DISCOVERING LARGER NETWORK MOTIFS

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DISCOVERING LARGER NETWORK MOTIFS

• Li Chen
• 4/16/2009
• CSC 8910 Analysis of Biological Network, Spring
  2009
• Dr. Yi Pan

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS
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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• Two distinct definitions of a motif based on
  frequency and statistical significance
• Definition 1 a motif is a sub-graph that appears
  more than a threshold number of times.
• Definition 2 a motif is a sub-graph that
  appears more often than expected by chance.
  (over-presented motif)

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• Two characteristics used to evaluate a motif
• Frequency
• 1. Arbitrary overlaps of nodes and edges (non-
  identical
• case)
• 2. Only overlaps of nodes (edge-disjoint case)
• 3. No overlaps (edge and vertex-disjoint case)

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• Statistical Significance compares the obtained
  values of the frequencies for the observed and
  random networks.
• 1. Z-score
• 2. Abundance

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• Models of Random Graphs
• Preserves the same degree distribution of
• biological networks
• Preserve degree sequence (search of n-node
  motifs)
• Based on geometric random networks and Poisson
• distribution of the degree
• Incorporate node clustering into model

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• 3. Compact Topological Motifs introduces
  a compact graph
• representation obtained by grouping
  together maximal
• sets of nodes that are
  indistinguishable.
• 
  
• 
  
• 
  
  The graph on the left
  show the
• 
  
  sets U1 and U2 as
  compact nodes
• 
  
  and U1U2 as compact
  edge.

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• Motif Discovery Algorithm
• Exact algorithm on motifs with a small number of
  nodes
• 1. Exhaustive Recursive Search (ERS) the
  input
• network is represented by an adjacency
  matrix M.
• (motif size lt 4)
• 2. ESU starting with individual nodes and
  adding
• one node at a time until the required
  size k is
• reached. (motif size lt14)

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• Approximate Algorithms
• 1. Search Algorithm Based on Sampling (MFINDER)
  it
• picks at random edges of the input graph
  until a set of
• k nodes obtained to get sample sub-graph
  and assigns
• weights to the samples to correct the
  non-uniform
• sampling. It scale will with large networks,
  but does not
• scale well with large motifs.

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• 2. Rand-ESU do not needed to compute the weights
  of all
• samples compared with MFINDER. ESU builds a
  tree
• whose leaves correspond to sub-graphs of size
  k while
• internal nodes correspond to sub-graphs of
  size 1 up to
• k-1, depending on the tree level. It assigns
  to each level
• in the tree a probability that the nodes are
  further
• explored, so as to guarantee all leaves are
  visited with
• uniform probability.

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• 3. NeMoFINDER combines approaches of data
  mining and
• computational biology communities. It
  search for repeated
• trees and extend them to sub-graphs. It
  leads to a
• reduction of the computation time for
  discovery of larger
• motifs, but at the cost of missing some
  potentially
• interesting sub-graphs.

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• 4. Sub-graph Counting by Scalar Computation
  it
• characterize a biological network by a
  set of measures
• based on scalars and functional of the
  adjacency matrix
• associated to the network. Its advantages
  are
• mathematical elegance and computational
  efficiency.

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THE REVIEW ON MODELS AND ALGORITHMS FOR MOTIF
DISCOVERY IN PROTEIN-PROTEIN INTERACTION NETWORKS

• 5. A-priori-based Motif Detection the basic
  idea is if a sub-
• graph is frequent so are all its
  sub-graphs. It builds
• candidate motifs of size k by joining
  motifs of size k-1 and
• then evaluating their frequency.

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS
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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Desirable features of clustering algorithms to
  evaluate
• Scalability
• Robustness
• Order insensitivity
• Minimum user-specified input
• Mixed data types
• Arbitrary-shaped clusters
• Point proportion admissibility Duplicating data
  and re-clustering should not alter the results.

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Five categories clustering algorithm
• Partitioning Clustering Algorithm
• Hierarchical Clustering Algorithm
• Grid-based Clustering Algorithm
• Density-based Clustering Algorithm
• Model-based Clustering Algorithm
• Graph-based Clustering Algorithm

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Partition Clustering Algorithm
• Numerical Methods
• 1. K-means algorithm and Farthest First Traversal
  k-center (FFT) algorithm
• 2. K-medoids or PAM (Partitioning Around
  Medoids)
• 3. CLARA (Clustering Large Applications)
• 4. CLARANS (Clustering Large Applications Based
  upon
• Randomized Search) and Fuzzy K-means

