Summary of Identifying Latent Disease Factors Differently Expressed in Patient Subgroups Using Group Factor Analysis, by Fabio S. Ferreira et al.
Identifying latent disease factors differently expressed in patient subgroups using group factor analysis
by Fabio S. Ferreira, John Ashburner, Arabella Bouzigues, Chatrin Suksasilp, Lucy L. Russell, Phoebe H. Foster, Eve Ferry-Bolder, John C. van Swieten, Lize C. Jiskoot, Harro Seelaar, Raquel Sanchez-Valle, Robert Laforce, Caroline Graff, Daniela Galimberti, Rik Vandenberghe, Alexandre de Mendonca, Pietro Tiraboschi, Isabel Santana, Alexander Gerhard, Johannes Levin, Sandro Sorbi, Markus Otto, Florence Pasquier, Simon Ducharme, Chris R. Butler, Isabelle Le Ber, Elizabeth Finger, Maria C. Tartaglia, Mario Masellis, James B. Rowe, Matthis Synofzik, Fermin Moreno, Barbara Borroni, Samuel Kaski, Jonathan D. Rohrer, Janaina Mourao-Miranda
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study proposes a novel approach to uncover subgroup-specific and subgroup-common latent factors in neurological and mental disorders, addressing challenges posed by heterogeneity. The method, sparse Group Factor Analysis (GFA) with regularized horseshoe priors, uses probabilistic programming to uncover associations among multiple data modalities differentially expressed in sample subgroups. Synthetic experiments demonstrate the approach’s robustness, while application to the Genetic Frontotemporal Dementia Initiative (GENFI) dataset identifies latent disease factors differentially expressed across genetic subgroups. The method captures associations between brain structure and non-imaging variables across subgroups, offering insights into disease profiles. Importantly, two latent factors are more pronounced in homogeneous FTD patient subgroups, showcasing the method’s ability to reveal subgroup-specific characteristics. This approach has potential for integrating multiple data modalities and identifying interpretable latent disease factors that can improve patient characterization and stratification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us better understand different brain disorders by finding patterns that are unique to specific groups of people with these conditions. The researchers developed a new way to look at many types of data together, like brain scans and questionnaires, to find common themes that don’t change across different groups. They tested this method on a dataset of people with frontotemporal dementia and found some patterns that were unique to certain groups. This could help doctors understand these conditions better and develop more targeted treatments. |
