1. By Eng. Monther Alhamdoosh Supervisor: Prof. Rita Casadio Co-supervisor: Dr. Piero Fariselli Disulfide Connectivity Prediction Using Machine Learning Approaches LAUREA MAGISTRALE IN BIOINFORMATICS INTERNATIONAL BOLOGNA MASTER IN BIOINFORMATICS ALMA MATER STUDIORUM ▪ UNIVERSITÀ DI BOLOGNA Session II 2009/2010

2. In Literature September 10th, 2010 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh 2 Accuracy indices The percentage of connectivity patterns that are correctly predicted. The percentage of disulfide bridges that are correctly predicted. δ(x, y) = 1 when the predicted pattern y matches the correct pattern x. Introduction  The Amino Acid Cysteine  Importance of SS Bonds  Machine Learning Statement of the Problem  Aim of Research  In Literature Our Proposed Solutions Results Comparisons with previous methods Conclusions

3. Our Proposed Solutions September 10th, 2010 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh 3 Introduction  The Amino Acid Cysteine  Importance of SS Bonds  Machine Learning Statement of the Problem  Aim of Research  In Literature Our Proposed Solutions Results Comparisons with previous methods Conclusions Machine Learning 1 2 3 4 Basic System Design Pattern Scoring Schemes

4. Our Proposed Solutions September 10th, 2010 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh 4 Step 3: Estimate the disulfide propensity Neural Networks-based Models Single-Layer Feed-forward Network (SLFN). Extreme Learning Machines (ELMs). Pseudo-inverse matrix to get output weights. Additive (Sigmoid) Hidden Neurons RBF (Guassian) Hidden Neurons. Back-propagation (BP). Gradient Descent to get all weights. Support Vector Machines (SVM) Support Vector Regression (SVR). Radial Basis Function (RBF) Kernels. Grid Search is used to find the best values for g and c. Introduction  The Amino Acid Cysteine  Importance of SS Bonds  Machine Learning Statement of the Problem  Aim of Research  In Literature Our Proposed Solutions Results Comparisons with previous methods Conclusions

5. SLFN September 10th, 2010 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh 5 ELM (Additive vs. RBF hidden neurons) Training Time curves Introduction  The Amino Acid Cysteine  Importance of SS Bonds  Machine Learning Statement of the Problem  Aim of Research  In Literature Our Proposed Solutions Results Comparisons with previous methods Conclusions Additive Hidden NeuronsRBF Hidden Neurons Number of Neurons

6. ELM outperforms BP September 10th, 2010 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh 6 The accuracy values of ELM and BP Performance Enhancement Introduction  The Amino Acid Cysteine  Importance of SS Bonds  Machine Learning Statement of the Problem  Aim of Research  In Literature Our Proposed Solutions Results Comparisons with previous methods Conclusions Comparison of different ELM and BP models. Model B = 2B = 3B = 4B = 5Overall Best # of neurons Time (s) QcQc QpQp QcQc QpQp QcQc QpQp QcQc QpQp QcQc QpQp ELM (Sig)65 42284224274464115028.52 ELM (RBF)66 4532452631548439018.55 BP (Sig)62 38264023295443895559.29 Our method performance with L1 RBF kernels initialized using k-mean clustering. The Best performing number of hidden neurons is 270 and the corresponding training time is 425.11 seconds. Connectivity Size 2345overal QcQc 6748443751 QpQp 673627645

7. SVR vs. NN September 10th, 2010 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh 7 Comparison of SVR and NN-based methods Both tested on PDB0909 with Set A of descriptors. Introduction  The Amino Acid Cysteine  Importance of SS Bonds  Machine Learning Statement of the Problem  Aim of Research  In Literature Our Proposed Solutions Results Comparisons with previous methods Conclusions Method B = 2B = 3B = 4B = 5Overall QcQc QpQp QcQc QpQp QcQc QpQp QcQc QpQp QcQc QpQp SVR (BSP)69 5946442345235650 NN (ELM)67 483644273765145