Huixin Zhan,Jason H Moore
Huixin Zhan
AI disclosure mandates have rapidly become standard in scientific publishing, a genuine success of scholarly self-governance. Yet across 10,000 bioRxiv preprints, disclosure tracks only modestly with code and data sharing. The next phase of...
Sample size calculation for training ensemble machine learning models on health data [0.03%]
基于健康数据训练集成机器学习模型的样本量计算
Nicholas Mitsakakis,Dan Liu,Thomas Walters et al.
Nicholas Mitsakakis et al.
Health research studies often suffer from small sample sizes, and training machine learning (ML) models requires large datasets. There is a dearth of literature on determining the adequate sample size for using ML models. We developed an em...
Bruce S Kristal,Melissa D McCradden,Jeyan Thiyagalingam et al.
Bruce S Kristal et al.
In this People of Data piece, our advisory board members select and discuss papers that they consider to be foundational to data science. © 2026 Publish...
Andrei Biswas,Rajagopal Venkatesaramani
Andrei Biswas
Computer science (CS) educators are increasingly concerned about students offloading thinking to generative AI (GenAI) tools. Reflecting on experiences teaching a foundational AI course, the authors argue that the community must rethink sup...
Helix 1.0: An open-source framework for reproducible and interpretable machine learning on tabular scientific data [0.03%]
Helix 1.0：基于表格科学数据的可重复和可解释机器学习的开源框架
Eduardo Aguilar-Bejarano,Daniel Lea,Karthikeyan Sivakumar et al.
Eduardo Aguilar-Bejarano et al.
Helix is an open-source, extensible, Python-based software framework to facilitate reproducible and interpretable machine learning workflows for tabular data. It addresses the growing need for transparent experimental data analytics provena...
Data-driven deformation correction in X-ray spectro-tomography with implicit neural networks [0.03%]
基于隐式神经网络的X射线谱层析中的数据驱动形变校正方法研究
Ting Wang,Zipei Yan,Hongyi Pan et al.
Ting Wang et al.
Full-field transmission X-ray microscopy with X-ray absorption near-edge structure spectroscopy enables non-destructive, high-resolution, chemically specific three-dimensional morphological and compositional analyses. However, spectro-tomog...
Michael A Lones
Michael A Lones
Modern generative AI approaches-including large language models and foundation models more generally-are increasingly part of machine learning (ML) workflows. This brings new opportunities but also new ways in which things can go wrong, inc...
A framework for reproducibly managing coupled research software and data assets based on shared transformation functions [0.03%]
一种基于共享变换函数的可重复管理关联研究软件和数据资产的方法
Patrick Kuckertz,Benjamin Fuchs,Julian Schönau et al.
Patrick Kuckertz et al.
In computational science, coupling research software and data into workflows is essential for addressing complex research questions and ensuring reproducibility. However, current metadata schemas rarely provide sufficient information about ...
Centering the marginalized: AI-driven strategies for advancing health equity in rare disease care [0.03%]
以人为本：推进罕见病诊疗健康公平的AI策略
Chaoyu Lei,Ying Zuo,Claudia Abreu Lopes et al.
Chaoyu Lei et al.
Rare diseases (RDs) affect 6%-8% of the global population but remain critically underserved. People living with an RD face misdiagnosis, limited treatment options, and inequitable access to specialized care. While artificial intelligence (A...
Evaluating large language models for evidence-based clinical question answering [0.03%]
基于证据的临床问答的大规模语言模型评估
Can Wang,Yiqun Chen
Can Wang
Large language models show potential in clinical applications, yet reliability for evidence-based medicine requires rigorous evaluation. We curated a multi-source benchmark with more than 20,000 question answering pairs from systematic revi...