OBJECTIVE: Self-anamnesis is a procedure in which a patient answers questions about the personal medical history without interacting directly with a doctor or medical assistant. If collected digitally, the anamnesis data can be shared among the healt...
The exploration of hybrid quantum-classical algorithms and programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards classical intractability, validation and benchmarking are critical to understanding the util...
Computer methods and programs in biomedicine
30777618
BACKGROUND AND OBJECTIVE: The quality of data is crucial for clinical registry studies as it impacts credibility. In the regular practice of most such studies, a vulnerability arises from researchers recording data on paper-based case report forms (C...
To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for ...
Computer methods and programs in biomedicine
31104710
BACKGROUND AND OBJECTIVE: In the last decade, RNA-sequencing technology has become method-of-choice and prefered to microarray technology for gene expression based classification and differential expression analysis since it produces less noisy data....
BACKGROUND AND OBJECTIVE: Deep learning techniques have been successfully applied to tackle several image classification problems in bioimaging. However, the models created from deep learning frameworks cannot be easily accessed from bioimaging tools...
BACKGROUND: Ontologies are invaluable in the life sciences, but building and maintaining ontologies often requires a challenging number of distinct tasks such as running automated reasoners and quality control checks, extracting dependencies and appl...
Proceedings of the National Academy of Sciences of the United States of America
31527280
The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learn...