Sentence examples for a personalised model from inspiring English sources

That, in turn, will reduce the time needed to develop new personalised diagnostic tests and treatments.According to Mike Svinte, head of IBM's information-based medicine initiative, "biobanking is becoming an essential part of the transformation to a personalised model of medicine".

An inverse dynamic model with three segments has been developed by Saraswat et al. [ 14], however the anatomical information was derived from literature, making it impossible to produce a personalised model.

However, the principal limitation of this personalised model is that only 12% of new cases of prostate cancer in Poland occur in men with one of these mutations and therefore the potential to reduce the overall cancer burden is limited.

22 Personalised models could facilitate this learning process, with a potential long-term impact on lifestyle adjustments that would be important to observe and quantify in future studies.

The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility.

Prof Kasabov originated methods and systems for intelligent information processing, including: evolving connectionist systems, hybrid neuro-fuzzy systems, evolving- and brain –inspired spiking neural network architectures, quantum-inspired methods, methods for personalised modelling in bio and neuroinformatics, published in more than 600 works.

Additional file 2: k nearest neighbour (k NN) classification methods for personalised modelling.

One can envisage a scenario where, by using such a personalised CLL xenograft model, together with new methodologies such as next generation sequencing and single cell analysis, the best combination of compounds targeting different CLL subclones can be easily designed.

The aim of this study was to model the efficiency of a personalised screening strategy based on a combination of age and polygenic risk-profile compared with a strategy based on age alone in prostate and breast cancer.

We modelled the efficiency of a personalised approach to screening for prostate and breast cancer based on age and polygenic risk-profile compared with the standard approach based on age alone.

Our results also show that a personalised recommender system powered by the SOM predictive model is able to produce consistent recommendations.