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</script>AbstractComplex phenotypes emerge from the interactions of thousands of macromolecules that are organized in multimolecular complexes and interacting functional modules. In turn, modules form functional networks in health and disease. Omics approaches collect data on changes for all genes and proteins and statistical analysis attempts to uncover the functional modules that perform the functions that characterize higher levels of biological organization. Systems biology attempts to transcend the study of individual genes/proteins and to integrate them into higher order information. Cancer cells exhibit defective genetic and epigenetic networks formed by altered complexes and network modules arising in different parts of tumor tissues that sustain autonomous cell behavior which ultimately lead tumor growth. We suggest that an understanding of tumor behavior must address not only molecular but also, and more importantly, tumor cell heterogeneity, by considering cancer tissue genetic and epigenetic networks, by characterizing changes in the types, composition, and interactions of complexes and networks in the different parts of tumor tissues, and by identifying critical hubs that connect them in time and space.
Genome, Systems Biology, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Computational Biology, Cell cycle, Neoplasm Proteins, Neoplasms, network, module, Humans, cyclin‐dependent kinase inhibitors signaling, complex, RC254-282, Algorithms, cyclin‐dependent kinase, Cancer Biology, Signal Transduction
Genome, Systems Biology, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Computational Biology, Cell cycle, Neoplasm Proteins, Neoplasms, network, module, Humans, cyclin‐dependent kinase inhibitors signaling, complex, RC254-282, Algorithms, cyclin‐dependent kinase, Cancer Biology, Signal Transduction
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 37 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
