Understanding complex systems through differential causal networks
Sign Up to like & getrecommendations! Published in 2024 at "Scientific Reports"
DOI: 10.1038/s41598-024-78606-w
Abstract: In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently… read more here.
Keywords: understanding complex; causal relationships; differential causal; complex systems ... See more keywords
Inference of Causal Networks Using a Topological Threshold
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DOI: 10.1109/access.2024.3451626
Abstract: We propose a constraint-based algorithm, which automatically determines causal relevance thresholds, to infer causal networks from data. We call these topological thresholds. We present two methods for determining the threshold: the first seeks a set… read more here.
Keywords: inference causal; networks using; causal; threshold ... See more keywords
Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC
Sign Up to like & getrecommendations! Published in 2024 at "BMC Medical Research Methodology"
DOI: 10.1186/s12874-024-02229-y
Abstract: Background Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context… read more here.
Keywords: hypertension using; molecular networks; interactive molecular; causal ... See more keywords
Learning causal networks with latent variables from multivariate information in genomic data
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DOI: 10.1371/journal.pcbi.1005662
Abstract: Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models… read more here.
Keywords: information; genomic data; causal networks; latent variables ... See more keywords
Combining Mendelian randomization and network deconvolution for inference of causal networks with GWAS summary data
Sign Up to like & getrecommendations! Published in 2023 at "PLOS Genetics"
DOI: 10.1371/journal.pgen.1010762
Abstract: Mendelian randomization (MR) has been increasingly applied for causal inference with observational data by using genetic variants as instrumental variables (IVs). However, the current practice of MR has been largely restricted to investigating the total… read more here.
Keywords: network; mendelian randomization; gwas summary; causal ... See more keywords
From classical mendelian randomization to causal networks for systematic integration of multi-omics
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2022.990486
Abstract: The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover… read more here.
Keywords: mendelian randomization; integration; causal; omic data ... See more keywords