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Integration of Algorithmic and Local Approaches for Link Prediction: A Analysis on Protein-Protein Interactions and Social Networks

Year 2025, EARLY VIEW, 1 - 1

Hasibe Candan Kadem , Volkan Altuntaş

https://doi.org/10.2339/politeknik.1563133

Abstract

Complex network analysis is applied in various fields such as network-based systems, social media recommendation systems, shopping platforms, and treatment methodologies. In this context, predicting the probability of connection between two nodes has become a focal point. Another significant aspect is the prediction of connections between proteins, especially with the increase in epidemic diseases. Link prediction methods, based on graph structures, aim to predict interactions between two nodes and measure the probability of connection between them. These methods proceed by relying on similarity values and can have multiple approaches, including local, global, and algorithmic. This study has emerged from a combination of algorithmic and local network approaches. Support Vector Machines are employed to predict connections in gene-protein networks and social network structures. Data sets from multiple social media platforms and human protein-protein interaction (PPI) data were utilized. Derived data were created by calculating local index values, including the number of neighbors, Adamic Adar index, Jaccard coefficient, and label values for each node. To enhance success rates, a model was developed that applied the discretization method as a preprocessing technique across all data sets. Machine learning algorithms such as Bayesian Networks, Multilayer Perceptron (MLP), Random Forest, and k-Nearest Neighborhood (kNN) were compared and evaluated. The results indicate that the Twitch dataset, which has the highest number of edges, produced successful outcomes. The contribution of edge numbers in the network structure to performance is highlighted, and it is observed that more successful metric values were obtained for the data with applied discretization.

Keywords

Link Prediction Protein-Protein Interaction Social Networks Machine Learning Algorithms Biological Networks

