ArticleInterdisciplinary sciences, computational life sciences2022
Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm.
Article in Interdisciplinary sciences, computational life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed.
- Bayesian Topology Inference of Regulatory Networks under Partial Observability.Results in control and optimization · 2025Article
- Review
- Article
- CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data.Interdisciplinary sciences, computational life sciences · 2024Article
- Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders.Frontiers in microbiology · 2023Article
- Myocardial Infarction-Induced INSL6 Decrease Contributes to Breast Cancer Progression.Disease markers · 2023Article
- Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis.BMC genomics · 2022Article
- RLFDDA: a meta-path based graph representation learning model for drug-disease association prediction.BMC bioinformatics · 2022Article
- Predicting miRNA-disease association through combining miRNA function and network topological similarities based on MINE.iScience · 2022Article
- Bioinformatics analyses of potential ACLF biological mechanisms and identification of immune-related hub genes and vital miRNAs.Scientific reports · 2022Article
- Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models.Scientific reports · 2022Article
- De Novo design of potential inhibitors against SARS-CoV-2 Mpro.Computers in biology and medicine · 2022Article
- Hyb4mC: a hybrid DNA2vec-based model for DNA N4-methylcytosine sites prediction.BMC bioinformatics · 2022Article
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- Predicting circRNA-drug sensitivity associations via graph attention auto-encoder.BMC bioinformatics · 2022Article
- Hierarchical graph attention network for miRNA-disease association prediction.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- Revealing key lncRNAs in cytogenetically normal acute myeloid leukemia by reconstruction of the lncRNA-miRNA-mRNA network.Scientific reports · 2022Article
- Research on a Weighted Gene Co-expression Network Analysis method for mining pathogenic genes in thyroid cancer.PloS one · 2022Article
- Article
- SGII: Systematic Identification of Essential lncRNAs in Mouse and Human Genome With lncRNA-Protein-Protein Heterogeneous Interaction Network.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Inferring gene regulatory networks (GRNs) from microarray data can help us understand the mechanisms of life and eventually develop effective therapies. Currently, many computational methods have been used in inferring GRNs. However, owing to high-dimensional data and small samples, these methods often tend to introduce redundant regulatory relationships. Therefore, a novel network inference method based on the improved Markov blanket discovery algorithm, IMBDANET, is proposed to infer GRNs. Specifically, for each target gene, data processing inequality was applied to the Markov blanket discovery algorithm for the accurate differentiation of direct regulatory genes from indirect regulatory genes. Finally, direct regulatory genes were used in constructing GRNs, and the network structure was optimized according to the importance degree score. Experimental results on six public network datasets show that the proposed method can be effectively used to infer GRNs.
Indexed as
Identifiers
34495484What 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.