PYPE: A pipeline for phenome-wide association and Mendelian randomization in investigator-driven biobank scale analysis [0.03%]
PYPE：一种针对研究驱动型生物样本库规模分析的表型全基因组关联和孟德尔随机化的工作流程
Taykhoom Dalal,Chirag J Patel
Taykhoom Dalal
Phenome-wide association studies (PheWASs) serve as a way of documenting the relationship between genotypes and multiple phenotypes, helping to uncover unexplored genotype-phenotype associations (known as pleiotropy). Secondly, Mendelian ra...
Marino Gavidia,Hongling Zhu,Arthur N Montanari et al.
Marino Gavidia et al.
Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learni...
Consider this a WARNing [0.03%]
视为警告
Sam Freesun Friedman,Shaan Khurshid
Sam Freesun Friedman
Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wear...
The receiver operating characteristic curve accurately assesses imbalanced datasets [0.03%]
接收者操作特征曲线可准确评估不平衡数据集
Eve Richardson,Raphael Trevizani,Jason A Greenbaum et al.
Eve Richardson et al.
Many problems in biology require looking for a "needle in a haystack," corresponding to a binary classification where there are a few positives within a much larger set of negatives, which is referred to as a class imbalance. The receiver o...
Emily Wong,Ryan J Urbanowicz,Tiffani J Bright et al.
Emily Wong et al.
The authors emphasize diversity, equity, and inclusion in STEM education and artificial intelligence (AI) research, focusing on LGBTQ+ representation. They discuss the challenges faced by queer scientists, educational resources, the impleme...
Fan Zhang,Daniel Kreuter,Yichen Chen et al.
Fan Zhang et al.
For healthcare datasets, it is often impossible to combine data samples from multiple sites due to ethical, privacy, or logistical concerns. Federated learning allows for the utilization of powerful machine learning algorithms without requi...
Enhancing visibility and inclusivity of queer scientists to advance equality in academia [0.03%]
提升学术界性小众科学家的可见度和包容性以促进平等
Zhuokun Feng,Yuanyuan Fu,Youping Deng
Zhuokun Feng
For Pride Month, we would like to emphasize the critical role that diversity, equity, and inclusion (DE&I) policies play in acknowledging and valuing the contributions of queer scientists, which are essential for advancing the scientific co...
CURE: A deep learning framework pre-trained on large-scale patient data for treatment effect estimation [0.03%]
基于大规模患者数据的深度学习框架在治疗效果估计中的预训练(CURE)
Ruoqi Liu,Pin-Yu Chen,Ping Zhang
Ruoqi Liu
Treatment effect estimation (TEE) aims to identify the causal effects of treatments on important outcomes. Current machine-learning-based methods, mainly trained on labeled data for specific treatments or outcomes, can be sub-optimal with l...
Rita González-Márquez,Luca Schmidt,Benjamin M Schmidt et al.
Rita González-Márquez et al.
The number of publications in biomedicine and life sciences has grown so much that it is difficult to keep track of new scientific works and to have an overview of the evolution of the field as a whole. Here, we present a two-dimensional (2...
Upol Ehsan,Mark O Riedl
Upol Ehsan
To make explainable artificial intelligence (XAI) systems trustworthy, understanding harmful effects is important. In this paper, we address an important yet unarticulated type of negative effect in XAI. We introduce explainability pitfalls...