3 papers
q-bio.QM2024
Happy and Immersive Clustering Segmentations of Biological Co-Expression Patterns
Richard Tjörnhammar
In this work, we present an approach for evaluating segmentation strategies and solving the biological problem of creating robust interpretable maps of biological data by employing…
q-bio.QM2022
Clustering Optimisation Method for Highly Connected Biological Data
Richard Tjörnhammar
Currently, data-driven discovery in biological sciences resides in finding segmentation strategies in multivariate data that produce sensible descriptions of the data. Clustering i…
q-bio.QM2020
Exploratory Projection to Latent Structure Models for use in Transcriptomic Analysis
Edward Tjörnhammar, Richard Tjörnhammar
In this paper, we ask if it is possible to increase the interpretability in multivariate analysis by aligning and projecting covariates onto comparative subspaces. We demonstrate o…