There has been recent disappointment expressed in the progress in the field of genomics. In my view, this results from an overly narrow view of the science of genes and biological information processing in general. It reminds me of the time when the field of “artificial intelligence” (AI) was equated with the methodology of “expert systems.” If someone referred to AI they were actually referring to expert systems and there were many articles on how limited this technique was and all of the things that it could not and would never be able to do. At the time, I expressed my view that although expert systems was a useful approach for a certain limited class of problems it did indeed have restrictions and that the field of AI was far broader. The human brain works primarily by recognizing patterns (we have about a billion pattern recognizers in the neocortex, for example) and there were at the time many emerging methods in the field of pattern recognition that were solving real world problems and that should properly be considered part of the AI field. Today, no one talks much about expert systems and there is a thriving multi-hundred billion dollar AI industry and a consensus in the AI field that nonbiological intelligence will continue to grow in sophistication, flexibility, and diversity. The same thing is happening here.
The problem starts with the word “genomics.” The word sounds like it refers to “all things having to do with genes.” But as practiced, it deals almost exclusively with single genes and their ability to predict traits or conditions, which has always been a narrow concept. The idea of sequencing genes of an individual is even narrower and typically involves individual single-nucleotide polymorphisms (SNPs) which are variations in a single nucleotide (A, T, C or G) within a gene, basically a two bit alteration.