Using Large Datasets to Understand Nanotechnology. Issue 43 (20th August 2019)

• Record Type: Journal Article
• Authors: Paunovska, Kalina; Loughrey, David; Sago, Cory D.; Langer, Robert; Dahlman, James E.
• Is Part Of: Advanced materials. Volume 31:Issue 43(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-20
• Subjects: big data -- drug delivery -- nanotechnology -- next‐generation sequencing; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.201902798
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: Advances in sequencing technologies have made studying biological processes with genomics, transcriptomics, and proteomics commonplace. As a result, this suite of increasingly integrated techniques is well positioned to study drug delivery, a process that is influenced by many biomolecules working in concert. Omics‐based approaches can be used to study the vast nanomaterial chemical space as well as the biological factors that affect the safety, toxicity, and efficacy of nanotechnologies. The generation and analysis of large datasets, methods to interpret them, and dataset applications to nanomaterials to date, are demonstrated here. Finally, new approaches for how sequencing‐generated datasets can answer fundamental questions in nanotechnology based drug delivery are proposed. Abstract : Studying biological processes with genomics, transcriptomics, and proteomics has become commonplace. Omics‐based approaches can help study the vast nanomaterial chemical space as well as biological factors that affect the safety, toxicity, and efficacy of nanotechnologies. The generation and analysis of large datasets and their application to answer fundamental questions in nanotechnology‐based drug delivery are reviewed.