Research Area: Machine Learning
The present brief survey is to alert developers in datamining, machine learning, inference methods, and other approaches in relation to diagnostic, predictive, and risk assessment medicine about a relatively new class of bioactive messaging peptides in which there is escalating interest. They provide patterns of communication and cross-chatter about states of health and disease within and, importantly, between cells (they also appear extracellularly in biological fluids). This chatter needs to be analyzed somewhat in the manner of the decryption of the Enigma code in the Second World War. It could lead not only to improved diagnosis but to predictive diagnosis, prediction of organ failure, and preventative medicine. This involves peptide products of short reading frames that have been previously somewhat neglected as unlikely gene products, with probably many examples in nuclear DNA, but certainly several known in the mitochondrial DNA. There is a great deal of knowledge now becoming available about the latter and it is believed that that the mRNA can be translated both by standard cytosolic and mitochondrial genetic codes, resulting in different peptides, adding a further level of complexity to the applications of bioinformatics and computational biology but a higher level of detail and sophistication to preventative diagnosis. The code to crack could be sophisticated and combinatorically complex to analyze by computers. Mitochondria may have combined with proto-eucaryotic cells some 2 billion years ago, only about a 7th of the age of the universe. Cells appeared some 2 billion years before that, also with possible signaling based on similar ideas. This makes life small in space but huge in time, refinement of which centrally involves these signaling processes.
Author(s) Name: Barry Robson
Journal name: Computers in Biology and Medicine
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.compbiomed.2021.105116
Volume Information: Volume 140, January 2022, 105116