Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Previous surveys of the literature have shown that reports of statistical analyses often lack
important information, causing lack of transparency and failure of reproducibility. Editors and …

We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …

The rapid growth of high-throughput technologies has transformed biomedical research.
With the increasing amount and complexity of data, scalability and reproducibility have …

Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …

An increasing number of researchers support reproducibility by including pointers to and
descriptions of datasets, software and methods in their publications. However, scientific …

This article presents a study on the quality and execution of research code from publicly-
available replication datasets at the Harvard Dataverse repository. Research code is …

RegulonDB is a database that contains the most comprehensive corpus of knowledge of the
regulation of transcription initiation of Escherichia coli K-12, including data from both …

Applying computational statistics or machine learning methods to data is a key component of
many scientific studies, in any field, but alone might not be sufficient to generate robust and …

As researchers are increasingly able to collect data on a large scale from multiple clinical
and omics modalities, multi-omics integration is becoming a critical component of …