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 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 …
Comparing neural-networks versus logistic regression for predicting readmission.
Novel computational method for drug-drug interaction predictions which are an important consideration for patient treatment.
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.
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.
Clinical Data Science, Translational Bioinformatics, Cancer Genetics
NLP, Machine Learning, Information Extraction, Domain Adaptation
Machine Learning, Gaussian Processes, Time Series
Bioinformatics, Cancer Genomics, Long-read sequencing, cfDNA sequencing
Natural Language Processing, Clinical Machine Learning, Spoken Dialogue Processing
Machine Learning, Clinical Decision Support Systems
Bioinformatics, Computational Pharmacology, Deep Learning
Bioinformatics, Cancer Genomics
Electrical heart disease, Biomedical signal processing, Machine Learning, Time Series
Machine Learning, Longitudinal Data Analysis, Machine Perception
Data Science Tooling, Statistics, Machine Learning
Bioinformatics, Applied Mathematics, Genomics & Epigenetics, Algorithms
Epidemiology, Biostatistics, Machine Learning for Health Care, Internal Medicine
Machine Learning, Bioinformatics, Time Series Analysis
Computer Vision, Natural Language Processing, Multi-task Learning
Bioinformatics, Cancer Genomics, Liquid biopsy
Contrastive Learning, Time-series analysis, Natural Language Processing
Physiology and Pathophysiology, Nonlinear Dynamics, Mechanistic Modelling
Sleeping, Hunting birds and mice, Scratching trees
Being the fastest dog in the neighborhood, Maintaining owner’s physical and mental health, Living the best day of her life every day
Jumping all over the place 🦘, Knocking over Chess pieces ♟️♕, remote control 📺 and everything in front of him, Playing soccer ⚽ and chasing butterflies 🦋, Sleeping 😴 in front of laptops 💻, Participating in origami 📄🏮 and crafting activities 🧶🎨
Belly rubs, Staying at home, Hanging around with her dog