17 citations · 17 across the 2 of their papers we have counts for
5 papers
A field guide to cultivating computational biology
Anne E Carpenter, Casey S Greene, Piero Carnici +11
Biomedical research centers can empower basic discovery and novel therapeutic strategies by leveraging their large-scale datasets from experiments and patients. This data, together…
The importance of transparency and reproducibility in artificial intelligence research
Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny +17
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines…
Recommendations to enhance rigor and reproducibility in biomedical research
Jaqueline J. Brito, Jun Li, Jason H. Moore +4
Computational methods have reshaped the landscape of modern biology. While the biomedical community is increasingly dependent on computational tools, the mechanisms ensuring open d…
Incorporating biological structure into machine learning models in biomedicine
Jake Crawford, Casey S. Greene
In biomedical applications of machine learning, relevant information often has a rich structure that is not easily encoded as real-valued predictors. Examples of such data include…
Evaluating deep variational autoencoders trained on pan-cancer gene expression
Gregory P. Way, Casey S. Greene
Cancer is a heterogeneous disease with diverse molecular etiologies and outcomes. The Cancer Genome Atlas (TCGA) has released a large compendium of over 10,000 tumors with RNA-seq…