References

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  • [2] Orman GK. “Discovering Link Prediction Methods' Performances by Network Topology Relation”. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 22(4), 778-788, (2022).
  • [3] Kösesoy İ, Gök M, Kahveci T. “Prediction of Host-Pathogen Protein Interactions by Extended Network Model”. Turkish Journal of Biology, 45(2), 138-148, (2021).
  • [4] Lei C, Ruan J. “A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity”. Bioinformatics, 29(3), 355-364, (2013).
  • [5] Kaya B. “Hotel recommendation system by bipartite networks and link prediction”. Journal of Information Science, 46(1), 53-63, (2020).
  • [6] Shabaz M, Garg U. “Predicting future diseases based on existing health status using link prediction”. World Journal of Engineering, (2021).
  • [7] Zareie A. “Sakellariou R. Similarity-based link prediction in social networks using latent relationships between the users”. Scientific Reports, 10(1), 1-11, (2020).
  • [8] Bandyopadhyay S, Chiang, CY. Srivastava J, Gersten M, “White S, Bell R, Ideker T, A human MAP kinase interactome”. Nature Methods, 7(10), 801-805, (2010).
  • [9] Kösesoy, İ, Gök M, Öz C. “A new sequence based encoding for prediction of host–pathogen protein interactions”. Computational Biology and Chemistry, 78, 170-177, (2019).
  • [10] Bisson, N, James, D. A, Ivosev G, Tate S. A, Bonner R, Taylor L, Pawson T. “Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor”. Nature Biotechnology, 29(7), 653-658, (2011).
  • [11] Martínez V, Berzal F, Cubero JC. “A survey of link prediction in complex networks”. ACM Computing Surveys (CSUR), 49(4), 1-33, (2016).
  • [12] Karaahmetoğlu, E, Ersöz, S, Türker, A. K., Ateş, V., İnal A. F. "Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey”. Politeknik Dergisi, 26(1), 107-124, 10.2339, (2023).
  • [13] Altuntas, V. “NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning”. Applied Sciences, 14(2), 775, (2024).
  • [14] Yücel, M., Osmanca, M. S. and Mercimek, İ. F. “Machine learning algorithm estimation and comparison of live network values of the inputs which have the most effect on the FEC parameter in DWDM systems”. Politeknik Dergisi, 1-1, (2024).
  • [15] Calp, M. H., & Bütüner, R. Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms. Politeknik Dergisi, 1-1, 1340515, (2024).
  • [16] Wang, M, Qiu L, Wang X. “A survey on knowledge graph embeddings for link prediction”. Symmetry, 13(3), 485, (2021).
  • [17] Baskar, P, Joseph, MA, Narayanan, N, Loya, RB. “Experimental investigation of oxygen enrichment on performance of twin cylinder diesel engine with variation of injection pressure”. In 2013 International Conference on Energy Efficient Technologies for Sustainability (pp. 682-687) IEEE. Nagercoil, India, (2013).
  • [18] Altuntas, V. “Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network”. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(2), 894-903, (2022).
  • [19] Zeng S. “Link prediction based on local information considering preferential attachment”. Physica A: Statistical Mechanics and its Applications, 443, 537-542, (2016).
  • [20] Deylami H. A. “Asadpour M. Link prediction in social networks using hierarchical community detection”. In 2015 7th Conference on Information and Knowledge Technology (IKT) (pp. 15). IEEE, Urmia, Iran, 26-28 May (2015).
  • [21] Rattigan M. J., Jensen D. “The case for anomalous link discovery”. ACM Sigkdd Explorations Newsletter, 7(2), 41-47, (2005).
  • [22] Qian F, Gao Y, Zhao S, Tang J, Zhang Y. “Combining topological properties and strong ties for link prediction”. Tsinghua Science and Technology, 22(6), 595-608, (2017).
  • [23] Kadem, O., Candan, H., and Kim, J. “Hybrid Deep Neural Network for Electric Vehicle State of Charge Estimation”. IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES) (pp. 1-6), (2024).
  • [24] Kadem O, Kim J. “Mitigation of state of charge estimation error due to noisy current input measurement”. Proceedings of the Institution of Mechanical Engineers, Part I Journal of Systems and Control Engineering, (2023).
  • [25] Kovács I.A., Luck K, Spirohn, K, Wang Y, Pollis, C, Schlabach S, Barabási A. L. “Network-based prediction of protein interactions”. Nature Communications, 10, 1-8, (2019).
  • [26] Kadem O. “Real-Time State of Charge Estimation Algorithm for Electrical Batteries”. PhD thesis, University of Leeds. https://etheses.whiterose.ac.uk/id/eprint/31973, (2022).
  • [27] Çakmak E, Kaya B, Kaya M. “İki Parçalı Ağda Bağlantı Tahminine Dayalı İlgi Çekici Nokta Tavsiyesi”. Computer Science, (Special), 154-161, (2021).
  • [28] Zhang M.L., Zhou Z.H. “ML-KNN: A lazy learning approach to multi-label learning”. Pattern Recognition, 40(7), 2038-2048, (2007).
  • [29] Martínez B, Cubero, Martínez V, Berzal F, Cubero JC. “A survey of link prediction in complex networks”, ACM Computing Surveys (CSUR), 49(4), (2017).
  • [30] Adamic, LA, & Adar E. “Friends and neighbors on the web”. Social Networks, 25, 211-230, (2023).
  • [31] Jaccard P. “The distribution of the flora in the alpine zone”. 1. New Phytologist, 11(2), 37-50, (1912).
  • [32] Clauset A, Moore C, Newman M.E. “Hierarchical structure and the prediction of missing links in networks”. Nature, 453(7191), 98-101, (2008).
  • [33] Jiang S, Xu K and Xiao J,” Link Prediction by Combining Local Structure Similarity with Node Behavior Synchronization”, IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3816-3825, (2024).
  • [34] Liu J, Li B and Dillon T, “An improved naive Bayesian classifier technique coupled with a novel input solution method [rainfall prediction]”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 31, no. 2, pp. 249-256, (2001).
  • [35] Baras D, Fine, S, Fournier L et al. “Automatic boosting of cross-product coverage using Bayesian networks”. Int J Softw Tools Technol Transfer 13, 247–261., (2011).
  • [36] Mathur A, Foody G.M. “Multiclass and binary SVM classification: Implications for training and classification users”. IEEE Geoscience and Remote Sensing Letters (5):241–245, (2008).
  • [37] Oshiro TM, Perez PS, Baranauskas JA. “How many trees in a Rotation Forest”. In International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 154-168), Springer, Berlin, Heidelberg, Germany, (2012).
  • [38] Vishwakarma, M., Kesswani, N. “A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection”. Decision Analytics Journal, 7, 100233, (2023).
  • [39] Pal M. “Random forest classifier for remote sensing classification”. International Journal of Remote Sensing, 26(1), 217-222, (2005).
  • [40] Fayyad U, Irani K. “Multi-interval discretization of continuous-valued attributes for classification learning”, International Joint Conference on Artificial Intelligence, (1993).
  • [41] Ayo, F. E., Folorunso, O., Ibharalu, F. T., & Osinuga, I. A. “Machine learning techniques for hate speech classification of twitter data: State-of-the-art, future challenges and research directions”. Computer Science Review, 38, 100311, (2020).
  • [42] LeCun Y, Jackel L, Bottou L, Brunot A, Cortes C, Denker J, Vapnik V. “Comparison of learning algorithms for handwritten digit recognition”. In International conference on artificial neural networks (Vol. 60, No. 1, pp. 53-60), Australia, (1995).
  • [43] Sharma V, Yadav S, Gupta M, “Heart Disease Prediction using Machine Learning Techniques”. 2nd Int. Conf. Adv. Comput. Commun. Control Networking, IEEE, ICACCCCN, Greater Noida, India, (2020).
  • [44] Katarya R, Meena S.K.,” Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis”, Health Technol. vol. 11, no. 1, pp. 87–97, 10.1007/s12553-020-00505-7, (2021).
  • [45] Mijwil M.M., Abttan R.A. “Utilizing the genetic algorithm to pruning the C4. 5 decision tree algorithm”. Asian Journal of Applied Sciences, 9(1), (2021).
  • [46] Valero-Carreras, D., Alcaraz, J., & Landete, M., “Comparing two SVM models through different metrics based on the confusion matrix”. Computers & Operations Research, 152, 106131, (2023).
  • [47] Leskovec, J., Sosič, R. “Snap: A general-purpose network analysis and graph-mining library”. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1), 1-20, (2016).

