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.

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.

Video self-confrontation as a therapeutic tool in schizophrenia: A randomized parallel-arm single-blind trial.

Background: Lack of insight is a barrier to treating psychosis. Preliminary studies have suggested that showing people videos of their psychotic behaviour may improve personal insight. This clinical trial aimed to assess the effect of video self-confrontation.

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'.

Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis.

MicroRNAs, small non-coding elements implied in gene regulation, are very interesting biomarkers for various diseases such as cancers. They represent potential prodigious biotechnologies for early diagnosis and gene therapies. However, experimental verification of microRNA-disease associations are time-consuming and costly, so that computational modeling is a proper solution.