Integrating Ontological Prior Knowledge into Relational Learning for Protein Function Prediction

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Integrating Ontological Prior Knowledge into
Relational Learning for Protein Function
Prediction
Stefan ReckowMax Planck Institute of
PsychiatryVolker TrespSiemens, Corporate
Technology
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Proteins and Protein Ontologies
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Protein and Protein Functions

• motivation
• proteins molecular machines in any organism
• understanding protein function is essential for
  all areas of bio-sciences
• diverse sources of knowledge about proteins

• challenges
• experimental determination of functions difficult
  and expensive
• homologies can be misleading
• most proteins have several functions

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Protein function prediction
What function does this protein have?
catalytic activity (catalyzes a
reaction) isomerase activity intramolecular
oxidoreductase activity
specificity
intramolecular oxidoreductase activity,
interconverting aldoses and ketoses
triose-phosphate isomerase activity (catalyzes a
very specific reaction)
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Function Ontologies

• ontologies are a way of bringing order in the
  function of proteins
• an ontology is a description of concepts of a
  domain and their relationships
• hierarchical representation (subclass-relationship
  )
• tree
• directed, acyclic graph

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Complex Ontology

• complex structure formed by a group of two or
  more proteins to perfom certain functions
  concertedly

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Ontologies as Great Source of Prior Knowledge in
Machine Learning

• A considerable amount of community effort is
  invested in designing ontologies
• Typically this prior knowledge is deterministic
  (logical constraints)
• Machine Learning should be able to exploit this
  knowledge
• Interactions of proteins is an important
  information for predicting function statistical
  relational learning

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Statistical Relational Learning with the IHRM
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Statistical Relational Learning (SRL)

• SRL generalizes standard Machine Learning to
  domains where relations between entities (and not
  just entity attributes) play a significant role
• Examples PRM, DAPER, MLN, RMN, RDN
• The IHRM is an easily applicable general model,
  performs a cluster analysis of relational domains
  and requires no structural learning
• Z. Xu, V. Tresp, K. Yu, and H.-P. Kriegel.
  Infinite hidden relational models. In Proc. 22nd
  UAI, 2006
• Kemp, C., Tenenbaum, J. B., Griffiths, T. L.,
  Yamada, T. Ueda, N. (2006). Learning systems of
  concepts with an infinite relational model. AAAI
  2006

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Standard Latent Model for Protein Mixture Models

• Protein1

Protein2

• In a Bayesian approach, we can permit an infinite
  number of states in the latent variables and
  achieve a Dirichlet Process Mixture Model (DPM)
• Advantage the model only uses a finite number of
  those states thus no time consuming structural
  optimization is required

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Infinite Hidden Relational Model (IHRM)

• Permits us to include protein-protein
  interactions into the model

interact

• Protein1

Protein3
interact
interact
Protein2
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Ground Network
function
motif
complex
Z2
motif
interact
interact
complex
Z3
interact
Z1
function
function
motif
complex
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Experimental Results

• KDD Cup 2001
• Yeast genome data
• 1243 genes/proteins 862 (training) / 381 (test)
• Attributes
• Chromosome
• Motif (351) 1-6 A gene might contain one or
  more characteristic motifs (information about the
  amino acid sequence of the protein)
• Essential
• Structural class (24) 1-2 The protein coded by
  the gene might belong to one or more structural
  categories (24) 1-2
• Phenotype (11)1-6 observed phenotypes in the
  organism
• Interaction
• Complex (56)1-3 The expression of the gene can
  complex with others to form a larger protein
• Function (14)1-4 (cell growth, cell
  organization, transport, )
• genes were anonymous

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Results
Comparison with Supervised Models
ROC curve
Accuracy
Model
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IHRM Result
Node gene Link interaction Color cluster.
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Integrating Ontological Prior Knowledge into the
IHRM
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Integration of ontologies
Deductive closure
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Integration of ontologies
Zi
independent concepts
dependent concepts
function
motif
complex
cytoskeleton
translocon
actin filaments
microtubules
signal peptidase
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Experiments Including Complex Ontology

• Data collected from CYGD of MIPS
• 1000 genes/proteins 800 (Training) / 200 (Test)
• Attributes
• chromosome, motif, essential, structural class,
  phenotype, interaction, complex, function
• interactions from DIP
• usage of ontological knowledge on complex
• five levels of hierarchal
• in our model 258 nodes (concepts) using 66 top
  level categories
• every protein has at least one complex annotation
• After including ontological constraints about
  three annotations per protein on average

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Results
800 (training) / 200 (test) 200 (training)
/ 200 (test)
w/o ontology 0.895 with ontology 0.928
w/o ontology 0.832 with ontology 0.894
AUC
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Results
explicit modeling of dependencies
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Results

• Grey in test set

• proteins concerned with secretion and
  transportation
• The "Golgi apparatus" works together with the
  "endoplasmatic reticulum (ER)" as the transport
  and delivery system of the cell.
• "SNARE" proteins help to direct material to the
  correct destination
• Test proteins also "cellular transport"

• proteins acting in cell division
• control proteins
• "Septins Septins have several roles throughout
  the cell cycle and carry out essential functions
  in cytokinesis
• The three highlighted proteins fit into this
  cluster ( "cell fate" and "cell type
  differentiation)

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Results
sampling convergence
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Results
Distribution of proteins in the clusters
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Results

• Grey former singletons

• Cellular Transport Cluster
• The former singleton "Clathrin light chain", as a
  major constituent of coated vesicles (a component
  for transport) fits into this cluster quite well

• Tasks occurring during DNA replication
• The former singleton "DNA polymerase", as a main
  actor in replication, obviously is assigned the
  correct cluster here

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Conclusion

• application of the IHRM to function prediction
• competitive with supervised learning methods
• insights into the solution
• advantages of integrating ontological knowledge
• improvement of the clustering structure
• robustness stable results with varying
  parameterization
• deductive closure prior to learning is a general
  powerful principle
• future challenges
• usage of several or more complex ontologies
• further analysis of dependent vs. independent
  concepts
• Acknowledgements
• Karsten Borgwardt (MPIs Tübingen)
  Hans-Peter Kriegel (LMU)