I really like that Pierre thank you especially the reply from "Drecate":
Biological validation doesn't necessarily mean that you need to do experiments to verify the computational results. There are many computational papers published without any accompanying experimental results (of course it would be fantastic if you can, either by yourself or in collaboration with experimentalists.) The real question here is whether you provide a biological context in which to put and assess your work. No matter whether your work is experimental or quantitative, you need to demonstrate that you understand the previous work done on the biological system that you are trying to study: what has been discovered, what are the interesting questions, how do your results build on/confirm/disprove previous work etc. You need to demonstrate that your work is relevant to the biologists working in the field in the sense that it attempts to address the relevant biological questions (or asks a new question that despite its importance has never been considered) and provide unique insight that is difficult if not impossible to obtain from experiments. Biologists are not interested in theory/computation for its own sake, and the failure to connect such work to the experimental reality is one of the biggest stumbling block for people with a "hard science" background working in biology."
And what's the type of analysis that you performed?
The pipeline calculates tissue specificity of genes based on expression data from many tissues. The future goal is to use it as part of a larger project to predict suitable targets for gene editing/phenotyping
Another validation for this would be to use another (public) dataset and see if the same genes replicate.
Yes - that is the alternative idea to experimental validation being considered.