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
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