<p>I am not sure if I understood correctly how you classify  the drugs. But what you attempt to do is similar to what they do <a href="http://cancerres.aacrjournals.org/content/77/2/459.full" rel="nofollow noreferrer">here</a> but in that article they use the drugs targeted to a molecule/pathway to find combination of drugs that are better for the disease (not an alternative drug), see the first image:</p>
<p><a href="https://i.sstatic.net/WWNuh.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WWNuh.jpg" alt="Pathway targeted by two drugs" /></a></p>
<p>In that article they mention the pathway cross-talk inhibition (PCI), you might want to calculate it for your pathways to assess the relative reduction of the pathway efficiency.</p>

CC BY-SA 3.0
2017-12-08T09:23:41.637Z
2017-12-08T09:23:41.637Z
2020-06-18T08:30:41.743Z
llrs
<p>Currently I'm building a big matrix (using microarray, mass spectrometry, RNAseq data) that consist by pathways (rows) and treatment/drugs (columns).</p>

<p>The values of that matrix are scores that describe the effect/relation of that specific treatment/drug on each pathway.</p>

<p>Now, some of the drugs is known to target specific disease (target disease) and thus is expected to have a high score with that disease's pathways. <strong>(A)</strong></p>

<p>The rest of the drugs is known to target other diseases.<strong>(B)</strong></p>

<p>My thought now is that, if I label the known drugs as "1" (or whatever), could I use a prediction model to find out new relationships (links) between drugs from the <strong>B</strong> class and the significant disease pathway from <strong>A</strong> ?</p>

<p>The whole thing, we can represent it as a bipartite network shown below.</p>

<p><a href="https://i.sstatic.net/pyNwa.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/pyNwa.png" alt="enter image description here"></a></p>

<p>For example, here the denoted as green, drugs are the known drugs belong to the class <strong>A</strong> that mentioned before and they are connected highly with some pathways of the target disease.</p>

<p>From the other hand blue and red ones are drugs taken from other cases (class <strong>B</strong>) that are used to target other diseases, but as my plot shows, blue seems to has an "affinity" to some of the pathways of the target disease and thus should be an alternative candidate for that disease. The red from the other side, doesn't have any "affinity" to any significant pathway and thus we can not consider it as possible candidate.</p>

<p>To find a solution to that kind of problem, I looked for algorithms and methods developed for social networks (e.g recommendation etc.) and mostly I read about random walks with return.</p>

<p>But before to proceed, I would like to know if someone else tried such an approach here and having more experience. </p>

<networks><pathway><machine-learning><modelling>
Link Prediction in Bipartite Networks between biological pathways and drugs
# Link Prediction in Bipartite Networks between biological pathways and drugs

(Asked by: username_0 on 2017-12-07)

Currently I'm building a big matrix (using microarray, mass spectrometry, RNAseq data) that consist by pathways (rows) and treatment/drugs (columns).

The values of that matrix are scores that describe the effect/relation of that specific treatment/drug on each pathway.

Now, some of the drugs is known to target specific disease (target disease) and thus is expected to have a high score with that disease's pathways. **(A)**

The rest of the drugs is known to target other diseases.**(B)**

My thought now is that, if I label the known drugs as "1" (or whatever), could I use a prediction model to find out new relationships (links) between drugs from the **B** class and the significant disease pathway from **A** ?

The whole thing, we can represent it as a bipartite network shown below.

[![enter image description here](https://i.sstatic.net/pyNwa.png)](https://i.sstatic.net/pyNwa.png)

For example, here the denoted as green, drugs are the known drugs belong to the class **A** that mentioned before and they are connected highly with some pathways of the target disease.

From the other hand blue and red ones are drugs taken from other cases (class **B**) that are used to target other diseases, but as my plot shows, blue seems to has an "affinity" to some of the pathways of the target disease and thus should be an alternative candidate for that disease. The red from the other side, doesn't have any "affinity" to any significant pathway and thus we can not consider it as possible candidate.

To find a solution to that kind of problem, I looked for algorithms and methods developed for social networks (e.g recommendation etc.) and mostly I read about random walks with return.

But before to proceed, I would like to know if someone else tried such an approach here and having more experience.

**Tags:** networks, pathway, machine-learning, modelling

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## 1 answers

### Answer by username_1 on 2017-12-08. Score: 2

I am not sure if I understood correctly how you classify the drugs. But what you attempt to do is similar to what they do [here](http://cancerres.aacrjournals.org/content/77/2/459.full) but in that article they use the drugs targeted to a molecule/pathway to find combination of drugs that are better for the disease (not an alternative drug), see the first image:

[![Pathway targeted by two drugs](https://i.sstatic.net/WWNuh.jpg)](https://i.sstatic.net/WWNuh.jpg)

In that article they mention the pathway cross-talk inhibition (PCI), you might want to calculate it for your pathways to assess the relative reduction of the pathway efficiency.


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