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Douglas B. Kell
Researcher at University of Liverpool
Publications - 657
Citations - 55792
Douglas B. Kell is an academic researcher from University of Liverpool. The author has contributed to research in topics: Systems biology & Dielectric. The author has an hindex of 111, co-authored 634 publications receiving 50335 citations. Previous affiliations of Douglas B. Kell include Max Planck Society & University of Wales.
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Substoichiometric molecular control and amplification of the initiation and nature of amyloid fibril formation: lessons from and for blood clotting. BioRXiv preprint.
TL;DR: In this paper, it was shown that anomalous fibrin fibre formation seen in such diseases actually amounts to amyloidogenesis, and that fibrins can interact with amyloids-beta (Abeta) protein that is misfolded in Alzheimer's disease.
Posted ContentDOI
Both Lipopolysaccharide And Lipoteichoic Acids Potently Induce Anomalous Fibrin Amyloid Formation: Assessment With Novel Amytracker™ Stains
Etheresia Pretorius,Martin J. Page,Lisa Hendricks,Nondumiso B. Nkosi,Sven R. Benson,Douglas B. Kell +5 more
TL;DR: The data provide further evidence for an important role of bacterial cell wall products in the various coagulopathies that are observable in chronic, inflammatory diseases.
Journal ArticleDOI
Harnessing the yeast Saccharomyces cerevisiae for the production of fungal secondary metabolites.
TL;DR: In this paper, the development of baker's yeast Saccharomyces cerevisiae to produce compounds derived from filamentous fungi and mushrooms was summarized, and the challenges and solutions in further development of yeast cell factories to more efficiently produce FSMs were discussed.
MassGenie: A Transformer-Based Deep Learning Method for Identifying Small Molecules from Their Mass Spectra
Aditya Divyakant Shrivastava,Neil Swainston,Soumitra Samanta,Ivayla Roberts,Marina Wright Muelas,Douglas B. Kell +5 more
TL;DR: For example, MassGenie as mentioned in this paper uses a transformer-based deep neural network trained on 6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion.
Journal ArticleDOI
The effect of heteroscedastic noise on the chemometric modelling of frequency domain data
TL;DR: The problems of mathematical modelling in the frequency domain in the presence of heteroscedastic noise are demonstrated using simple, illustrative, synthesised datasets and partial least squares regression.