Contribution | Applied filters | Potential correlation adjustment | Strengths | Limitations |
---|---|---|---|---|
Random forest (Darst) | None | Yes | Model free; adequate for high-dimensional data | Does not work well with highly correlated variables |
Deep learning (Islam) | Methylation variability | Yes | Robust; adequate for high-dimensional data | Difficult result interpretation, tough parameter set up, large sample sizes are needed |
Cluster analysis (Kapusta) | Reported genome-wide association studies on metabolic syndrome and fenofibrate treatment, principal component analysis, random forest | Yes | Intuitive cluster interpretation | Previous dimension reduction can be indicated |
Mixed models (Datta) | Mixed models modification | Yes | Simple regression framework | Not indicated for low-dimensional data |
Gene-set enrichment (Piette) | T-tests and linear regression | No | Circumvents multiple testing | Requires biological insight |