Collaboration-based Function Prediction in Protein-Protein - - PowerPoint PPT Presentation

▶
collaboration based function prediction in protein
SMART_READER_LITE
LIVE PREVIEW

Collaboration-based Function Prediction in Protein-Protein - - PowerPoint PPT Presentation

Dec 01, 2022 138 likes •439 views

Collaboration-based Function Prediction in Protein-Protein Interaction networks Hossein Rahmani Joint work with: Hendrik Blockeel, Andreas Bender October 2010 Hossein Rahmani (Leiden University) October 2010 1 / 28 Protein-Protein

slide-1
SLIDE 1

Collaboration-based Function Prediction in Protein-Protein Interaction networks

Hossein Rahmani

Joint work with: Hendrik Blockeel, Andreas Bender

October 2010

Hossein Rahmani (Leiden University) October 2010 1 / 28

slide-2
SLIDE 2

Protein-Protein Interaction (PPI) Networks

Hossein Rahmani (Leiden University) October 2010 2 / 28

slide-3
SLIDE 3

Function Prediction in PPI Networks

Hossein Rahmani (Leiden University) October 2010 3 / 28

slide-4
SLIDE 4

Outline

Similarity based Function Prediction Proposed Methods:

Collaboration based Function Prediction

Evaluation

Hossein Rahmani (Leiden University) October 2010 4 / 28

slide-5
SLIDE 5

Similarity based Function Prediction Methods

Similarity based Function Prediction Methods

Assumption:

Interacting proteins have similar functions

Optimization criteria:

Minimizing the number of interacting pairs

  • f proteins with no common function

Majority Rule Functional Clustering

Hossein Rahmani (Leiden University) October 2010 5 / 28

slide-6
SLIDE 6

Similarity based Function Prediction Methods

Majority Rule

Predicted function: Most common function(s) among classified partners Problem: Links unclassified-unclassified proteins completely neglected

Hossein Rahmani (Leiden University) October 2010 6 / 28

slide-7
SLIDE 7

Similarity based Function Prediction Methods

Functional Clustering

Cluster the PPI network Predict the function of unclassified protein based on the cluster they belong to

Hossein Rahmani (Leiden University) October 2010 7 / 28

slide-8
SLIDE 8

Similarity based Function Prediction Methods

Outline

Similarity based Function Prediction Proposed Methods:

Collaboration based Function Prediction

Evaluation

Hossein Rahmani (Leiden University) October 2010 8 / 28

slide-9
SLIDE 9

Collaboration based Function Prediction Method

Collaboration based Function Prediction Method

Main Idea: A biological process is the aggregation

  • f each individual protein’s functions

Assumption: Topologically close proteins tend to have collaborative functions Collaborative functions: Pairs of functions that frequently interface with each other in different interacting proteins A Reinforcement Based Function Predictor (RL) SOM Based Function Predictor protein p:

  • Function Set :

FSp; FSp(fi) Neighborhood Function Vector : NBp; NBp(fj)

Hossein Rahmani (Leiden University) October 2010 9 / 28

slide-10
SLIDE 10

Collaboration based Function Prediction Method

A Reinforcement Based Function Predictor

Hossein Rahmani (Leiden University) October 2010 10 / 28

slide-11
SLIDE 11

Collaboration based Function Prediction Method

A Reinforcement Based Function Predictor

Prediction Time:

Select candidate functions Rank candidate functions based on how well they collaborate with the neighborhood of unclassified protein p Formula (1) assigns a collaboration score to each candidate function fc: Score(fc)=

  • ∀fj∈F

NBp(fj) ∗ FuncColVal(fj, fc) (1)

Hossein Rahmani (Leiden University) October 2010 11 / 28

slide-12
SLIDE 12

Collaboration based Function Prediction Method

SOM Based Function Predictor

Self Organizing Map (SOM) |inputNeurons| = |outputNeurons| = |F| inputNeuron(i) = NBp(fi)

  • utputNeuron(i) = FSp(fi)

Tune Parameters Predict Functions

Hossein Rahmani (Leiden University) October 2010 12 / 28

slide-13
SLIDE 13

Collaboration based Function Prediction Method

Outline

Similarity based Function Prediction Proposed Methods:

Collaboration based Function Prediction

Evaluation

Data sets Parameter Tuning Similarity V.S Collaboration

Hossein Rahmani (Leiden University) October 2010 13 / 28

slide-14
SLIDE 14

Evaluation Datasets

Datasets

Three Yeast Datasets: Krogan, VonMering and DIP-Core Number of Proteins Number of Interactions Von Mering 2401 22000 Krogan 2708 14246 DIP-Core 2388 4400

Table: Statistical information of datasets.

