Svetlana Bojić @ Startit Meetup

1. Ulcerous colitis,
irritable bowel
syndrome and
microbiota
Can machine learning help
forecasting out of poop?
Bojic Svetlana, R user, MD
University of Belgrade

2. Human microbiota
1 human cell
for every 10
microbial cells
• Are we only humans (Homo sapiens)?
1 human gene
For every 100-
1000 microbial
genes
……if there was democracy in our bodies…

3. Gut microbiota-”forgotten organ”
• Synthesize and excrete vitamins, transform non-
digestible carbohydrates, produce SCFA that
feed colonocytes
• Prevent colonization by pathogens-”defending
the territory”
• May antagonize other bacteria
• Educate the immune system

4. How can we tell who is in there?
• Each bacteria needs to produce
proteins- hence has ribosomes
(and ribosomal DNA encoded in
its genome)
• While large parts of 16S
ribosomal DNA are “conserved”,
V1 and V6 are highly specific
for particular bacterial strain
• V1 and V6 regions (24-72bp) of all known intestinal bacteria were
selected as basis for the probe design
• 3699 unique probes were printed on the microarray slide
• We have culture independent tool to assess and quantify the
presence of ~1000 bacterial strains simultaneously

5. HITChip based experiment
microbial
community DNA
RNA
hybri
disation
Nucleic acids
extraction & labelling
Data analysis
- Profiling
- Identification
- Quantification
imaging
• ….You end up with data frame,
the intensity of the signal from
the given probe per sample
• One could combine those with
phylogenetic map, to get the
abundancy of particular bacteria
on genus or phylum level

6. Our problem
• Ulcerative colitis (UC)
is a chronic, or long
lasting disease that
causes inflammation-
irritation or swelling- and
ulcers on the inner lining
of the large intestine
• Irritable bowel
syndrome (IBS) is a
group of symptoms –
including pain or
discomfort in your
abdomen and changes
in your bowel movement
patterns- that occur
together.
• It’s a functional
gastrointestinal disorder.
Endoscopies of the large
intestine are the most
accurate methods for
diagnosing ulcerative colitis
We need less invasive
alternative!

7. calprotectin
• One large (n=2499) meta analysis gave pooled estimate of sensitivity and
specificity for calprotectin (0.88, 0.73) for assassment of endoscopically
defined disease activity in UC. (Mosli et al, 2015)
• marker of neutrophilic intestinal inflammation
• On the other hand, Clostridium
sphenoides and Hemophilus
strongly correlate with
calprotectin levels!
Kolho et. al. 2015

8. Our data : 150 patients X 3699 features
• Preliminary
constrained RDA
analysis on phylum
level confirmed that
there is significant
effect of health
status on microbial
composition (p<0.01)
• The health status
alone could explain
as much as 10.2% of
variability, even
when the influence
of ProjectID and
gender was partialed
out.

9. Supervised machine learning procedures
• The most successful
model – from elastic
net family
(sensitivity=0.5,
specificity=0.98 for UC
class) utilized only 89
unique probes- ..and
all made biological
sense!
* Most common error was mistaking diarrhea
predominant IBS subtype for UC and vice
versa
• Why we love R : package
‘caret’ has a number of models
implemented, we had
preference for feature-selection
algorithms

10. Yet, situation complicates further…
..which means that one out of 10 labels in our training set
could be wrong!

11. And that calls for semi-supervised
methods…
• These are the methods that
make use of both labeled and
unlabeled data to train the
model
• We are currently experimenting
with upclass package, and are
in the correspondence with MDs
• …+ implementing the learning
algorithms of our own