Protein networks that mirror the transitions between disease stages hold the key to early diagnosis and make it easy to understand the essential mechanisms of disease progression at protein network level. But, identifying critical transitions between disease stages and corresponding protein networks during the initiation and progression of a complex disease like cancer is a challenging task. This preliminary work identifies the possible building blocks for disease initiation and progression at the protein network level based on biological rationale that a group of proteins are localized at a specific subcellular location to accomplish a function, which could be beneficial to human body or adversarial to cause a disease. We discovered that three graph-theoretic concepts - i) Clique-like structures, ii) Bipartite-like structures, and iii) Diffusion Kernels could be possible building blocks for disease progression at the protein network level. Using these building blocks, disease progression can be modeled as an event-schedule-like structure, meaning that each of the disease stages corresponds to an event, where each event is completed by a set of proteins by forming a clique-like structure. Once an event or disease stage is completed by a group of proteins, disease signals go to the next group of proteins to cause the next event or disease stage and so on. The transfer of signals can be represented by bipartite-like structure and diffusion kernels can be used to find the strength of disease signals. Further study is required to fully explore the application of these building blocks to analyze the disease progression.
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