Bioinformatic data analysis and statistical modelling are now playing an increasingly important role in biology. The simulation and modeling of biological systems involves defining algorithms for a range of computational tasks such as data mining, statistical analysis, pattern matching and searching.
Bioinformatics provides for experimental projects in terms of downstream primary data analysis.
Translating your research outputs into high-level, user interpretable data analyses can both help to engage with people in your discipline and to promote your work across the wider research community.
After processing, users can be presented with long lists of features that can be hard to understand without context. Using statistical and machine learning techniques, we can help make sense of these data and provide robust and informative conclusions to studies.
Possible techniques used include supervised methods, such as regresssion, classification and feature selection to unsupervised techniques such as clustering and visualisation. More sophisticated techniques that interpret results in the context of a system are also possible.