Search Results
Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry
2015, Oetjen, Janina, Veselkov, Kirill, Watrous, Jeramie, McKenzie, James S., Becker, Michael, Hauberg-Lotte, Lena, Kobarg, Jan Hendrik, Strittmatter, Nicole, Mróz, Anna K., Hoffmann, Franziska, Trede, Dennis, Palmer, Andrew, Schiffler, Stefan, Steinhorst, Klaus, Aichler, Michaela, Goldin, Robert, Guntinas-Lichius, Orlando, von Eggeling, Ferdinand, Thiele, Herbert, Maedler, Kathrin, Walch, Axel, Maass, Peter, Dorrestein, Pieter C., Takats, Zoltan, Alexandrov, Theodore
Background: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatial organization of biological processes, and has growing potential to be introduced into routine use in both biology and medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3D imaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack of publicly available benchmark datasets needed to evaluate and compare algorithms. Findings: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired, supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights, an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two types of 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected from murine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies cultured in Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectal adenocarcinoma. Conclusions: With the aim to stimulate computational research in the field of computational 3D imaging MS, selected high-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.
Deep Learning and Inverse Problems
2019, Arridge, Simon, de Hoop, Maarten, Maass, Peter, Öktem, Ozan, Schönlieb, Carola, Unser, Michael
Big data and deep learning are modern buzz words which presently infiltrate all fields of science and technology. These new concepts are impressive in terms of the stunning results they achieve for a large variety of applications. However, the theoretical justification for their success is still very limited. In this snapshot, we highlight some of the very recent mathematical results that are the beginnings of a solid theoretical foundation for the subject.