Reecha Khanal Mentor: Avdesh Mishra Supervisor: Dr. Md Tamjidul Hoque

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Presentation transcript:

Effective Prediction of RNA-Binding Proteins Using Machine Learning Techniques Reecha Khanal Mentor: Avdesh Mishra Supervisor: Dr. Md Tamjidul Hoque University of New Orleans Department of Computer Science 4/24/2019

Presentation Overview Brief Background Objective Dataset Collection Feature Extraction Methods Performance Evaluation Conclusions 4/24/2019

Brief Background Amino Acids: Organic Compounds with key elements of carbon(C), hydrogen(H), oxygen(O), and Nitrogen(N) 20 Standard Amino Acids Proteins: Sequence of Amino Acids 4/24/2019

Brief Background Amino Acids: Organic Compounds with key elements of carbon(C), hydrogen(H), oxygen(O), and Nitrogen(N) 20 Standard Amino Acids Proteins: Sequence of Amino Acids 4/24/2019

Objective To develop a computational approach for prediction of RNA Binding Proteins (RBP) from Non-RNA Binding Proteins (NRBP). 4/24/2019

Why RNA Binding Protein Prediction? RNA Binding Proteins are important in many biological functions: mRNA stability Stress response Cell cycle Tumor Differentiation RNA Binding Proteins have been linked to some critical diseases: Cancer Neuro-degenerative diseases Immunological disorders Muscular atrophies and Metabolic disorders 4/24/2019

NON-RBP Protein Chains Data Collection PISCES UniProt Database Sequence Identity : 25% 68084 RBPs X-ray resolution: 3Å Sequence Length: 50 – 10,000 amino acids 14389 NON-RBP Protein Chains 4/24/2019

Data Collection CD-HIT: Sequence Identity Cutoff >= 25% 14389 Protein Chains 68084 RBPs CD-HIT: Sequence Identity Cutoff >= 25% Protein Length: 50 – 10,000 amino acids 7077 NonRBPs 2770 RBPs 4/24/2019

Feature Extraction 4/24/2019

Composition, Transition, and Distribution (C-T-D) 4/24/2019

Feature Set 1 … 2602 2603 12.496 0.579 97.89 13.587 0.6786 96.65 . 8.78678 0.123123 56.87 7.78927 0.061447 60.761 4/24/2019

Incremental Feature Selection Removed 21 features 2582 features were selected out of 2603 features Incremental Feature Selection Removed 1257 features 1346 features were selected out of 2603 features Genetic Algorithm 4/24/2019

Machine Learning 4/24/2019

Machine Learning Approaches Comparison of various machine learning algorithms on the benchmark dataset through 10-fold CV. Metric/ Methods Bagging K Nearest Neighbours Logistic Regression Random Forest Extreme Gradient Boosting Classifier (XGBC) Extremely Randomized Trees (ET) Sensitivity (%) 82.18 57.54 82.00 72.24 89.09 67.44 Specificity (%) 96.84 89.17 96.39 98.47 97.48 98.58 Balanced Accuracy (%) 89.51 73.35 89.20 85.36 93.28 83.01 Accuracy (%) 92.68 80.19 92.31 91.03 95.10 89.75 False Positive Rate 0.032 0.108 0.036 0.015 0.025 0.014 False Negative Rate 0.178 0.425 0.180 0.278 0.109 0.326 Precision (%) 91.14 67.77 90.00 94.92 93.34 94.96 F1-score 0.866 0.622 0.858 0.820 0.912 0.789 MCC 0.816 0.492 0.807 0.775 0.878 0.742 4/24/2019

Stacking: 4/24/2019

Stacking Models SM1: RDF, XGBC, LogReg, KNN in base-level and XGBoost in meta- level, SM2: Bag, XGBC, LogReg, KNN in base-level and XGBoost in meta-level and SM3: ET, XGBC, LogReg, KNN in base-level and XGBoost in meta-level. 4/24/2019

Performance Comparison of Stacked Models Metric/Methods SM1 SM2 SM3 Sensitivity (%) 90.17 89.99 90.53 Specificity (%) 97.44 97.15 97.29 Balanced Accuracy (%) 93.80 93.57 93.91 Overall Accuracy (%) 95.38 95.12 False Positive Rate 0.026 0.028 0.027 False Negative Rate 0.098 0.100 0.095 Precision (%) 93.31 92.59 92.98 F1-score 0.917 0.912 MCC 0.885 0.879 4/24/2019

Stacking: 4/24/2019

FrameWork: 4/24/2019

Performance Comparison Metric/Methods RBPPred AIRBP (% imp.) Sensitivity (%) 83.07 90.17 (8.55%) Specificity (%) 96.00 97.44 (1.50%) Balanced Accuracy(%) - 93.80 (-) Overall Accuracy (%) 92.36 95.38 (3.26%) False Positive Rate 0.026 (-) False Negative Rate 0.098 (-) Precision (%) 89.00 93.31 (4.84%) F1-score 0.859 0.917 (6.75%) MCC 0.808 0.885 (9.53%) 4/24/2019

Performance Comparison on Test Dataset Methods Dataset Evaluation Metrics Sensitivity (%) Specificity (%) Balanced Accuracy (%) Overall Accuracy (%) FPR FNR Precision (%) F1-score MCC RBPPred Human 84.28 96.65 - 89.00 97.65 0.905 0.788 S. cerevisiae 86.16 94.59 87.73 96.52 0.910 0.729 A. thaliana 86.40 87.02 0.925 0.537 AIRBP (% imp.) 92.14 (9.32%) 94.52 (-2.21%) 93.33 (-) 93.04 (4.54%) 0.055 0.079 96.53 (-1.14%) 0.943 (4.19%) 0.855 (8.50%) 94.35 (9.51%) 84.33 (-10.85%) 89.34 91.60 (4.41%) 0.157 0.057 94.09 (-2.52%) 0.942 (3.52%) 0.789 (8.23%) 92.11 (6.61%) 86.11 (-8.97%) 89.11 91.67 (5.34%) 0.139 98.82 (4.28%) 0.953 (3.03%) 0.594 (10.61%) (avg. % imp.) (8.48%) (-7.34%) (4.76) (0.21%) (3.58%) (9.11%)

4/24/2019