ArticleBMC genomics2015
Identifying essential proteins from active PPI networks constructed with dynamic gene expression.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Strategies for constructing context-specific protein-protein interaction networks.Briefings in bioinformatics · 2026Review
- Beyond α-Glucosidase and α-Amylase Inhibition: Integrated In Vitro and Multi-Scale In Silico Insights into the Antidiabetic and Antioxidant Mechanisms ofMolecules (Basel, Switzerland) · 2026Article
- DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.Genomics, proteomics & bioinformatics · 2025Article
- Computational Analysis of Virus-Host Interactomes.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Gene expression analysis identifies hub genes and pathways distinguishing fatal from survivor outcomes of Ebola virus disease.FASEB bioAdvances · 2024Article
- A protein network refinement method based on module discovery and biological information.BMC bioinformatics · 2024Article
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.Health information science and systems · 2023Article
- Article
- Using Network Pharmacology and Molecular Docking to Explore the Mechanism of Qiju Dihuang Pill against Dry Eye Disease.Computational and mathematical methods in medicine · 2022Article
- Uncovering the Pharmacological Mechanisms of Gexia-Zhuyu Formula (GXZY) in Treating Liver Cirrhosis by an Integrative Pharmacology Strategy.Frontiers in pharmacology · 2022Article
- A novel essential protein identification method based on PPI networks and gene expression data.BMC bioinformatics · 2021Article
- The mechanism of TiaoGanYiPi formula for treating chronic hepatitis B by network pharmacology and molecular docking verification.Scientific reports · 2021Article
- Combined use of feature engineering and machine-learning to predict essential genes inNAR genomics and bioinformatics · 2020Article
- Analysis of Differentially Expressed Genes in Coronary Artery Disease by Integrated Microarray Analysis.Biomolecules · 2019Article
- A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein⁻Protein Interactive Networks.Genes · 2019Article
- Norepinephrine triggers an immediate-early regulatory network response in primary human white adipocytes.BMC genomics · 2018Article
- Improved flower pollination algorithm for identifying essential proteins.BMC systems biology · 2018Article
- DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.PloS one · 2017Article
- A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes.Frontiers in microbiology · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Essential proteins are vitally important for cellular survival and development, and identifying essential proteins is very meaningful research work in the post-genome era. Rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein essentiality at the network level. A series of centrality measures have been proposed to discover essential proteins based on the PPI networks. However, the PPI data obtained from large scale, high-throughput experiments generally contain false positives. It is insufficient to use original PPI data to identify essential proteins. How to improve the accuracy, has become the focus of identifying essential proteins. In this paper, we proposed a framework for identifying essential proteins from active PPI networks constructed with dynamic gene expression. Firstly, we process the dynamic gene expression profiles by using time-dependent model and time-independent model. Secondly, we construct an active PPI network based on co-expressed genes. Lastly, we apply six classical centrality measures in the active PPI network. For the purpose of comparison, other prediction methods are also performed to identify essential proteins based on the active PPI network. The experimental results on yeast network show that identifying essential proteins based on the active PPI network can improve the performance of centrality measures considerably in terms of the number of identified essential proteins and identification accuracy. At the same time, the results also indicate that most of essential proteins are active.
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.