Bağlantı Tahmini için Algoritmik ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri ve Sosyal Ağlar Üzerine Bir Analiz

Year 2025, EARLY VIEW, 1 - 1

Hasibe Candan Kadem , Volkan Altuntaş

https://doi.org/10.2339/politeknik.1563133

Abstract

Karmaşık ağ analizi, ağ tabanlı sistemler, sosyal medya öneri sistemleri, alışveriş platformları ve tedavi metodları gibi çeşitli alanlarda uygulanmaktadır. Bu bağlamda, iki düğüm arasındaki bağlantı olasılığını öngörmek odak noktası haline gelmiştir. Özellikle salgın hastalıklardaki artışla birlikte, proteinler arasındaki bağlantıların tahmin edilmesi önemli bir konudur. Graf yapılarına dayalı olan bağlantı tahmini yöntemleri, iki düğüm arasındaki etkileşimleri tahmin etmeyi ve bunlar arasındaki bağlantı olasılığını ölçmeyi amaçlar. Bu yöntemler, benzerlik değerlerine dayanarak ilerler ve yerel, global ve algoritmik gibi çeşitli yaklaşımlara sahip olabilir. Bu çalışma, algoritmik ve yerel ağ yaklaşımlarının bir kombinasyonundan ortaya çıkmıştır. Gen-protein ağları ve sosyal ağ yapılarında bağlantıları tahmin etmek için Destek Vektör Makineleri kullanılmıştır. Birden çok sosyal medya platformundan ve insan protein-protein etkileşimi (PPI) verilerinden elde edilen veri setleri kullanılmıştır. Her düğüm için komşu sayısı, Adamic Adar endeksi, Jaccard katsayısı ve etiket değerleri de dahil olmak üzere yerel indeks değerlerini hesaplayarak türetilen veriler oluşturulmuştur. Başarı oranlarını artırmak için, bir model, tüm veri setlerinde ön işleme tekniği olarak kesikli yöntemi uygulayan bir model geliştirilmiştir. Bayesian Ağları, Çok Katmanlı Algılayıcı (MLP), Rastgele Orman ve k-En Yakın Komşuluk (kNN) gibi makine öğrenimi algoritmaları karşılaştırılmış ve değerlendirilmiştir. Sonuçlar, en yüksek kenar sayısına sahip olan Twitch veri setinin başarılı sonuçlar verdiğini göstermektedir. Ağ yapısındaki kenar sayısının performansa katkısı vurgulanmış ve kesikli yöntemin uygulandığı veriler için daha başarılı metrik değerler elde edildiği gözlemlenmiştir.

