Information Gradient among Nucleotide Sequences of Essential RNAs from an Evolutionary Perspective.

We hypothesize that the first ancestral "protocell" molecular structures, i.e., the first RNAs and peptides that gradually transformed into real cells once the Earth had cooled sufficiently for organic molecules to appear, have left traces in the RNAs and the genes in present cells. We propose a circular RNA that could have been one of these ancestral structures whose vestigial pentameric subsequences would mark the evolution from this key moment when the protocells began to join with living organisms. In particular, we propose that, in present RNAs (ribosomal or messenger), which play an important role in the metabolism of current cells, we look for traces of the proposed primitive structure in the form of pentamers (or longer fragments) that belong to their nucleotide sequence. The result obtained can be summarized in the existence of a gradient of occurrence of such pentamers, with a high frequency for the most vital functions (protein synthesis, nucleic synthesis, cell respiration, etc.). This gradient is also visible between organisms, from the oldest (Archaea) to the most recent (Eukaryotes) in the evolution of species.

An Unsupervised Classifier for Whole-Genome Phylogenies, the Maxwell© Tool.

The development of phylogenetic trees based on RNA or DNA sequences generally requires a precise and limited choice of important RNAs, e.g., messenger RNAs of essential proteins or ribosomal RNAs (like 16S), but rarely complete genomes, making it possible to explain evolution and speciation. In this article, we propose revisiting a classic phylogeny of archaea from only the information on the succession of nucleotides of their entire genome. For this purpose, we use a new tool, the unsupervised classifier Maxwell, whose principle lies in the Burrows-Wheeler compression transform, and we show its efficiency in clustering whole archaeal genomes.

Maxwell®: An Unsupervised Learning Approach for 5P Medicine.

In the 5P medicine (Personalized, Preventive, Participative, Predictive and Pluri-expert), the general trend is to process data by displacing the barycenter of the information from hospital centered systems to the patient centered ones through his personal medical records. Today, the use of artificial intelligence for supporting this transition shows real limitations in its implementation in operational practice, both at the level of patient care, but also in the general daily life of the health professional, because of the medico-legal imperatives induced by the promises of the '5P medicine'. In this paper, we propose to fill this gap by introducing an original artificial intelligence platform, named Maxwell, which follows an unsupervised learning approach in line with the medico-legal imperatives of the '5P medicine'. We describe the functional platform characteristics and illustrate them by two examples of clustering in genomics and magnetic resonance imaging.