Anna Köttgen | Searcharxiv

arXiv2026

Small Data Explainer -- The impact of small data methods in everyday life

The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as how best to include under-represented groups in data-driven policy and decision making, or the health benefits of assistive technologies. We provide a conceptual overview, clarify the relationship between small data and big data, and identify common themes from exemplary case studies and application areas. Potential solutions are described in a more detailed technical overview of current data analysis and modelling techniques, highlighting contributions from different disciplines, such as knowledge-driven modelling from statistics and data-driven modelling from computer science. By linking application settings, conceptual contributions and specific techniques, we highlight what is already feasible and suggest what an agenda for fully leveraging small data might look like.

cs.CY↗

arXiv2018

Fully integrative data analysis of NMR metabolic fingerprints with comprehensive patient data: a case report based on the German Chronic Kidney Disease (GCKD) study

Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To that end, it is necessary to integrate omics data with other data types such as clinical, phenotypic, and demographic parameters of categorical or continuous nature. Here, we exemplify this data integration issue for a study on chronic kidney disease (CKD), where complex clinical and demographic parameters were assessed together with one-dimensional (1D) 1H NMR metabolic fingerprints. Routine analysis screens for associations of single metabolic features with clinical parameters, which requires confounding variables typically chosen by expert knowledge to be taken into account. This knowledge can be incomplete or unavailable. The results of this article are manifold. We introduce a framework for data integration that intrinsically adjusts for confounding variables. We give its mathematical and algorithmic foundation, provide a state-of-the-art implementation, and give several sanity checks. In particular, we show that the discovered associations remain significant after variable adjustment based on expert knowledge. In contrast, we illustrate that the discovery of associations in routine analysis can be biased by incorrect or incomplete expert knowledge in univariate screening approaches. Finally, we exemplify how our data integration approach reveals important associations between CKD comorbidities and metabolites. Moreover, we evaluate the predictive performance of the estimated models on independent validation data and contrast the results with a naive screening approach.

stat.AP↗