A computational approach inspired by simulated annealing to study the stability of protein interaction networks in cancer and neurological disorders

作者：Ibanez Kristina^*; Guijarro Maria; Pajares Gonzalo; Valencia Alfonso^*

来源：Data Mining and Knowledge Discovery, 2016, 30(1): 226-242.

DOI：10.1007/s10618-015-0410-5

• Simulated annealing
• Systems biology
• Gene expression
• Protein interaction networks
• Cancer
• Neurological disorders
• Inverse comorbidity

Molecular networks provide a powerful tool for the study of biomedical systems, in particular several studies have detected alterations of the network structure associated to disease states. Here we propose that diseases cannot only alter the structure of the network but also its stability. To evaluate network stability we have developed a new methodological framework. Our approach is an adaptation of the classical Deterministic Simulated Annealing algorithm to work with discrete states. Adjusted energy values are used to compare the network stability in disease and control states. The results show that cancer networks are less stable than the Alzheimer's disease (AD) ones. These results can be interpreted in terms of our previous observations on cancer and AD inverse comorbidity, i.e. AD patients have lower than expected risk to suffer cancer.

• 出版日期2016-1