KrauthammerLab
University of Zurich & University Hospital Zurich
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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 with the Schwank lab investigate the use of AI …
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.
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.
We investigated Talimogene laherparepvec (T-VEC) and its effect on the clinical, histological, single-cell transcriptomic and immune repertoire level using repeated fine-needle aspirates (FNAs) of injected and noninjected lesions in primary cutaneous B-cell lymphoma (pCBCL).
To help patients find high quality health information online, we developed a Deep Learning system that evaluates the quality of online health articles. The system implements the DISCERN criteria, which checks for references, balanced writing, and more.
In this review, we provide a comprehensive overview of the tools and methods that are used in patient similarity analysis with longitudinal data and discuss its potential for improving clinical decision making.