  Following the earlier formalism of the categorical representation learning
(arXiv:2103.14770) by the first two authors, we discuss the construction of the
"RG-flow based categorifier". Borrowing ideas from theory of renormalization
group flows (RG) in quantum field theory, holographic duality, and hyperbolic
geometry, and mixing them with neural ODE's, we construct a new algorithmic
natural language processing (NLP) architecture, called the RG-flow categorifier
or for short the RG categorifier, which is capable of data classification and
generation in all layers. We apply our algorithmic platform to biomedical data
sets and show its performance in the field of sequence-to-function mapping. In
particular we apply the RG categorifier to particular genomic sequences of flu
viruses and show how our technology is capable of extracting the information
from given genomic sequences, find their hidden symmetries and dominant
features, classify them and use the trained data to make stochastic prediction
of new plausible generated sequences associated with new set of viruses which
could avoid the human immune system. The content of the current article is part
of the recent US patent application submitted by first two authors (U.S. Patent
Application No.: 63/313.504).
