An understanding of the molecular mechanisms underlying resistance to HDACi may help identify predictive biomarkers for response to HDACi therapy.
Our most important finding is that incorporating higher-order representative features leads to improved prediction performance, which may help build better predictive models for cancer prognosis.
Recent progress in molecular psychiatry and the use of multiscale datasets coupled with machine learning techniques may help to refine predictive models of conversion to bipolar disorder in clinical and population samples (Kapczinski and Passos 2015).
Additionally, more in-depth investigations on the correlation of the percentage of Her2/neu-immunopositive GCs with clinical outcomes may help to improve predictive diagnostics.
The understanding of the signalling pathways involved in these processes may help us find predictive factors for tumour aggressiveness and therapy resistance.
Such studies may help to nominate predictive biomarkers that can be applied to enrich patient populations with higher likelihood of response or resistance to a given immunotherapeutic intervention.
Exploiting bioinformatics, such as from expression analyses and knowledge of the location of genes known to influence traits in beef cattle or other species, may help to increase predictive ability by allowing focusing on additional SNP only in the regions that lack sufficient LD.
A better and more consistent data collection regime may help to improve the predictive performance of the NNM and identify the significant factors.
These variables may help to improve the predictive capacity of future models.
Increased data definition may help in increasing the predictive accuracy, but the use of aggregate percentages in this study has produced good results.
