Normalization techniques have become integral to the training of deep neural networks, serving to stabilise learning dynamics, accelerate convergence and improve generality. At their core, these ...

The rapid evolution of mass spectrometry (MS) has transformed biological research, yet the reliability of these insights depends entirely on the rigor of the applied proteomics statistics.

Methods for depth normalization have been assessed primarily with simulated data or cell-line–mixture data. There is a pressing need for benchmark data enabling a more realistic and objective ...

The challenges of deriving statistically robust conclusions from high-throughput technologies such as microarrays are well known. Yet the exploration of similar challenges for data from sequencers, ...