ArticleGenes2020
Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification.
Article in Genes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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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
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 37 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- EssSubgraph improves performance and generalizability of mammalian essential gene prediction with large networks.GigaScience · 2025Article
- HELP: A computational framework for labelling and predicting human common and context-specific essential genes.PLoS computational biology · 2024Article
- Enzymatic Metabolic Switches of Astrocyte Response to Lipotoxicity as Potential Therapeutic Targets for Nervous System Diseases.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience.Biomolecules · 2023Article
- Tumor type classification and candidate cancer-specific biomarkers discovery via semi-supervised learning.Biophysics reports · 2023Article
- Biological network topology features predict gene dependencies in cancer cell-lines.Bioinformatics advances · 2022Article
- A disease-related essential protein prediction model based on the transfer neural network.Frontiers in genetics · 2022Article
- CREB1ACS omega · 2021Article
- Identifying Cancer Subtypes Using a Residual Graph Convolution Model on a Sample Similarity Network.Genes · 2021Article
- An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity.Frontiers in aging neuroscience · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein-protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes' sequence information or genes' centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches.
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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.