ReviewMolecular genetics & genomic medicine2022
A review of causal discovery methods for molecular network analysis.
Review in Molecular genetics & genomic medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Causal network analysis of omics data using prior knowledge databases.Briefings in bioinformatics · 2025Pooled it
- CONSTRUCTING GENE REGULATORY NETWORK USING CHATTERJEE'S RANK CORRELATION WITH SINGLE-CELL TRANSCRIPTOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Investigating causal networks of dementia using causal discovery and natural language processing models.NPJ dementia · 2025Article
- Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.BMC medical research methodology · 2024Article
- Bioinformatics analysis of the potentially functional circRNA-miRNA-mRNA network in breast cancer.PloS one · 2024Article
- A review of causal discovery methods for molecular network analysis.Molecular genetics & genomic medicine · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundWith the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions.
methodsWe have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade.
resultsIn this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area.
conclusionExisting methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.
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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.