Recent high throughput experimental methods have been used to collect large biomedical
omics datasets. Clustering of single omic datasets has proven invaluable for biological and …

Dynamical systems see widespread use in natural sciences like physics, biology, and
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Generative models are becoming a tool of choice for exploring the molecular space. These
models learn on a large training dataset and produce novel molecular structures with similar …

The scientific community is rapidly generating protein sequence information, but only a
fraction of these proteins can be experimentally characterized. While promising deep …

Abstract Concept-based explanations permit to understand the predictions of a deep neural
network (DNN) through the lens of concepts specified by users. Existing methods assume …

Modern machine learning models are complicated. Most of them rely on convoluted latent
representations of their input to issue a prediction. To achieve greater transparency than a …

Imaging flow cytometry (IFC) produces up to 12 spectrally distinct, information‐rich images of
single cells at a throughput of 5,000 cells per second. Yet often, cell populations are still …

Accurate detection and localization for angiodysplasia lesions is an important problem in
early stage diagnostics of gastrointestinal bleeding and anemia. Gold standard for …

Background Technological advances in next-generation sequencing (NGS) and
chromatographic assays [eg, liquid chromatography mass spectrometry (LC-MS)] have …