509 citations
- Google (United States)US6 papers
- University of Tennessee Health Science CenterUS3 papers
- AstraZeneca (United States)US2 papers
- Beijing Normal UniversityCN2 papers
- Humboldt-Universität zu BerlinDE2 papers
- Medical University of GrazAT2 papers
- Stanford UniversityUS2 papers
- Bernstein Center for Computational Neuroscience BerlinDE1 paper
- Booz Allen Hamilton (United States)US1 paper
- Brown UniversityUS1 paper
- Bucknell UniversityUS1 paper
- East China Normal UniversityCN1 paper
13 papers
Self-Aligning Polar Active Matter
Paul Baconnier, Olivier Dauchot, Vincent Démery +4
Self-alignment describes the property of a polar active unit to align or anti-align its orientation towards its velocity. In contrast to mutual alignment, where the headings of mul…
A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models
Chao Yan, Yao Yan, Zhiyu Wan +5
Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. Mo…
BigBIO: A Framework for Data-Centric Biomedical Natural Language Processing
Jason Alan Fries, Leon Weber, Natasha Seelam +40
Training and evaluating language models increasingly requires the construction of meta-datasets --diverse collections of curated data with clear provenance. Natural language prompt…
Phases and homogeneous ordered states in alignment-based self-propelled particle models
Yinong Zhao, Thomas Ihle, Zhangang Han +2
We study a set of models of self-propelled particles that achieve collective motion through similar alignment-based dynamics, considering versions with and without repulsive intera…
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
Ellery Wulczyn, Kunal Nagpal, Matthew Symonds +20
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial i…
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
Ellery Wulczyn, David F. Steiner, Melissa Moran +20
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) f…