I'm trying to construct a pipeline for link prediction to find novel links between the entity nodes. My objective is to identify the future links between protein and target given positive and negative links. I referred to the co-author link prediction tutorial, in that they considered all pair of nodes that don’t have a relationship as negative classes. But in my case, I have two csv files, one with the positive classes (i.e, proteins binding to a target) and other with the negative classes (i.e., proteins not binding to a target). I created the network as: (P:protein_id)-[:POSITIVE]-->(T:target_id) and (P:protein_id)-[:NEGATIVE]-->(T:target_id). Is that approach correct for the link prediction?
I also want to include tested_species, scale, unit and value (will use this as a weight property), all of these being string values except the target_measure_value, would that improve the prediction if I add them as properties to the target_id node or should I add them as separate nodes. Can someone guide how to proceed with this, thanks in advance.
| protein_id |
target_id |
tested_species |
target_measure_scale |
target_measure_units |
target_measure_val |
target |
| A0JP26 |
mus musculus |
homo sapiens |
ic50 |
ug/ml |
0.01 |
POSITIVE |
| A1L190 |
hiv inhibition |
trypanosoma cruzi |
mc50 |
um |
10 |
POSITIVE |
| protein_id |
target_id |
tested_species |
target_measure_scale |
target_measure_units |
target_measure_val |
target |
| A2RUB6 |
venom activity |
homo sapiens |
ic50 |
um |
1000 |
NEGATIVE |
| A4D1B5 |
signalling activity |
rattus norvegicus |
mc50 |
ug/ml |
250 |
NEGATIVE |