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Discrete Methods
• 1. K-modes
• 2. Fuzzy K-modes
• 3. Squeezer and COOLCAT.
• Mixed of Discrete and Numerical Clustering
  Methods
• 1. K-prototypes

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Hierarchical Clustering Algorithm
• Divide the data into a tree of nodes, where each
  node represents a cluster.
• Two categories based on methods or purposes
• 1. Agglomerative vs. Divisive
• 2. Single vs. Complete vs. Average linkage

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Popular natures can have various levels of
  subsets
• Drawbacks
• 1. Slow
• 2. Errors are not tolerable
• 3. Information losses when moving the levels
• Two kinds of methods
• 1. Numerical Methods BIRCH, CURE , Spectral
  clustering
• 2. Discrete Methods ROCK, Chameleon, LIMBO

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Grid-based Clustering Algorithm
• Form a grid structure of cells from the input
  data. Then each data is distributed in a cell of
  the grid.
• STING combines a numerical grid-base clustering
  method and hierarchical method

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Density-based Clustering Algorithm
• Use a local density standard
• Clusters are dense subspaces separated by low
  density spaces
• Examples of bioinformatics application finding
  the densest subspaces in interactome(protein-prote
  in interaction) networks

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• DBSCAN, OPTICS, DENCLUE, WaveCluster, CLIQUE use
  numerical values for clustering
• SEQOPTICS is used for sequence clustering
• HIERDENC (Hierarchical Density-based Clustering),
• MULIC (Multiple Layer Incremental
  Clustering), Projected (subspace) clustering,
  CACTUS, STIRR, CLICK, CLOPE use discrete values
  for clustering

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Model-based Clustering Algorithm
• Uses a model often derived by a statistical
  distribution
• Bioinformatics applications
• 1. gene expression
• 2. interactomes
• 3. sequences

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Numerical model-based methods
• 1. Self-Organizing Maps
• Discrete model-based clustering algorithm
• 1. COBWEB
• Numerical and discrete model-based clustering
  methods
• 1. BILCOM (Bi-level clustering of Mixed Discrete
  and
• Numerical Biomedical Data) using empirical
  Bayesian
• approach

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Examples
• 1. Gene expression clustering
• 2. Protein sequence clustering
• 3. AutoClass
• 4. SVM Clustering methods
• Graph-based Clustering Algorithm
• Applied to interactomers for complex prediction
  and sequence networks

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Examples
• 1. MCODE (Molecular Complex Detection)
• 2. SPC (Super Paramagnetic Clustering)
• 3. RNSC (Restricted Neighborhood Search
  Clustering)
• 4. MCL(Markov Clustering)
• 5. TribeMCL
• 6. SPC
• 7. CD-HIT
• 8. ProClust
• 9. BAG algorithms

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A ROADMAP OF CLUSTERING ALGORITHM IN
BIOINFORMATICS APPLICATIONS

• Usage in Bioinformatics Applications
• Gene expression clustering
• 1. K-means algorithm
• 2. Hierarchical algorithm
• 3. SOMs
• Interactomes
• 1. AutoClass,
• 2. SVM clustering
• 3. COBSEB
• 4. MULIC
• Sequence clustering
• 1. Hierarchical clustering algorithm

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REFERENCES

• 1 Bill Andreopoulos, Aijun An, Xiaogang Wang,
  and Michael Schroeder. A roadmap of clustering
  algorithms finding a match for a biomedical
  application. Brief Bioinform, pages bbn058,
  February 2009.
• 2 Alberto Apostolico, Matteo Comin, and Laxmi
  Parida". Bridging Lossy and Lossless Compression
  by Motif Pattern Discovery. Electronic Notes in
  Discrete Mathematics, 21219 - 225, 2005. General
  Theory of Information Transfer and Combinatorics.
  
• 3 Giovanni Ciriello and Concettina Guerra. A
  review on models and algorithms for motif
  discovery in protein-protein interaction
  networks. Brief Funct Genomic Proteomic,
  7(2)147-156, 2008.
• 4 Jun Huan, Wei Wang, and Jan Prins. Efficient
  Mining of Frequent Subgraphs in the Presence of
  Isomorphism. Data Mining, IEEE International
  Conference on, 0549, 2003.
• 5 Michihiro Kuramochi and George Karypis.
  Finding Frequent Patterns in a Large Sparse
  Graph. Data Mining and Knowledge Discovery,
  11(3)243-271, November 2005.
• 6 Laxmi Parida. Discovering Topological Motifs
  Using a Compact Notation. Journal of
  Computational Biology, 14(3)300-323, 2007.

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• Thank you so much !