
Quirin Stier- Master of Science
- Data Scientist at Philipps University of Marburg
Quirin Stier
- Master of Science
- Data Scientist at Philipps University of Marburg
Hey there, I am pursuing research in AI.
About
6
Publications
1,814
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
53
Citations
Introduction
Current institution
Publications
Publications (6)
Diagnostic immunophenotyping of malignant non-Hodgkin-lymphoma (NHL) by multiparameter flow cytometry (MFC) relies on highly trained physicians. Artificial intelligence (AI) systems have been proposed for this diagnostic task, often requiring more learning examples than are usually available. In contrast, Flow XAI has reduced the number of needed l...
Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance- and density-based structures. The topological invariance of dimension indicates that the two-dimen...
The Gene Ontology (GO) knowledge base provides a standardized vocabulary of GO terms for describing gene functions and attributes. It consists of three directed acyclic graphs which represent the hierarchical structure of relationships between GO terms. GO terms enable the organization of genes based on their functional attributes by annotating gen...
Research data obtained during economics or human studies experiments often displays a complex distribution. Even in the two-dimensional case, the statistical identification of subgroups in research data poses an analytical challenge. Here we introduce an interactive R-based tool called “AdaptGauss2D”. It enables a valid identification of a meaningf...
The forecasting of univariate time series poses challenges in industrial applications if the seasonality varies. Typically, a non-varying seasonality of a time series is treated with a model based on Fourier theory or the aggregation of forecasts from multiple resolution levels. If the seasonality changes with time, various wavelet approaches for u...
The article presents immediate access to over fifty fundamental clustering algorithms. Additionally, access to clustering benchmark datasets published priorly as “Fundamental Clustering Problems Suite” (FCPS) is provided. The software library is named “FCPS”, available in R on CRAN and accessible within Python. The input and output of clustering al...