ArticleBiometrics2019
Incorporating prior information with fused sparse group lasso: Application to prediction of clinical measures from neuroimages.
Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Variable Selection via Fused Sparse-Group Lasso Penalized Multi-state Models Incorporating Molecular Data.Biometrical journal. Biometrische Zeitschrift · 2025Trial
- TC2-Res: a structured fusion of tract-level and connectome-level brain imaging in small-sample cohorts of athletes.Frontiers in neuroanatomy · 2026Article
- S3R: Modeling spatially varying associations with Spatially Smooth Sparse Regression.bioRxiv : the preprint server for biology · 2025Article
- Delineating a Pathway for the Discovery of Functional Connectome Biomarkers of Autism.Advances in neurobiology · 2024Review
- Interpretable principal component analysis for multilevel multivariate functional data.Biostatistics (Oxford, England) · 2023Article
- COVID-19 modelling by time-varying transmission rate associated with mobility trend of driving via Apple Maps.Journal of biomedical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Predicting clinical variables from whole-brain neuroimages is a high-dimensional problem that can potentially benefit from feature selection or extraction. Penalized regression is a popular embedded feature selection method for high-dimensional data. For neuroimaging applications, spatial regularization using the
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.