ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2021
Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 2026Review
- Dynamic Bayesian networks for neural information flow: evaluation of continuous and discrete scoring metrics.Journal of computational neuroscience · 2026Article
- DC-FusionGNN: A Dual-Channel Framework Integrating Global Self-Attention and Local Topology Learning for Identifying Key Resistance Genes AgainstPlants (Basel, Switzerland) · 2026Article
- Methotrexate Exposure and Inflammatory-Metabolic Biomarker Networks in Hospitalized Patients with Psoriasis: A Network Analysis Approach.Pharmaceuticals (Basel, Switzerland) · 2026Article
- A network-guided penalized regression with application to proteomics data.Bioinformatics advances · 2026Article
- The Constrained Disorder Principle: A Paradigm Shift for Accurate Interactome Mapping and Information Analysis in Complex Biological Systems.Bioengineering (Basel, Switzerland) · 2025Review
- ShinyDegSEM: an interactive application for pathway perturbation analysis in gene expression studiesPeerJ · 2025Article
- DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.Bioinformatics (Oxford, England) · 2024Article
- A Joint estimation approach to sparse additive ordinary differential equations.Statistics and computing · 2022Article
- A Matrix-VariateFrontiers in artificial intelligence · 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
The rise of network data in many different domains has offered researchers new insight into the problem of modeling complex systems and propelled the development of numerous innovative statistical methodologies and computational tools. In this paper, we primarily focus on two types of biological networks, gene networks and brain networks, where statistical network modeling has found both fruitful and challenging applications. Unlike other network examples such as social networks where network edges can be directly observed, both gene and brain networks require careful estimation of edges using covariates as a first step. We provide a discussion on existing statistical and computational methods for edge esitimation and subsequent statistical inference problems in these two types of biological networks.
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.