ArticleNPJ systems biology and applications2020
Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data.
Article in NPJ systems biology and applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Comprehensive analysis of the RBP regulome reveals functional modules and drug candidates in liver cancer.Scientific reports · 2026Article
- Gene regulatory network transitions reveal the central transcription factors in lung adenocarcinoma progression.NPJ systems biology and applications · 2026Article
- Biofilm-associated antibiotic tolerance in the era of multidrug resistance: quorum-sensing mechanisms and emerging therapeutic strategies.Frontiers in cellular and infection microbiology · 2026Review
- Topology-based metrics for finding the optimal sparsity in gene regulatory network inference.Bioinformatics (Oxford, England) · 2025Article
- Emerging Signatures of Hematological Malignancies from Gene Expression and Transcription Factor-Gene Regulations.International journal of molecular sciences · 2024Article
- OneSC: a computational platform for recapitulating cell state transitions.Bioinformatics (Oxford, England) · 2024Article
- OneSC: A computational platform for recapitulating cell state transitions.bioRxiv : the preprint server for biology · 2024Article
- UNMF: a unified nonnegative matrix factorization for multi-dimensional omics data.Briefings in bioinformatics · 2023Article
- Drug Target Identification and Drug Repurposing in Psoriasis through Systems Biology Approach, DNN-Based DTI Model and Genome-Wide Microarray Data.International journal of molecular sciences · 2023Article
- Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications.International journal of molecular sciences · 2022Article
- Knowledge of the perturbation design is essential for accurate gene regulatory network inference.Scientific reports · 2022Article
- Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications.International journal of molecular sciences · 2022Article
- Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications.International journal of molecular sciences · 2022Article
- Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.Frontiers in genetics · 2022Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.
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