Such an ontology model may also serve as the basis for an intelligent, predictive agent, as one of us has described elsewhere (Carvunis and Ideker, 2014).
The therapeutic landscape of Non Small Lung Cancer (NSCLC) has been profoundly changed over the last decade with the clinical introduction of Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) and the discovery of EGFR activating mutations as the major predictive factor to these agents.
Based on this predictive information, the agent develops prior ownership from time point 6 on and with peak value 0.7.
One current approach involves modelling germ line and somatic markers of risk and predictive markers of agent benefit or toxicity such that it may be possible to personalise cancer prevention.
The true goal for these studies is to identify predictive biomarkers for specific agents, which requires randomisation against other agents.
Overall, our findings suggest that mutational burden may be a good predictive biomarker for immunomodulatory agents in melanoma.
Using social CRM and advanced tools with predictive analytics, social media agents can move beyond issue resolution to provide tailored recommendations.
Nevertheless, the EPM is accepted to have predictive validity for pharmacological agents that reduce anxiety in humans [26], [42].
Consequently, selection of patients who are most likely to benefit from such agents using predictive biomarkers may aid their efficacy.
Also constant has been the use of oral antipsychotic agents; the predictive strength of early response to depot (injectable) formulations has not yet been evaluated.
Overall metrics of tumor heterogeneity and genome integrity (for example, homologue recombination deficiency score) are emerging as potential new predictive markers for platinum agents.
