The field I'm learning about has a lot of papers which usually go something like NGS data about a disease into differential expression analysis, enrichment analysis and protein-protein interaction networks resulting in a hub gene/s and proposed biomarker. Looking back on these papers from the past decade some portion seem to have few citations or be elaborated on any further than the original finding.
As a result when reading through the literature it has left me with the feeling of there being an enormous gap between where the current body of knowledge and computational tools are and getting to a point where we understand the biology of diseases to a similar extent that we understand something like the citric acid cycle.
- Have I missed a section of the literature and am getting a biased view or is establishing hub genes and a network the fullest extent that computers can currently contribute and the rest is up to wet-lab innovation?
- Can anyone recommend work of labs who might be attempting to improve this situation?
- Have any of you experienced a similar phenomena with your niche in this field?
- Is it just an unfortunate reality of the logistics of validation that only a smaller number of these findings get investigated in the wet-lab?
- Or have I misunderstood and mischaracterized the state of things?
Just hoping for some discussion so if you have any thoughts, even tangential, please contribute.