09/2020 - Subclonal reconstruction of tumors by using machine learning and population genetics
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that […]
08/2020 - Dynamic monitoring of the effects of adherence to medication on survival in heart failure patients: A joint modeling approach exploiting time-varying covariates
Adherence to medication is the process by which patients take their drugs as prescribed, and represents an issue in pharmacoepidemiological studies. Poor adherence is often associated with adverse health conditions and outcomes, especially in case of chronic diseases such as heart failure (HF). This turns out in an increased request for health care services, and […]
08/2020 - Improving Blind Spot Denoising for Microscopy
Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on […]
08/2020 - Basal Progenitor Morphology and Neocortex Evolution
The evolutionary expansion of the neocortex is thought to be largely the consequence of an increase in the proliferative capacity of a specific class of neural progenitors called basal progenitors (BPs). Here we propose that BP morphology is a key cell biological feature underlying the increase in BP proliferative capacity. During neocortical expansion, BPs show […]
08/2020 - ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy
The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement […]
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