Keywords

Bağlantı Tahmini Protein-Protein Etkileşimi Sosyal Ağlar Makine Öğrenme Algoritmaları Biyolojik Ağlar

References

  • [1] Altuntas, V., Gok, M., & Kocal, O. H. “Response of Lyapunov exponents to diffusion state of biological networks”. International Journal of Applied Mathematics and Computer Science 30(4), 689-702, (2020).
  • [2] Orman GK. “Discovering Link Prediction Methods' Performances by Network Topology Relation”. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 22(4), 778-788, (2022).
  • [3] Kösesoy İ, Gök M, Kahveci T. “Prediction of Host-Pathogen Protein Interactions by Extended Network Model”. Turkish Journal of Biology, 45(2), 138-148, (2021).
  • [4] Lei C, Ruan J. “A novel link prediction algorithm for reconstructing protein–protein interaction networks by topological similarity”. Bioinformatics, 29(3), 355-364, (2013).
  • [5] Kaya B. “Hotel recommendation system by bipartite networks and link prediction”. Journal of Information Science, 46(1), 53-63, (2020).
  • [6] Shabaz M, Garg U. “Predicting future diseases based on existing health status using link prediction”. World Journal of Engineering, (2021).
  • [7] Zareie A. “Sakellariou R. Similarity-based link prediction in social networks using latent relationships between the users”. Scientific Reports, 10(1), 1-11, (2020).
  • [8] Bandyopadhyay S, Chiang, CY. Srivastava J, Gersten M, “White S, Bell R, Ideker T, A human MAP kinase interactome”. Nature Methods, 7(10), 801-805, (2010).
  • [9] Kösesoy, İ, Gök M, Öz C. “A new sequence based encoding for prediction of host–pathogen protein interactions”. Computational Biology and Chemistry, 78, 170-177, (2019).
  • [10] Bisson, N, James, D. A, Ivosev G, Tate S. A, Bonner R, Taylor L, Pawson T. “Selected reaction monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor”. Nature Biotechnology, 29(7), 653-658, (2011).
  • [11] Martínez V, Berzal F, Cubero JC. “A survey of link prediction in complex networks”. ACM Computing Surveys (CSUR), 49(4), 1-33, (2016).
  • [12] Karaahmetoğlu, E, Ersöz, S, Türker, A. K., Ateş, V., İnal A. F. "Evaluation of Profession Predictions for Today and the Future with Machine Learning Methods: Emperical Evidence From Turkey”. Politeknik Dergisi, 26(1), 107-124, 10.2339, (2023).
  • [13] Altuntas, V. “NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning”. Applied Sciences, 14(2), 775, (2024).
  • [14] Yücel, M., Osmanca, M. S. and Mercimek, İ. F. “Machine learning algorithm estimation and comparison of live network values of the inputs which have the most effect on the FEC parameter in DWDM systems”. Politeknik Dergisi, 1-1, (2024).
  • [15] Calp, M. H., & Bütüner, R. Detecting the Cyber Attacks on IoT-Based Network Devices Using Machine Learning Algorithms. Politeknik Dergisi, 1-1, 1340515, (2024).
  • [16] Wang, M, Qiu L, Wang X. “A survey on knowledge graph embeddings for link prediction”. Symmetry, 13(3), 485, (2021).
  • [17] Baskar, P, Joseph, MA, Narayanan, N, Loya, RB. “Experimental investigation of oxygen enrichment on performance of twin cylinder diesel engine with variation of injection pressure”. In 2013 International Conference on Energy Efficient Technologies for Sustainability (pp. 682-687) IEEE. Nagercoil, India, (2013).
  • [18] Altuntas, V. “Diffusion Alignment Coefficient (DAC): A Novel Similarity Metric for Protein-Protein Interaction Network”. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(2), 894-903, (2022).
  • [19] Zeng S. “Link prediction based on local information considering preferential attachment”. Physica A: Statistical Mechanics and its Applications, 443, 537-542, (2016).
  • [20] Deylami H. A. “Asadpour M. Link prediction in social networks using hierarchical community detection”. In 2015 7th Conference on Information and Knowledge Technology (IKT) (pp. 15). IEEE, Urmia, Iran, 26-28 May (2015).
  • [21] Rattigan M. J., Jensen D. “The case for anomalous link discovery”. ACM Sigkdd Explorations Newsletter, 7(2), 41-47, (2005).
  • [22] Qian F, Gao Y, Zhao S, Tang J, Zhang Y. “Combining topological properties and strong ties for link prediction”. Tsinghua Science and Technology, 22(6), 595-608, (2017).
  • [23] Kadem, O., Candan, H., and Kim, J. “Hybrid Deep Neural Network for Electric Vehicle State of Charge Estimation”. IEEE 3rd International Conference on Electrical Power and Energy Systems (ICEPES) (pp. 1-6), (2024).
