University of Zurich & University Hospital Zurich
$ health = f(data) $
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.
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable conversion of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have vast potential as genome editing tools for basic research and gene therapy, their application has been hampered by a broad variation in editing efficiencies on different genomic loci. Here we perform an extensive analysis of adenine- and cytosine base editors on thousands of lentivirally integrated genetic sequences and establish BE-DICT, an attention-based deep learning algorithm capable of predicting base editing outcomes with high accuracy. BE-DICT is a versatile tool that in principle can be trained on any novel base editor variant, facilitating the application of base editing for research and therapy.
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.
Healthcare professionals have long envisioned using the enormous processing powers of computers to discover new facts and medical knowledge locked inside electronic health records. These vast medical archives contain time-resolved information about medical visits, tests and procedures, as well as outcomes, which together form individual patient journeys. By assessing the similarities among these journeys, it is possible to uncover clusters of common disease trajectories with shared health outcomes. The assignment of patient journeys to specific clusters may in turn serve as the basis for personalized outcome prediction and treatment selection. This procedure is a non-trivial computational problem, as it requires the comparison of patient data with multi-dimensional and multi-modal features that are captured at different times and resolutions. 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.
Clinical Data Science, Translational Bioinformatics, Cancer Genetics
Bioinformatics, Cancer Genomics, Long-read sequencing, cfDNA sequencing
Machine Learning, NLP, Image Processing
NLP, Machine Learning, Information Extraction, Domain Adaptation
Artificial Intelligence, Causal Inference
Bioinformatics, Computational Pharmacology, Deep Learning
Bioinformatics, Cancer Genomics
Human Disease Monitoring, Smart Sensors, IoT
Data Science Tooling, Reproducibility, Machine Learning
Physiology and Pathophysiology, Nonlinear Dynamics, Mechanistic Modelling
Machine Learning for Health Care
Sleeping, Hunting birds and mice, Scratching trees
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 🧶🎨
Kinematics of toy mice, Hiding in cardboard boxes
Fluid dynamics, Burrowing under blankets
Belly rubs, Staying at home, Hanging around with her dog