1. Gene based diagnostic prediction of cancers by using Artificial Neural Network Liya Wang ECE/CS/ME539

2. Introduction Neuroblastoma (NB), Burkitt lymphomas (BL), Rhabdmyosarcoma (RMS) and Ewing family of tumors (EWS) are different cancers with similar appearance on routine histology. Accurate diagnosis is essential because the treatment options vary widely depending on the diagnosis. Gene-expression technique provides a new support for diagnosis.

3. Project Description There are 63 training samples and 20 testing samples. Each sample is expressed by 96 genes. Nature Medicine, 7(6), June 2001 Generalized Hebbian Algorithm (GHA) is used for extracting the principal components of the gene data. Multilayer Perceptron with Back-propagation learning is responsible for performing the actual classification.

4. Neural Network Design y1y1 y2y2 ynyn  x1x1 x2x2 xmxm x3x3    GHAMLP m  n, m=96,n=10 1

5. Generalized Hebbian Algorithm Initialize the weights of the network, w ji, to small random values at time k=1. Assign a small positive value to the learning-rate parameter . Calculate Increment k by 1, go to step 2, and continue until w ji reach their steady-state values.

6. GHA weights convergence

7. Multilayer Perceptron 10 input neurons, 4 output neurons, no hidden layer. Output neurons use sigmoidal active function Output from output nodes for training samples are scaled to: [0.2 0.8] Use the entire training set to estimate training error # training samples = epoch size = 63

8. Training error

9. Results Testing Sample ANN TESTING RESULTS RMS NB BL EWS ANN Histological Classification Diagnosis TEST-8 0.1442 0.7870 0.1792 0.2624 NB NB-C TEST-10 0.5990 0.2723 0.2461 0.3118 RMS RMS-T TEST-1 0.1653 0.7139 0.2301 0.3106 NB NB-C TEST-2 0.3876 0.3043 0.2748 0.5413 EWS EWS-C TEST-4 0.6657 0.2144 0.2002 0.2539 RMS RMS-T TEST-7 0.2227 0.2422 0.6153 0.2482 BL BL-C TEST-12 0.3128 0.3331 0.2252 0.5438 EWS EWS-T TEST-24 0.7712 0.1790 0.2240 0.2216 RMS RMS-T TEST-6 0.1621 0.2466 0.1415 0.8613 EWS EWS-T TEST-21 0.4177 0.2710 0.2498 0.4708 EWS EWS TEST-20 0.3601 0.2990 0.3810 0.3642 BL EWS-T

10. Results (Cont’d) Testing Sample ANN TESTING RESULTS RMS NB BL EWS ANN Histological Classification Diagnosis TEST-17 0.7414 0.2832 0.2797 0.2314 RMS RMS-T TEST-18 0.2449 0.2418 0.6643 0.2654 BL BL-C TEST-22 0.8005 0.1918 0.2326 0.1933 RMS RMS-T TEST-16 0.1853 0.6235 0.2367 0.3035 NB NB-T TEST-23 0.2961 0.4018 0.2442 0.3506 NB NB-T TEST-14 0.1631 0.4309 0.1634 0.3259 NB NB-T TEST-25 0.1514 0.6670 0.2195 0.3540 NB NB-T TEST-15 0.2587 0.2909 0.5125 0.3386 BL BL-C TEST-19 0.2846 0.2398 0.2240 0.6775 EWS EWS