ArticlePloS one2018
A robust gene regulatory network inference method base on Kalman filter and linear regression.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- A general model-based causal inference method overcomes the curse of synchrony and indirect effect.Nature communications · 2023Article
- Gene regulatory network inference based on a nonhomogeneous dynamic Bayesian network model with an improved Markov Monte Carlo sampling.BMC bioinformatics · 2023Article
- Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM.BMC bioinformatics · 2023Article
- Article
- MICFuzzy: A maximal information content based fuzzy approach for reconstructing genetic networks.PloS one · 2023Article
- Computational systems biology in disease modeling and control, review and perspectives.NPJ systems biology and applications · 2022Review
- Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data.Nucleic acids research · 2021Article
- Optimal parameter identification of synthetic gene networks using harmony search algorithm.PloS one · 2019Article
- Network Medicine in the Age of Biomedical Big Data.Frontiers in genetics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The reconstruction of the topology of gene regulatory networks (GRNs) using high throughput genomic data such as microarray gene expression data is an important problem in systems biology. The main challenge in gene expression data is the high number of genes and low number of samples; also the data are often impregnated with noise. In this paper, in dealing with the noisy data, Kalman filter based method that has the ability to use prior knowledge on learning the network was used. In the proposed method namely (KFLR), in the first phase by using mutual information, the noisy regulations with low correlations were removed. The proposed method utilized a new closed form solution to compute the posterior probabilities of the edges from regulators to the target gene within a hybrid framework of Bayesian model averaging and linear regression methods. In order to show the efficiency, the proposed method was compared with several well know methods. The results of the evaluation indicate that the inference accuracy was improved by the proposed method which also demonstrated better regulatory relations with the noisy data.
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.