bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P38. MINING ACROSS “OMICS” DATA FOR DRUG PRIORITIZATION
Stefan Naulaerts 1,2* , Pieter Meysman 1,2 , Bart Goethals 1 , Wim Vanden Berghe ,3 & Kris Laukens 1,2 .
Advanced Database Research and Modeling (ADReM), University of Antwerp 1 ; Biomedical informatics research center
Antwerpen (biomina) 2 ; Department for Biomedical Sciences, University of Antwerp 3 . * stefan.naulaerts@uantwerpen.be
Drug resistance and response have traditionally been investigated by means of case-by-case studies. The process to
profile drug compounds is time and resource intensive. Large scale information on gene expression and protein
abundance, protein interactions, as well as functional and pathways annotations exist nowadays, as well as freely
accessible repositories for drug targets. Also structural evidence of select drug compounds is publicly available. These
data offer an enormous opportunity for data integration and pattern mining efforts across each of these levels. Here, we
apply frequent itemset mining to identify structurally similar compounds, and to detect patterns within the biological
effect profiles of these chemical compound families. Next, we explore how we can link both types of patterns to metainformation
(such as drug interactions) in a bid to identify promising compounds and speed up the drug discovery
process by means of candidate prioritization.
INTRODUCTION
In the last decades, several widely used databases have
emerged. These vary from gene expression data and massspectrometric
protein identifications to resources covering
interaction graphs or functional annotations of proteins
and chemicals.
The presence of these resources offers interesting
opportunities to gain deeper insight in drug mode of action,
as well as help reduce important bottlenecks with regards
to the speed of novel drug discovery or drug repurposing,
by intelligently prioritizing potentially interesting
compounds.
METHODS
To integrate the listed kinds of data, we use pattern mining
methods that are collectively known as “frequent itemset
mining”. This set of techniques uses clever heuristics to
efficiently find items that occur more often together than a
minimal threshold. In this work, we identified several
pattern types based on their source:
Expression itemsets
Metadata itemsets
Graph patterns (protein-protein, protein-drug and
chemical structures)
For subgraph mining, we used GASTON 1 . All other data
sources were analysed with Apriori 2 .
To deal with the extreme numbers of patterns that result
from mining this kind of data, we used a filter which
incorporates several quality measures based on objective
data mining measures properties (e.g. lift), as well as more
biologically inspired methods (e.g. functional coherence in
the Gene Ontology 3 tree).
Simple classification based on the patterns was performed
with CBA 4 .
RESULTS & DISCUSSION
We were able to identify several backbone patterns within
the chemical structures studied and used these to define
“chemical compound families”. Next, we used this
classification as starting point to group experimental
evidence (bio-assays, interactions and metadata). After
applying cut-offs based on the quality measures, all
patterns remaining were significant and made sense
biologically.
Unsurprisingly, structurally similar compound families
show significant pattern overlaps in drug-drug interactions,
gene expression, term co-occurrence and conserved
protein-protein interactions. We found that specific
patterns in the biological profile often correlate with
specific discriminative structural patterns. Moreover, these
collections of structural frequent subgraphs seemed highly
relevant for the mode in which a compound connects to
the “core” proteome. This central proteome performs
essential functions of the cell (e.g. energy metabolism) and
it is known to be conserved across cell types. Structurally
distinct compound families converge much later (if at all)
to the same “core proteins” than more similar chemicals
do. This observation corresponds to currently known
pathway knowledge and tissue biology.
We were further able to associate previously unseen
compounds to chemicals present in the database, based on
the subgraph collection and by extension to the biological
profile patterns. Manual survey of literature indicated that
several compounds not covered by our database have
recently been approved or are in testing as alternative
drugs to the compounds we hypothesized as being
substantially similar.
FIGURE 1. Visualizing the dexamethasone environment. Both predictions
and experimental evidence (drug-target and protein-protein interactions)
are shown.
REFERENCES
1. Nijssen S & Kok J. ENTCS 127, 77-87 (2005).
2. Agrawal R & Srikant R. Proc 20th Int Conf on Very Large Databases
(1994).
3. Ashburner M et al. Nat Genet 25, 25-29 (2000).
4. Liu B et al. KDD (1998).
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