  • [24] Kadem O, Kim J. “Mitigation of state of charge estimation error due to noisy current input measurement”. Proceedings of the Institution of Mechanical Engineers, Part I Journal of Systems and Control Engineering, (2023).
  • [25] Kovács I.A., Luck K, Spirohn, K, Wang Y, Pollis, C, Schlabach S, Barabási A. L. “Network-based prediction of protein interactions”. Nature Communications, 10, 1-8, (2019).
  • [26] Kadem O. “Real-Time State of Charge Estimation Algorithm for Electrical Batteries”. PhD thesis, University of Leeds. https://etheses.whiterose.ac.uk/id/eprint/31973, (2022).
  • [27] Çakmak E, Kaya B, Kaya M. “İki Parçalı Ağda Bağlantı Tahminine Dayalı İlgi Çekici Nokta Tavsiyesi”. Computer Science, (Special), 154-161, (2021).
  • [28] Zhang M.L., Zhou Z.H. “ML-KNN: A lazy learning approach to multi-label learning”. Pattern Recognition, 40(7), 2038-2048, (2007).
  • [29] Martínez B, Cubero, Martínez V, Berzal F, Cubero JC. “A survey of link prediction in complex networks”, ACM Computing Surveys (CSUR), 49(4), (2017).
  • [30] Adamic, LA, & Adar E. “Friends and neighbors on the web”. Social Networks, 25, 211-230, (2023).
  • [31] Jaccard P. “The distribution of the flora in the alpine zone”. 1. New Phytologist, 11(2), 37-50, (1912).
  • [32] Clauset A, Moore C, Newman M.E. “Hierarchical structure and the prediction of missing links in networks”. Nature, 453(7191), 98-101, (2008).
  • [33] Jiang S, Xu K and Xiao J,” Link Prediction by Combining Local Structure Similarity with Node Behavior Synchronization”, IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3816-3825, (2024).
  • [34] Liu J, Li B and Dillon T, “An improved naive Bayesian classifier technique coupled with a novel input solution method [rainfall prediction]”, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 31, no. 2, pp. 249-256, (2001).
  • [35] Baras D, Fine, S, Fournier L et al. “Automatic boosting of cross-product coverage using Bayesian networks”. Int J Softw Tools Technol Transfer 13, 247–261., (2011).
  • [36] Mathur A, Foody G.M. “Multiclass and binary SVM classification: Implications for training and classification users”. IEEE Geoscience and Remote Sensing Letters (5):241–245, (2008).
  • [37] Oshiro TM, Perez PS, Baranauskas JA. “How many trees in a Rotation Forest”. In International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 154-168), Springer, Berlin, Heidelberg, Germany, (2012).
  • [38] Vishwakarma, M., Kesswani, N. “A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection”. Decision Analytics Journal, 7, 100233, (2023).
  • [39] Pal M. “Random forest classifier for remote sensing classification”. International Journal of Remote Sensing, 26(1), 217-222, (2005).
  • [40] Fayyad U, Irani K. “Multi-interval discretization of continuous-valued attributes for classification learning”, International Joint Conference on Artificial Intelligence, (1993).
  • [41] Ayo, F. E., Folorunso, O., Ibharalu, F. T., & Osinuga, I. A. “Machine learning techniques for hate speech classification of twitter data: State-of-the-art, future challenges and research directions”. Computer Science Review, 38, 100311, (2020).
  • [42] LeCun Y, Jackel L, Bottou L, Brunot A, Cortes C, Denker J, Vapnik V. “Comparison of learning algorithms for handwritten digit recognition”. In International conference on artificial neural networks (Vol. 60, No. 1, pp. 53-60), Australia, (1995).
  • [43] Sharma V, Yadav S, Gupta M, “Heart Disease Prediction using Machine Learning Techniques”. 2nd Int. Conf. Adv. Comput. Commun. Control Networking, IEEE, ICACCCCN, Greater Noida, India, (2020).
  • [44] Katarya R, Meena S.K.,” Machine Learning Techniques for Heart Disease Prediction: A Comparative Study and Analysis”, Health Technol. vol. 11, no. 1, pp. 87–97, 10.1007/s12553-020-00505-7, (2021).
  • [45] Mijwil M.M., Abttan R.A. “Utilizing the genetic algorithm to pruning the C4. 5 decision tree algorithm”. Asian Journal of Applied Sciences, 9(1), (2021).
  • [46] Valero-Carreras, D., Alcaraz, J., & Landete, M., “Comparing two SVM models through different metrics based on the confusion matrix”. Computers & Operations Research, 152, 106131, (2023).
  • [47] Leskovec, J., Sosič, R. “Snap: A general-purpose network analysis and graph-mining library”. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1), 1-20, (2016).
There are 47 citations in total.

