الفهرس | Only 14 pages are availabe for public view |
Abstract Discovering possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods to predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. This thesis aims to predict drug interactions in human cells with the most accurate performance using machine learning techniques. First, DTIs prediction model is developed to take advantage of the structured form of proteins and drugs. This model obtains features from protein amino-acid sequences, and from drugs SMILES (Simplified Molecular Input Line Entry System) strings using encoding techniques. Empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data. Second scheme predicting DTIs based on drug chemical structures and protein sequences are employ to extract the drugs and protein’s characteristics. Then, proposed approach discovering negative samples using a support vector machine one- class classifier to tackle the imbalanced data problem. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTls. Third, Network-based approach is developed utilizing feature representation and deep learning. This approach extracts the relevant features of drugs and proteins from heterogeneous networks using graph mining techniques, by constructing a heterogeneous graph of known drug-protein interactions, protein-protein, and drug-drug similarities. Then applying two feature extraction techniques, the first utilizes the cosine similarity coefficient and random walk with restart model, and the second involves a similarity selection procedure and a similarity fusion algorithm. Four Benchmark datasets are used to evaluate the proposed approach. Finally, the hierarchical network embedding methods, and the graph autoencoder (GAE) scheme developed to extract the embedding feature vectors of drugs and numbered targets from multi-source heterogeneous networks to predict DTIs. In comparison with literature methods, the achieved results demonstrate the validity and effectiveness of the proposed work for drug target interactions and improvement of the drug repositions |