作者: Ekaterina Kotelnikova , Anton Yuryev , Sergei Maslov , Andrey Kalinin
DOI: 10.4137/EBO.S0
关键词: Dependency (UML) 、 Similarity (network science) 、 Probabilistic logic 、 Computational biology 、 Data science 、 Protein–protein interaction 、 Genomics 、 Sequence 、 Protein superfamily 、 Computer science 、 Probabilistic method
摘要: Motivation: Although a great deal of progress is being made in the development fast and reliable experimental techniques to extract genome-wide networks protein-protein protein-DNA interactions, sequencing new genomes proceeds at an even faster rate. That why there considerable need for methods in-silico prediction protein interaction based solely on sequence similarity information known interactions from well-studied organisms. This problem can be solved if dependency exists between conservation proteins’ functions. Results: In this paper, we introduce novel probabilistic method using empirical formula describing loss homologous proteins during course evolution. describes evolutional process quite similar Earth’s population growth. addition, our favors predictions confi rmed by several interacting pairs over coming single pair. Our approach useful working with “noisy” data such as those high-throughput experiments. We have generated fi ve “model” organisms: H. sapiens, D. melanogaster, C. elegans, A. thaliana, S. cerevisiae evaluated quality these predictions.