ranking with missing modalities for clinical trial site selection [0.03%]
FRAMM：针对临床试验现场选择中模态缺失的公平排序
Brandon Theodorou,Lucas Glass,Cao Xiao et al.
Brandon Theodorou et al.
The underrepresentation of gender, racial, and ethnic minorities in clinical trials is a problem undermining the efficacy of treatments on minorities and preventing precise estimates of the effects within these subgroups. We propose FRAMM, ...
Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation [0.03%]
跨越不同的生物医学数据模式和队列的学习：创新的挑战与机遇
Suraj Rajendran,Weishen Pan,Mert R Sabuncu et al.
Suraj Rajendran et al.
In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privac...