2018/07/15 by Khalique Newaz, Newaz, Khalique, Tijana Milenković +1
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #FOS: Biological sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Molecular Networks (q-bio.MN)
paper · pdf · doi:10.48550/arxiv.1807.05637
openalex publication_date 2018/07/15 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
Gene expression (GE) data capture valuable condition-specific information\n("condition" can mean a biological process, disease stage, age, patient, etc.)\nHowever, GE analyses ignore physical interactions between gene products, i.e.,\nproteins. Since proteins function by interacting with each other, and since\nbiological networks (BNs) capture these interactions, BN analyses are\npromising. However, current BN data fail to capture condition-specific\ninformation. Recently, GE and BN data have been integrated using network\npropagation (NP) to infer condition-specific BNs. However, existing NP-based\nstudies result in a static condition-specific subnetwork, even though cellular\nprocesses are dynamic. A dynamic process of our interest is human aging. We use\nprominent existing NP methods in a new task of inferring a dynamic rather than\nstatic condition-specific (aging-related) subnetwork. Then, we study evolution\nof network structure with age - we identify proteins whose network positions\nsignificantly change with age and predict them as new aging-related candidates.\nWe validate the predictions via e.g., functional enrichment analyses and\nliterature search. Dynamic network inference via NP yields higher prediction\nquality than the only existing method for inferring a dynamic aging-related BN,\nwhich does not use NP.\n