New technologies and methodologies in biological systems make the measurement of more characteristics of cellular components such as genes , proteins , transcripts and metabolites possible . These components have essential interactions among each other. A comprehensive evaluation of functions and communications of each cellular component in systems biology is called omics, the most important of which are layers of genomics , proteomics , transcriptomics and metabolomics . These omics layers are related to each other by fundamental interactions. So far, due to the extent and complexity of biological systems , the investigations have been focused on one of the layers while the interactions between those layers have not been taken into account. Results of studying one omics layer describe a limited part of a biological system , however, many diseases originate as a result of interactions in broad and complex molecular network . Therefore, the interactions of omics and effects of omics layers on each other should also be considered in biological systems investigations. The integration of omics data would lead to a better understanding of functions of cellular components , cause of disease and identification of drug targets . In this thesis, omics data has been integrated and a computational method has been developed for predicting interactions between omics layers in multi-layer interaction networks. With the development of various forms of matrix factorization methods and the emergence of deep learning , the integration of information and data of the omics layers as well as the creation a multi-layer network of omics interactions has been made possible . In this thesis , miRNAs and proteomics data in Diabetic nephropathy collected from real experiments has been used . In order to predict the interactions between proteins and miRNAs , a generalized non-negative matrix tri-factorization method has been proposed . The proposed model has employed methods on the interaction matrix of these layers . This model is not only reduces large size and complexity of data , but also reveals latent components of data . Then , protein interactions and gene ontology data has been integrated to analyze effects of data integration. Because of the dynamic nature of biological phenomena and the probabilistic characteristics of experimental measurements , integration of data limits model performance. Then, deep matrix factorization method has been used which improved model performance. In this method , two deep neural networks for proteomics and miRNAs are used to find the best representation vectors for proteins and miRNAs . Also , employing a decoder has improved model performance . By modifying inputs or adding neural networks , a generalization of deep matrix factorization method has been proposed to integrate more information in the model as well as making the modeling of more than two heterogeneous object types possible . The problem of over fitting in deep neural networks has been solved by techniques such as dropout , regularization , and early stopping . It also has been suggested to use the singular value decomposition technique to determine the dimensions of the representation vectors . To evaluate the performance of the methods , two sets of colon cancer data and Gene ontology of the genes have been modeled . In this thesis, a novel method of modeling multi-layer interaction network has been proposed to investigate omics layers together, which will help to predict more appropriate drug targets in the future. Link prediction , Matrix factorization , Deep matrix factorization , Multi-layer interaction network , Omics integration , Data integration