4 ms·
In theory, yes. In reality, we don't have enough data for that to work. A model that takes the whole genome as input is excessively expressive and would overfit
by allenz 9y ago
In theory, yes. In reality, we don't have enough data for that to work. A model that takes the whole genome as input is excessively expressive and would overfit, finding spurious correlations everywhere. In the near term, we still need to preprocess the genome to extract lower-dimensional features of interest.
- posterboy 9y agoyou could use montecarlo to search for an effective compression of the input data. That's Singular Value Decomposition if I'm not mistaken. Dimensional Reduction is a hot topic in coding theory. Optimaly, understanding of the biologic process involved would certainly help here. DNA is thought to be higly compressed and self modifying, so a smaller encoding is unlikely. Therefore, seperation of the DNA sequences involved in the effect under scrutiny might fail on the possibly highly random inputs without good techniques and heuristics. Effectively, pre-processing could involve recompilation and all the techniques used in software analysis, only then live debugging is to be taken literal. Reduction to spectra can be used to achieve sparseness, that cover the error margin with probalistic precission including invariants and the mentioned external factors, maybe also data aquisition errors from cost effective (read cheap:) methods.