Stephen Woloszynek

TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.

TL;DR: This review is inspired by some of the topics that arose as priority areas from an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in the authors' Understanding of the Microbiome, and seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today.

TL;DR: This work examines applications of deep learning to a variety of biomedical problems -- patient classification, fundamental biological processes, and treatment of patients -- to predict whether deep learning will transform these tasks or if the biomedical sphere poses unique challenges.

TL;DR: The findings suggest that bushmeat hunting and availability increased in parallel with the growth of Equatorial Guinea’s GDP and disposable income of its citizens, and demonstrate the need for strong governmental support if conservation strategies are to be successful at preventing extinctions of tropical wildlife.

TL;DR: This work uses Skip-Gram word2vec to embed k-mers, obtained from 16S rRNA amplicon surveys, and then leverages an existing sentence embedding technique to embed all sequences belonging to specific body sites or samples, demonstrating that these representations are meaningful, and hence the embedding space can be exploited as a form of feature extraction for exploratory analysis.