Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and...
Acute diarrheal disease (ADD) caused by rotavirus (RV) contributes significantly to morbidity and mortality in children under five years of age. Currently, there are no specific drugs for the treatment of RV infections. Previously, we reported the an...
Accurately counting and localising cellular events from movies is an important bottleneck of high-content tissue/embryo live imaging. Here, we propose a new methodology based on deep learning that allows automatic detection of cellular events and the...
The present work develops ANAKIN: an. ANAKIN is trained and tested over 513 cell survival experiments with different types of radiation contained in the publicly available PIDE database. We show how ANAKIN accurately predicts several relevant biologi...
Axonal degeneration (AxD) is a pathological hallmark of many neurodegenerative diseases. Deciphering the morphological patterns of AxD will help to understand the underlying mechanisms and develop effective therapies. Here, we evaluated the progressi...
Adverse outcome pathways (AOPs) and their networks are important tools for the development of mechanistically based non-animal testing approaches, such as in vitro and/or in silico assays, to assess toxicity induced by chemicals. In the present study...
Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell dea...
Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid ...
We introduce machine learning (ML) to perform classification and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate...
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