It was possible to rank/prioritize the combinations of NFkB, WNTs, DNA repair components, ABC transporters, etc at 2nd order level. These combinations contained two factors which were recorded to be either up or down regulated in CRC cells after the administration of the ETC-1922159 drug. Please note that the in silico work extends the range of discoveries by majority voting through two way cross family analysis and later deriving the influence between the components of the combinations. I cover a range of combinations many of which have been established earlier in wet lab experiments and also show the unexplored combinations that have yet to be tested. The in-silico ranking and the two-way analysis further open a wide area of work and a range of possibilities.The latest unpublished work containing these discoveries and titled Mixed vegetable tikka masala : Two-way cross family analysis of in-silico ranked 2nd order unexplored, ETC- 1922159 affected, synergistic combinations of RAD, NFkB, WNTS, ABC transporters, etc in CRC cells has been updated on
These results were derived from earlier work in
Hope it gives a glimpse of how the search engine can assist in revealing unknown/unexplored combinations that might be important to biologists. Sincere thanks for the patient hearing.
Coming from an entirely different field I am not sure what I should learn from your post. Could you elaborate or point to the essential parts? Note also that most people don't have the time to read papers if they're not sure it is going to be interesting for them. To me at least it is unclear what was done here, and how I could apply it to my own research.
The results might be specialised, however the basic design of the search engine that was developed and description about which was posted in one of the earlier messages talks about a way to rank combinations of factors that might be working in a signaling pathway and the synergy of which might not been explored. In in silico design helps in prioritising these combinations at higher order level (>2) using sensitivity analysis method and then using a support vector machine to rank these sensitivity indices which capture the affect of combinations. The details at too heavy to discuss in a small post. However, for the draft version of the work, people can get to go through the following
(1) https://osf.io/qk83c/ (Search engine with results generated on datasets from 3 different experimental works)
(2) https://osf.io/6zk3t/ (R code for the design of the search engine)
(2) contains a flowchart which is easy to understand of what the basic idea is graphically!
Hope it helps!