Thanks Jason, Sebastian and Maciek!
I believe from all the suggestions, the most feasible solutions is to look
experimental assays which overlap by at least two compounds, and then
adjust the binding affinities of one of them by looking in their difference
in both assays. Sebastian mentioned the s
Hi Thomas and others,
It also really depend on how many data points you have on each compound. If
you had more than a few then there are few options. If you get two very
distinct activities for one ligand. I'd discard such samples as ambiguous
or decide on one of the assays/experiments (the one wi
Another approach would be to pose this as a "ranking" problem to predict
relative affinities rather than absolute affinities. E.g., if you have data
from one (or more) molecules that has/have been tested under 2 or more
experimental conditions, you can rank the other molecules accordingly or
no
Thomas,
This is sort of related to the problem I did my M.S. thesis on years ago:
cross-platform normalization of gene expression data. If you google that
term you'll find some papers. The situation is somewhat different, though,
because with microarrays or RNA-seq you get thousands of data poin
Greetings,
I am working on a problem that involves predicting the binding affinity of
small molecules on a receptor structure (is regression problem, not
classification). I have multiple small datasets of molecules with measured
binding affinities on a receptor, but each dataset was measured in
di
