ArticleNature computational science2023
Large-scale correlation network construction for unraveling the coordination of complex biological systems.
Article in Nature computational science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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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.
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Who cites it
23 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
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- Multivariate hematological and biochemical profiling reveals conserved physiological modules in Brahman- and Charolais-crossbred beef cattle under tropical smallholder systems.Veterinary world · 2026Article
- Cross-Disease Breathomics by PTR-TOF-MS: Multiclass Machine Learning and Network Remodeling Across Asthma, COPD, Cystic Fibrosis, and Lymphangioleiomyomatosis.International journal of molecular sciences · 2026Article
- Multi-Omics for Mothers and Infants (MOMI) Consortium: a global initiative to study adverse pregnancy outcomes.Journal of global health · 2026Article
- Distribution-valued data graphical model estimation based on M-LDQ feature embedding.Journal of applied statistics · 2026Article
- Dietary Intervention with Cottonseed and Olive Oil Differentially Affect the Circulating Lipidome and Immunoregulatory Compounds-A Randomized Clinical Trial.Metabolites · 2025Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Large language models in perioperative medicine-applications and future prospects: a narrative review.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2025Review
- Persistence of a Proteomic Signature After a Hypertensive Disorder of Pregnancy.Hypertension (Dallas, Tex. : 1979) · 2025Article
- Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.Pediatric research · 2025Article
- Advancing clinical biochemistry: addressing gaps and driving future innovations.Frontiers in medicine · 2025Review
- Learning Gaussian Graphical Models from Correlated Data.Frontiers in systems biology · 2025Article
- PyNetCor: a high-performance Python package for large-scale correlation analysis.NAR genomics and bioinformatics · 2024Article
- Bayesian inference of sample-specific coexpression networks.Genome research · 2024Article
- On network deconvolution for undirected graphs.Biometrics · 2024Article
- Placental Origins of Preeclampsia: Insights from Multi-Omic Studies.International journal of molecular sciences · 2024Review
- Artificial Intelligence in Perioperative Care: Opportunities and Challenges.Anesthesiology · 2024Article
- Network dynamics and therapeutic aspects of mRNA and protein markers with the recurrence sites of pancreatic cancer.Heliyon · 2024Article
- A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction.Heliyon · 2024Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
Advanced measurement and data storage technologies have enabled high-dimensional profiling of complex biological systems. For this, modern multiomics studies regularly produce datasets with hundreds of thousands of measurements per sample, enabling a new era of precision medicine. Correlation analysis is an important first step to gain deeper insights into the coordination and underlying processes of such complex systems. However, the construction of large correlation networks in modern high-dimensional datasets remains a major computational challenge owing to rapidly growing runtime and memory requirements. Here we address this challenge by introducing CorALS (Correlation Analysis of Large-scale (biological) Systems), an open-source framework for the construction and analysis of large-scale parametric as well as non-parametric correlation networks for high-dimensional biological data. It features off-the-shelf algorithms suitable for both personal and high-performance computers, enabling workflows and downstream analysis approaches. We illustrate the broad scope and potential of CorALS by exploring perspectives on complex biological processes in large-scale multiomics and single-cell studies.
Identifiers
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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.