|Building and deploying digital pathology infrastructure for a heterogeneous user base|
Silke Smeets, Stijn Piessens, Sabrina D'Haese, Chris Groven, Wim Waelput, Ramses Forsyth, Peter In't Veld
Too often, image analysis and data/image mining projects remain stuck in
microenvironments because they are limited by vendorspecific
solutions that neither scale nor interact with material from other departments or institutions. Successful rollout of digital histopathology therefore requires more than a whole slide scanner. At Brussels Free
University (VUB), we wanted to provide a core digital pathology infrastructure that can support a range of different use cases.
|The Chemical Synthesis of Long and Highly Modified RNA using 2'-ACE Chemistry|
Xiaoqin Cheng, Kristina Larson, Letitia Kwok, David Mierzejewski, Shawn Begay, Randy Rauen, Kelly Grimsley, Kaizhang He, Michael Delaney, David Kitchen, Amanda Haas, Melissa Kelley, Anja van Brabant Smith
Dharmacon has previously developed a novel RNA synthesis chemistry making RNA synthesis as reliable, accessible and of comparable quality as routinely observed in DNA synthesis.
|Platform Agnostic Data Processing Routine for Targeted and Untargeted Metabolite Identification in Drug Discovery |
Richard Lee,1 Vitaly Lashin,2 Andrey Paramonov,2 Alexandr Sakharov,2 Alexey Aminov2
A discussion of a new informatics solution that addresses common challenges in metabolite identification and characterization--from LC/MS data processing and metabolites prediction to reporting and databasing of assembled biotransformation knowledge.
|Deep Phenotyping - Harnessing Data Richness for Unsupervised High-Content Analysis|
Huang Dong, Wang Yi, Maciej Hermanowicz, Ke Yiping, Maja Choma, Lee Kee Khoon, Frederic Bard
Recognising the key challenges, we develop an end-to-end computational framework for HCA dubbed “Deep Phenotyping” that perform unsupervised analysis to leverage on the data richness for the discovery of unknown sub-phenotypes with minimal labeling cost.
|The Case for CASE: Computer-Assisted Structure Elucidation|
Modern CASE systems such as Structure Elucidator Suite provide the necessary capability to accurately elucidate a novel chemical structure for complex molecules based on readily available NMR data sets. This allows organizations to avoid expensive, labor-intensive, and time-consuming synthetic efforts.
|A Unified Software Platform for Laboratory Informatics|
Graham A. McGibbon, Hans de Bie, David Hardy, Ryan Sasaki, Patrick Wheeler, Carol Preisig
Reported here are capabilities in automated workflows involving analytical data with chemical structures. Specifically described is automated homogenization of data from a set of instruments, including NMR structure verification, as one solution.
|Addressing False Positive Variants Arising from Pseudogenes|
Risha Govind1,2, Sam Wilkinson1,3, Nicola Whiffin1,2, Shibu John1,2, Rachel J. Buchan1,2, Elizabeth Edwards1,2, Deborah J. Morris-Rosendahl1,3, James S. Ware1,2, P.J. Barton1,2, Stuart A. Cook1,2
Clinical genetic testing has been transformed in recent years by the introduction of Next-Generation Sequencing (NGS).
|PredRet: Prediction of Retention Time by Direct Mapping between Multiple Chromatographic Systems|
Jan Stanstrup, Steffen Neumann, Urška Vrhovšek
Retention time (RT) information is under-utilized in LC-MS based metabolomics and sharing of RTs between systems is not currently possible. PredRet is a new system that allows highly accurate mapping and prediction of RTs between LC systems.
|Building a digital pathology ecosystem for education and research|
Yves Sucaet, Silke Smeets, Stijn Piessens, Sabrina D'Haese, Chris Groven, Wim Waelput, Peter In't Veld
We wanted to build a core digital pathology infrastructure to support different use cases. Various images platforms needed to be accessible through a single access point, and support different user profiles.
|Predicting Regioselectivityand Labilityof Cytochrome P450 Metabolism using Quantum Mechanical Simulations|
Tyzack, Nicholas Foster, Peter Hunt, Matthew Segall
Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism using Quantum Mechanical Simulations
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