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Disulfide Connectivity Prediction Using Machine Learning

This thesis explores the accurate prediction of disulfide connectivity patterns in bioinformatics through machine learning approaches, comparing various methods and presenting innovative solutions.

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Disulfide Connectivity Prediction Using Machine Learning

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

  2. In Literature • 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 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh

  3. Our Proposed Solutions • 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 1 2 Machine Learning 3 4 Pattern Scoring Schemes Basic System Design M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh

  4. Our Proposed Solutions • 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 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh

  5. SLFN • 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 Number of Neurons Number of Neurons Additive Hidden Neurons RBF Hidden Neurons M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh

  6. ELM outperforms BP • 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 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh

  7. SVR vs. NN • Comparison of SVR and NN-based methods • Both tested on PDB0909 with Set Aof 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 M.Sc. Thesis in Bioinformatics Eng. Monther Alhamdoosh

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