The rapid growth of high-throughput technologies has transformed biomedical research.
With the increasing amount and complexity of data, scalability and reproducibility have …

Introduction: Artificial intelligence (AI) and machine learning (ML) are increasingly used in
many aspects of drug discovery. Larger data sizes and methods such as Deep Neural …

Data analysis often entails a multitude of heterogeneous steps, from the application of
various command line tools to the usage of scripting languages like R or Python for the …

Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a
novel artificial intelligence platform is necessary for drug discovery. Machine learning and a …

Machine Learning (ML) has extended its use in several domains to support complex
analyses of data. The medical field, in which significant quantities of data are continuously …

The direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for
direct measurement of RNA molecules without the need of conversion to complementary …

Background: In biomedical imaging research, experimental biologists generate vast
amounts of data that require advanced computational analysis. Breakthroughs in …

As Kubernetes becomes the infrastructure of the cloud-native era, the integration of workflow
systems with Kubernetes is gaining more and more popularity. However, when migrating the …

The changing landscape of genomics research and clinical practice has created a need for
computational pipelines capable of efficiently orchestrating complex analysis stages while …

Ligand-based models can be used in drug discovery to obtain an early indication of
potential off-target interactions that could be linked to adverse effects. Another application is …