Examples of problems that can be solved using data science:
Statistics, multivariate analyses, machine learning, manual curation, and follow-up experiments are typical necessities to arrive at those factors that causally affect product quality.
Statistics, clustering and manual curation were used to integrate genomics and glycomics data to find this causally related bone mineralization factor.
Random Forest Machine Learning, statistics and modelling applied to whole blood transcriptome to derive a predictive gene expression signature.
Random Forest Machine Learning to pinpoint toxin genes that associate to human colorectal cancer from meta-transcriptomics data.
Based on statistics and machine learning find metabolites that anti-correlate to presence of plant predator insects with the aim to find that metabolite that deters a particular plant predator.