Details

Primary Language Turkish
Subjects Machine Learning (Other)
Journal Section Research Article
Authors

Hasibe Candan Kadem 0000-0001-5722-0811

Volkan Altuntaş 0000-0003-3144-8724

Early Pub Date April 12, 2025
Publication Date
Submission Date October 8, 2024
Acceptance Date March 23, 2025
Published in Issue Year 2025 EARLY VIEW

Cite

APA Candan Kadem, H., & Altuntaş, V. (2025). Bağlantı Tahmini için Algoritmik ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri ve Sosyal Ağlar Üzerine Bir Analiz. Politeknik Dergisi1-1. https://doi.org/10.2339/politeknik.1563133
AMA Candan Kadem H, Altuntaş V. Bağlantı Tahmini için Algoritmik ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri ve Sosyal Ağlar Üzerine Bir Analiz. Politeknik Dergisi. Published online April 1, 2025:1-1. doi:10.2339/politeknik.1563133
Chicago Candan Kadem, Hasibe, and Volkan Altuntaş. “Bağlantı Tahmini için Algoritmik Ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri Ve Sosyal Ağlar Üzerine Bir Analiz”. Politeknik Dergisi, April (April 2025), 1-1. https://doi.org/10.2339/politeknik.1563133.
EndNote Candan Kadem H, Altuntaş V (April 1, 2025) Bağlantı Tahmini için Algoritmik ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri ve Sosyal Ağlar Üzerine Bir Analiz. Politeknik Dergisi 1–1.
IEEE H. Candan Kadem and V. Altuntaş, “Bağlantı Tahmini için Algoritmik ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri ve Sosyal Ağlar Üzerine Bir Analiz”, Politeknik Dergisi, pp. 1–1, April 2025, doi: 10.2339/politeknik.1563133.
ISNAD Candan Kadem, Hasibe - Altuntaş, Volkan. “Bağlantı Tahmini için Algoritmik Ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri Ve Sosyal Ağlar Üzerine Bir Analiz”. Politeknik Dergisi. April 2025. 1-1. https://doi.org/10.2339/politeknik.1563133.
JAMA Candan Kadem H, Altuntaş V. Bağlantı Tahmini için Algoritmik ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri ve Sosyal Ağlar Üzerine Bir Analiz. Politeknik Dergisi. 2025;:1–1.
MLA Candan Kadem, Hasibe and Volkan Altuntaş. “Bağlantı Tahmini için Algoritmik Ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri Ve Sosyal Ağlar Üzerine Bir Analiz”. Politeknik Dergisi, 2025, pp. 1-1, doi:10.2339/politeknik.1563133.
Vancouver Candan Kadem H, Altuntaş V. Bağlantı Tahmini için Algoritmik ve Yerel Yaklaşımların Entegrasyonu: Protein-Protein Etkileşimleri ve Sosyal Ağlar Üzerine Bir Analiz. Politeknik Dergisi. 2025:1-.

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