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Yayın Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses(Elsevier Ltd, 2022-08-13) Koca, Mehmet Burak; Nourani, Esmaeil; Abbasoğlu, Ferda; Karadeniz, İlknur; Sevilgen, Fatih ErdoğanComputational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of path-ogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3–23% better area under curve (AUC) score than its competitors.Yayın Investigating effects of milling conditions on cutting temperatures through analytical and experimental methods(Elsevier Science SA, 2018-12) Karagüzel, Umut; Budak, ErhanCutting temperatures in milling operations have a significant impact on tool wear, size and shape tolerances and residual stresses of the machined part. Prediction and measurement of cutting temperatures in milling, on the other hand, have some challenges due to the rotary tools resulting in an intermittent process and transient thermal loadings. In this study, novel approaches are presented to model and measure the cutting tool temperature variations during milling. The model is used to predict effects of milling conditions on cutting temperatures particularly to determine a relationship between tool temperature and radial depth of cut. The model predictions are verified by measurements obtained from the developed measurement technique and the literature data.Yayın Transient multi-domain thermal modeling of interrupted cutting with coated tools(Springer Science and Business Media Deutschland GmbH, 2021-09) Karagüzel, UmutInterrupted cutting operations, such as milling, produce fluctuating tool temperatures which directly affect the process outputs. Thus, prediction of cutting tool temperatures enables process planning, selection of materials for tool substrate and coating layers, and tool geometric design for improved productivity in machining operations. Theoretical analysis of temperature is a cost effective way to predict the tool temperatures. Considering the industrial needs, a theoretical model should be fast, easy to implement, and reliable. To that end, a novel hybrid model, which assembles analytical and numerical methods, is proposed in this study. This novel transient thermal model simulates the interrupted cutting with coated cutting tools. The proposed model includes an analytical heat flux calculation at the tool-chip interface considering the sticking-sliding contact behavior. The determined heat flux is, then, used to perform a numerical solution of the transient heat conduction problem in the cutting tool geometry with temperature-dependent thermal properties. The developed model is validated with experimental results found in literature under different cutting conditions. The results show that the model can predict the maximum temperatures generated in a thermal cycle with an accuracy of 2–10%. Thus, the proposed model can be further used to determine the process parameters, properties of coating layers, and tool geometric design.