Hossein Rahmani (Leiden University) October 2010 14 / 28

slide-15
SLIDE 15

Evaluation Parameter Tuning

Parameter Tuning

SOM method

Candidate Function Strategy Decreasing Learning Rate Termination Criteria

Majority Rule

Wider Neighborhood Level

Hossein Rahmani (Leiden University) October 2010 15 / 28

slide-16
SLIDE 16

Evaluation Parameter Tuning

“Candidate Function Strategy” in SOM method

Second function level produces the best result

Hossein Rahmani (Leiden University) October 2010 16 / 28

slide-17
SLIDE 17

Evaluation Parameter Tuning

“Decreasing Learning Rate” in SOM method

Fmeasure values maximize when DecLR equals to 0.9

Hossein Rahmani (Leiden University) October 2010 17 / 28

slide-18
SLIDE 18

Evaluation Parameter Tuning

“Termination Criteria” in SOM method

TC = 10 produces the best result

Hossein Rahmani (Leiden University) October 2010 18 / 28

slide-19
SLIDE 19

Evaluation Parameter Tuning

“Wider Neighborhood Level” in Majority Rule method

NB-Li represents the 1-, 2- or 3-neighborhood of the protein Mostly, no improvement by considering wider neighborhood

Hossein Rahmani (Leiden University) October 2010 19 / 28

slide-20
SLIDE 20

Evaluation Similarity Vs Collaboration

Similarity V.S Collaboration

Compare Collaboration based methods (SOM and RL) with Similarity based methods (MR and FC) Five different function levels

11.02.01 (rRNA synthesis) Vs 11.02.03 (mRNA synthesis)

Hossein Rahmani (Leiden University) October 2010 20 / 28

slide-21
SLIDE 21

Evaluation Similarity Vs Collaboration

Similarity V.S Collaboration

Hossein Rahmani (Leiden University) October 2010 21 / 28

slide-22
SLIDE 22

Evaluation Similarity Vs Collaboration

Similarity V.S Collaboration

Hossein Rahmani (Leiden University) October 2010 22 / 28

slide-23
SLIDE 23

Evaluation Similarity Vs Collaboration

Similarity V.s Collaboration

Hossein Rahmani (Leiden University) October 2010 23 / 28

slide-24
SLIDE 24

Evaluation Similarity Vs Collaboration

Similarity V.s Collaboration

In all three datasets, collaboration methods predicts functions more accurately than similarity based methods More detailed functions level → More difference in performance

Hossein Rahmani (Leiden University) October 2010 24 / 28

slide-25
SLIDE 25

Conclusions

Conclusions

Function prediction in PPI networks Similarity based Approaches Collaboration based Approaches

Reward-Punish (RL) Self Organizing Map (SOM)

Similarity V.s Collaboration

3% to 17% improvement in F-measure values

Hossein Rahmani (Leiden University) October 2010 25 / 28

slide-26
SLIDE 26

Thanks!

Hossein Rahmani (Leiden University) October 2010 26 / 28

slide-27
SLIDE 27

Reinforcement based Function Predictor

Reinforcement based Function Predictor

∀fi ∈ FSp : FuncColVal(fi, fj)+= NBp(fj)∗R

support(fj)

∀fi ∈ FSp : FuncColVal(fi, fj)−=

P support(fj)

Candidate Function Strategies:

First Function Level Strategy Second/Third/Fourth Function Level Strategy

Hossein Rahmani (Leiden University) October 2010 27 / 28

slide-28
SLIDE 28

SOM based Function Predictor

SOM based Function Predictor

Hossein Rahmani (Leiden University) October 2010 28 / 28