This document describes a multi-objective simulated annealing (MOSA) based clustering method for cancer diagnosis and classification using gene expression data. The method uses MOSA to identify optimal clustering solutions based on multiple objectives like minimizing intra-cluster distances and maximizing inter-cluster distances. It is tested on three cancer gene expression datasets and able to accurately cluster samples and identify relevant gene markers for different cancer types. The method performs better than other algorithms like MOGASVM in terms of clustering accuracy and identifies clinically relevant gene markers for cancer subtypes.

1. MULTI OBJECTIVE SIMULATED ANNEALING-
BASED CLUSTERING OF TISSUE SAMPLES FOR
CANCER DIAGNOSIS

2. CONTENTS
 INTRODUCTION
 STEPS
 Input preprocessing
String representation and archive initialization
Assignment of points and computation of objective functions
Search operators
Selecting best clustering solution from the pareto optimal front
 EXPERIMENTAL SETUP
Datasets
Evaluation metrics
Gene marker identification
 RESULT
Clustering performance
Gene markers for brain tumor dataset
 CONCLUSION

3. INTRODUCTION
 Classification done with the help of microarray technology
 Binary cancer cell classification
Benign (noncancerous)
Malignant (cancerous)
 Multiple class cancer cell classification
 MOO based clustering method for classification
 Archived multi objective simulated annealing (AMOSA)

4.  Datasets Brain tumor, Adult malignancy and Small round
blood cell tumors (SRBCT)
Two clustering performance matrices
 Adjusted rand index (ARI)
 % classification accuracy (%CoA)
 MOGASVM utilizes NSGA ll and SVM
 Generalized unsupervised clustering algorithm

5. STEPS
 INPUT PREPROCESSING
 Variance of all genes are calculated
 Top 200 genes are selected from the list
 Log transformations of the gene values are calculated
 Sample is normalized to mean 0 and variance 1

6.  STRING REPRESENTATION AND ARCHIVE INITIALIZATION
 Utilizes the concept of string
 Each member represent one clustering solution
 Each member have different length
li= d ∗ Ki
 i is chosen between Kmin and Kmax
Ki = [rand()mod(Kmax-1)]+2
 Whole clusters varying between 2 and Kmax
 Ki represented as a string

7.  ASSIGNMENT OF POINTS AND COMPUTATION OF OBJECTIVE
FUNCTIONS
 Values of n samples are calculated using FCM algorithm
 Samples are similar to a particular center
 Degree of Xi with respect to Cj is
 Compute XB index, FCM index, PBM index
 Value of XB and FCM are minimized and value of PBM should
be maximized

8.  SEARCH OPERATORS
 Using three mutation operation
 To change cluster centre by small amount
Center is selected for mutation ,then for all of its dimensions mutation is
applied
To decrease the size of string by one
 Each cluster center is considered to be indivisible
 To increase the size of string by one

9. SELECTING BEST CLUSTERING SOLUTION FROM THE PARETO
OPTIMAL FRONT
 Best solution is selected using external cluster validity index and ARI
 Select single solution using semi supervised approach
 Based on class label information ARI values can be calculated

10. EXPERIMENTAL SETUP
 DATASETS
 Brain tumor 42 tumor samples
 Adult malignancy 190 tumor samples
 SRBCT 63 tumor samples
 EVALUATION METRICS
 Choosing 2 matrices for evaluating clustering solution

11.  GENE MARKER IDENTIFICATION
 Clustering solutions are collected from AMOSA
 Clustered into five tumor classes such as MGLIO, RHAB,
NCER, PNET, MD Class
 Top ten genes are selected from SNR list
 For other subtypes ten gene markers for each type are selected
 Final set of selected ten gene markers changes slightly after execution

12. RESULT
 CLUSTERING PERFORMANCE

13.  Determine the cancer class of data points
 From the obtained cluster actual class label can be calculated
 Calculate the frequency of each cancer type
 Cancer type with highest frequency be the type of obtained cluster
Dataset ARI %CoA
Adult malignancy 3.81% 1.25%
Brain tumor 5.33% 3.65%
SRBCT 36.7% 14.44%

14.  AMOSA performs much better than other algorithm and also
MOGASVM
 MOGASVM uses MOGA to solve clustering problem and utilizes
principle of SVM to combine the solution
 So MOGASVM is time complex
 AMOSA without using post processing procedure
 It is less time complex

15.  GENE MARKERS FOR BRAIN TUMOR DATASET

16.  Row Identified gene markers
 Column Class name of the sample
 Cells in the heat map represents expression level of gene marker in terms of
color
 Red color represents high expression
 Green color represents low expression
 Black color represents absence of differential expression

17. CONCLUSION
 MOO problem can be solved with the help of AMOSA based clustering
 AMOSA based clustering performs better than other algorithm
 Effectiveness of proposed method finds better solution with in reasonable
time frame

18. REFERENCES
 S. Bandyopadhyay, S. Saha, U. Maulik, and K. Deb, “A simulated
annealing-based multi objective optimization algorithm: Amosa,” IEEE
Trans. Evol. Compute., vol. 12, no. 3, pp. 269–283, Jun. 2008.
 K. Deb, A. Pratap, S. Agrawal, and T. Meyarivan, “A fast and elitist
multi objective genetic algorithm: NSGA-II,” IEEE Trans. Evol.
Compute., vol. 6, no. 2, pp. 182–197, Apr. 2002.
 S. Bandyopadhyay and S. Saha, “Unsupervised classification: Similarity
measures,” in Classical Metaheuristic Approaches, Appl. New York, NY,
USA: Springer, 2012.
 L. An and R. W. Doerge, “Dynamic Clustering of Gene Expression,
ISRN Bioinformatics, vol. 2012, art. no. 537217, pp. 1–12, 2012, doi:
10.5402/2012/537217.
 Y. Wang and Y. Pan, “Semi-supervised consensus clustering for gene
expression data analysis, Bio Data Mining vol. 7.1, pp. 1–13, 2014.

19. THANK YOU