University of Zurich & University Hospital Zurich
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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 transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great 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 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. 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.
Background: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. Methods: We propose a Siamese self-attention multi-modal neural network for 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. Results: Our proposed DDI prediction model provides multiple advantages: 1) It is trained end-to-end, overcoming limitations of models composed of multiple separate steps, 2) it offers model explainability via an Attention mechanism for identifying salient input features and 3) it achieves similar or better prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to state-of-the-art DDI models when tested on various benchmark datasets. Novel DDI predictions are further validated using independent data resources. Conclusions: We find that a Siamese multi-modal neural network is able to accurately predict DDIs and that an Attention mechanism, typically used in the Natural Language Processing domain, can be beneficially applied to aid in DDI model explainability.
Talimogene laherparepvec (T-VEC) is a genetically modified herpes simplex 1 virus (HSV-1), approved for cancer therapy. We investigated 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). Thirteen patients received intralesional T-VEC, eleven of which demonstrated tumor response in the injected lesions. Upon single cell sequencing of the FNAs, we identified the malignant population and separated three pCBCL subtypes. HSV-1T-VEC transcripts were detected 24h after injection in malignant and non-malignant cells of the injected lesion but were absent in the noninjected lesion. Oncolytic virotherapy resulted in a rapid eradication of malignant cells by more than one death mechanism, IFN pathway activation, early influx of NK cells, monocytes, dendritic cells, followed by an enrichment in clonal cytotoxic T-cells and decrease of regulatory T-cells in both injected and noninjected lesions.
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
NLP, Machine Learning, Information Extraction, Domain Adaptation
Finding solutions for biology motivated problems from an engineering point of view, Leveraging ICT for medicine, IoT & Smart Sensors
Time series analysis, Bayesian inference, Machine learning, Decision support systems for healthcare
Bioinformatics, Computational Pharmacology, Deep Learning
Bioinformatics, Cancer Genomics
Data Science Tooling, Reproducibility, Machine Learning
Data Science Tooling, Statistics, Machine Learning
Epidemiology, Biostatistics, Machine Learning for Health Care, Internal Medicine
Internal Medicine, Health Economics, Machine Learning in Health Care
Data Science, Human Disease Monitoring, IoT & Smart Sensors
Bioinformatics, Applied Mathematics, Genomics & Epigenetics, Algorithms
Physiology and Pathophysiology, Nonlinear Dynamics, Mechanistic Modelling
Machine Learning, NLP, Image Processing
Bioinformatics, Molecular biology
Artificial Intelligence, Causal Inference
Machine Learning for Health Care
Bioinformatics, Machine learning
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