Akademik Veri Yönetim Sistemi
16th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2025, Pennsylvania, Amerika Birleşik Devletleri, 12 - 15 Ekim 2025, (Tam Metin Bildiri)
Biological networks are dynamic structures, continuously evolving by rewiring their interactions. These rewirings happen at different rates for different cells, and the rates can change over time, yet we can only observe the cell at a limited number of stages of their evolution, limiting the number of possible observed gene networks. In this paper, we consider the problem of predicting entire gene networks of dynamic biological networks. We develop a novel algorithm PLATO (Predicting Longitudinally-Aligned Time Observations), which utilizes dynamic network alignment that maps multiple systems of networks to improve the prediction accuracy of the matrix factorization model. We evaluate our method on gene-gene interaction networks using a mouse model with evolutionary patterns caused by chronic myeloid leukemia (CML) and compare it to four existing state of the art methods, including two deep learning and one matrix factorization techniques. Our experimental results demonstrate that PLATO outperforms both traditional matrix factorization and other competing methods in terms of gene-gene interaction prediction accuracy.