Feb 16, 2018

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  • Biomedical Analogies


    Can you solve these biomedical analogies?

    More fun with word2vec! See my previous post on getting started with word2vec to run these examples yourself.

    Can you solve these biomedical analogies? Or can a machine learning / artificial intelligence algorithm solve them better than you? Put yourself to the test! I put myself to the test and this is what I got.

    1. Receptor ligand relationships

    Recall that IGF1R is the receptor for IGF1.

    IGF1 is to IGF1R as HGF is to ____.

    In my naïveté, I would guess HGFR. Let’s see what word2vec says:

     > model %>% closest_to( ~ "IGF1R" - "IGF1" + "HGF")
          word similarity to "IGF1R" - "IGF1" + "HGF"
     1     HGF                              0.8062725
     2   c-Met                              0.7701611
     3   IGF1R                              0.7375224
     4   c-met                              0.7345159
     5   c-MET                              0.7340344
    

    Omitting our input terms, it looks like the answer word2vec proposes is ‘c-Met’.

    According to Wikipedia: “c-Met, also called tyrosine-protein kinase Met or hepatocyte growth factor receptor (HGFR)” so it looks like we’re both right ;)

    Let’s try another.

    IGF1 is to IGF1R as SDF1 is to ____.

    Uhhh, SDF1R? Let’s see what word2vec says:

          word similarity to "IGF1R" - "IGF1" + "SDF1"
     1    SDF1                               0.8387451
     2   CXCR4                               0.7308524
     3   CXCR7                               0.6997249
     4   ITGB1                               0.6853041
     5  CXCR-4                               0.6771502
    
    

    Again, omitting our input terms, it looks like the answer word2vec proposes is ‘CXCR4’.

    According to Wikipedia: “CXCR4’s ligand SDF-1”

    So looks like I was way off.

    2. Disease

    Recall that insulin is a hormone that is released to signal absorption of glucose to regular blood sugar. In diabetic patients, insulin is not produced.

    Diabetes is to insulin as obesity is to ____.

    Ok. I’m not a dietician. I have no idea. word2vec says:

    > model %>% closest_to( ~ "insulin" - "diabetes" + "obesity")
                     word similarity to "insulin" - "diabetes" + "obesity"
     1            insulin                                        0.7239623
     2             leptin                                        0.6307069
     3            Insulin                                        0.6017395
     4          adiposity                                        0.5957565
     5            ghrelin                                        0.5901437
    

    According to Wikipedia, leptin is a hormone that regulates energy balance by inhibiting hunger. Similarly, ghrelin is a hormone that promotes hunger.

    Given these relationships, can you figure out:

    Leptin is to ghrelin as insulin is to ____.

    I know this one! Must be glucagon?

     > model %>% closest_to( ~ "ghrelin" - "leptin" + "insulin")
             word similarity to "ghrelin" - "leptin" + "insulin"
     1    insulin                                      0.8649428
     2   glucagon                                      0.7823924
     3    Insulin                                      0.7570941
     4    ghrelin                                      0.7506850
     5      GLP-1                                      0.7482180
    
    

    Yay glucagon! I didn’t know about GLP-1 (Glucagon-like peptide-1), but it also decreases blood sugar levels in a glucose-dependent manner by enhancing the secretion of insulin. So it seems like an appropriate answer too. Cool! Learned something new.

    3. Cancer cell types

    Astrocytoma is a cancer of the brain originating from a type of cell called astrocytes.

    *Astrocyte is to astrocytoma as b-cells are to ____

    > model %>% closest_to( ~ 'astrocytoma' - "astrocyte" + "B-cell")
                word similarity to "astrocytoma" - "astrocyte" + "B-cell"
     1      lymphoma                                            0.7575159
     2     lymphomas                                            0.7466955
     3        B-cell                                            0.7399720
     4  Burkitt-type                                            0.7304544
     5           NHL                                            0.7163928
    

    All are lymphomas!

    4. Drug discovery

    Lupus is an autoimmune disease that causes inflammation. Its symptoms are often treated with nonsteroidal anti-inflammatory drugs (NSAID).

    Lupus is to NSAID as depression is to ____.

    I’m going to guess SSRIs.

    > model %>% closest_to( ~ "NSAID" - "lupus" + "depression")
                   word similarity to "NSAID" - "lupus" + "depression"
     1    psychotropics                                      0.4961523
     2    tranquilizers                                      0.4881507
     3          tNSAIDs                                      0.4878482
     4      nonnarcotic                                      0.4767651
     5  benzodiazepines                                      0.4746059
    

    Psychotropics are drugs affecting mental state, of which SSRI are one type. So actually psychotropics is the more appropriate answer here since it is much more general just like NSAIDs. I didn’t even know about benzodiazepines but it is indeed a class of drugs use to treat depression and anxiety.

    Ok. ADHD is commonly treated with dextroamphetamines like Adderall.

    Dextroamphetamine is to ADHD as SSRI is to ____.

    > model %>% closest_to( ~ "ADHD" - "dextroamphetamine" + "SSRI", n=5)
        word similarity to "ADHD" - "dextroamphetamine" + "SSRI"
     1  ADHD                                           0.7410688
     2  SSRI                                           0.6886885
     3   OCD                                           0.6865491
     4   MDD                                           0.6664822
     5 AD/HD                                           0.6517358
    

    Expectedly, we get MDD or major depressive disorder. Apparently, antidepressants that inhibit presynaptic reuptake of serotonin (ie. SSRIs) appear to be effective in treating obsessive-compulsive disorder (OCD).

    I may not have done very well here but I learned a lot from my machine learning / artificial intelligence learning buddy. How did you do? Can you think of other biomedical analogies to test?