Computational Support System for Personalized Medicine

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Computational Support System for Personalized Medicine. GaneshKumar Pugalendhi. School of Computer Science and Engineering. Kyungpook National ...
Computational Support System for Personalized Medicine GaneshKumar Pugalendhi

Ku-Jin Kim*

School of Computer Science and Engineering Kyungpook National University, South Korea

School of Computer Science and Engineering Kyungpook National University, South Korea

[email protected]

[email protected] *

Corresponding Author

ABSTRACT The abundance of genes with the scarcity of samples tending towards a convinced group of disease stresses physician during the personalized medicine treatment. This paper suggests an amalgamation of different intelligent techniques to comprehend the patient’s genetic information during multicategory diagnosis. An improved fuzzy rough set based on lower approximation is proposed to compute f-information extrinsically to filter gene subset. Water swirl algorithm inspired by the water movement in the sink is recommended to identify potential genes intrinsically using fuzzy rule based decision support system.

3. COMPUTATIONAL FRAMEWORK Gene Expression Data

Generation of FEPM

IMPROVED FUZZY ROUGH SET

FILTER METHOD Computation of FI

Filtered Genes

Categories and Subject Descriptors Computational System Biology, Clinical and Health Decision Support System

FUZZY RULE BASED COMPUTATIONAL SUPPORT SYSTEM

General Terms

If-Then Rules

Algorithms, Design, Performance, Verification EMBEDDED METHOD

Keywords

WATER SWIRL ALGORI THM

Membership Function

Personalized medicine, fuzzy logic, water swirl algorithm

1. PERSONALIZED HEALTH CARE TRADITIONAL THERAPY

4. IMPROVED FUZZY ROUGH BASED F-INFORMATION (IFRFI)

1. Drugs targeted at broad segments 2. Pathology determines therapy.

SET

1. Gene-Group significance Fsig Gi j ,Gc  

3. Significant adverse effects

n n 1 n  G   PL i  j  PLGc  j   12  PLGi  j  PLGc  j  n j 1 n j 1 J 1

PERSONALIZED THERAPY 1. Drugs targeted at distinct segments

n n 1 n    PH Gi  j  PH Gc  j   12  PH Gi  j  PH Gc  j  n j 1 n j 1 J 1

2. Genomic profiles determine therapy 3. Minimal adverse effects

2. GENE EXPRESSION DATA: ISSUES Patients

Gene 1

Gene 2

… Gene m

Patient 1 Patient 2 … Patient n

96.42 38.42 … 21.72

21.43 … 40.71 29.19 … 31.15 … … … 38.05 … 26.41 Dimensionality

Disease Normal Tumor … Cancer

Multicategory

Scarcity

Disparity

Class Label

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n n 1 n    PM Gi  j  PM Gc  j   12  PM Gi  j  PM Gc  j n j 1 n j 1 J 1





2. Gene-Gene Severance Fsev Fsig , Grem  x j

x j

n n l  1 n  l   PL relx  j  PLlremx  j   12  PLlrelx  j  PL remx  j  n j  1 j 1  n j 1 n n l  1 n    PH lrelx  j  PH lremx  j   12  PH lrelx  j  PH remx  j  n j  1 j 1  n j 1 n n l  1 n    PM lrelx  j  PM lremx  j   12  PM lrelx  j  PM remx  j n j  1 j 1  n j 1

3. FI  min Fsig  Fsev

BCB '15, September 09-12 2015, Atlanta, GA, USA ACM 978-1-4503-3853-0/15/09. http://dx.doi.org/10.1145/2808719.2811422 ACM-BCB 2015

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5. WATER SWIRL ALGORITHM (WSA)

Comprehensibility of rules generated using WSA for GRM dataset 1 0.9

Co-Firing Comprehensive Index

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Figure 1 Vortex Ring

0

5

10

15 20 Rule Number

25

30

35

Figure 4 Comprehensibility of rules by WSA for GRM dataset

START

ROC Analysis 1

Read the number of water particles, boundary of each particle and max. iterations

0.9 0.8 0.7

Generate particle’s position, strength and reference position randomly within the specified range

T11 T14 BT1 GCM GRM

TP Rate

0.6 0.5 0.4

For each water particle’s position (xp) evaluate fitness

0.3 0.2

Loop until maximum iteration

If fitness(xp) better than fitness(xprevBest) then xprevBest = xp

0.1 0

Loop until all particles exhaust Set best of xprevBest as xgBest

STOP

Figure 2 Flowchart of WSA Table 1 Details of multicategory gene expression dataset

#Samples 174 308 90 144 123

#Classes 11 26 5 14 11

0.9

Mean Error Value

0.8

0.7

0.6

0.5

0.4

6

8 10 12 Number of genes

14

0.5 0.6 FP Rate

0.7

0.8

0.9

1

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF2013R1A1A2A10004391) and the BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005).

9. REFERENCES

Results of Monte Carlo Cross Validation 1

4

0.4

8. ACKNOWLEDGEMENT

6. SIMULATION RESULTS

2

0.3

Experimental results with gene expression data clearly depicts that the proposed method works well in finding out accurate and interpretable rules with compact disease-causing genes to comprehend well the vital interrogations demanded by personalized medicine practitioners. It is substantiated that the proposed IFRFI-WSA results in near optimal neither overfitted nor an underfitted knowledge-based system for use in personalized therapy.

Return xgBest as optimal solution

0

0.2

7. CONCLUSION

Update particle’s position using equation (7)

#Genes 12533 15009 5920 16063 7129

0.1

Figure 5 ROC analyzes of IFRFI-WSA System

Update particle’s strength using equation (6)

Dataset 11_Tumors (T11) 14_Tumors (T14) Brain_Tumor1 (BT1) GCM GCM RM (GRM)

0

16

18

20

[1] Rani, C. 2012. Intelligent Optimization Techniques for Fuzzy Logic based Data Classification. Doctoral Thesis. Anna University, Tamil Nadu, India. [2] Zhnag, Y., Cheng, Y., Jia, K., Zhang, A. 2014. Opportunities for Computational Techniques for MultiOmics Integrated Personalized Medicine. Tsinghua Science and Technology. 19, 6, 545-558. [3] GaneshKumar, P., Rani, C., Mahibha, D., Victoire, T.A.A. 2015. Fuzzy-rough-neural-based f-information for gene selection and sample classification. International Journal of Data Mining and Bioinformatics, 11, 1, 31-52.

Figure 3 Generalization ability of WSA using GRM dataset

ACM-BCB 2015

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