KrauthammerLab
University of Zurich & University Hospital Zurich
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AI-assisted diagnosis in rheumatology
Today, in every aspect of our lives, everything we do leaves a digital footprint. Health-care institutions are also increasingly …
We are proud to be part of the UZH University Research Priority Programs (URPP) where we together with the Schwank lab investigate the …
Since the early days of computing, healthcare professionals have dreamt of using the vast storage and processing powers of computers to …
An introduction to long-read sequencing.
Helping reduce idle time in the USZ Radiology department
Comparing neural-networks versus logistic regression for predicting readmission.
Assessing the quality of online health information with AI.
Cancer on the cell level.
Novel computational method for drug-drug interaction predictions which are an important consideration for patient treatment.
Using deep learning for automatically generated medical reports describing radiological images.
The first objective of this study was to implement and assess the performance and reliability of a vision transformer (ViT)-based deep-learning model, an ‘off-the-shelf’ artificial intelligence solution, for identifying distinct signs of microangiopathy in nailfold capilloroscopy (NFC) images of patients with SSc. The second objective was to compare the ViT’s analysis performance with that of practising rheumatologists.
Here we perform an extensive analysis of adenine- and cytosine base editors on a library of 28,294 lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy.
We propose a Siamese self-attention multi-modal neural network for Drug-drug interaction (DDI) prediction that integrates multiple drug similarity measures that have been derived from a comparison of drug characteristics including drug targets, pathways and gene expression profiles.