ReviewFrontiers in physiology2016
Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review.
Review in Frontiers in physiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 41 papers, 1 of them a synthesis that pooled it.
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
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.
- Meta-analysis of dispensable essential genes and their interactions with bypass suppressors.Life science alliance · 2024Pooled it
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.Briefings in bioinformatics · 2026Article
- Predicting FOX gene candidates for oxic nitrogen fixation using multi-omic machine learning and comparative bioinformatics.Scientific reports · 2026Article
- A deep ensemble framework for human essential gene prediction by integrating multi-omics data.Scientific reports · 2025Article
- Identifying Opportunity Targets in Gram-Negative Pathogens for Infectious Disease Mitigation.ACS central science · 2025Review
- 'Bingo'-a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data.Briefings in bioinformatics · 2023Article
- TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models.Scientific data · 2022Article
- Computer-Based Identification of Potential Druggable Targets in Multidrug-ResistantMicroorganisms · 2022Article
- Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing.Human genetics · 2022Article
- Predicting and explaining the impact of genetic disruptions and interactions on organismal viability.Bioinformatics (Oxford, England) · 2022Article
- Review
- Biological network topology features predict gene dependencies in cancer cell-lines.Bioinformatics advances · 2022Article
- PangenomeNet: a pan-genome-based network reveals functional modules on antimicrobial resistome for Escherichia coli strains.BMC bioinformatics · 2021Article
- Review
- Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning.International journal of molecular sciences · 2021Article
- NetGenes: A Database of Essential Genes Predicted Using Features From Interaction Networks.Frontiers in genetics · 2021Article
- A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.International journal of molecular sciences · 2020Article
- DeeplyEssential: a deep neural network for predicting essential genes in microbes.BMC bioinformatics · 2020Article
- DeepHE: Accurately predicting human essential genes based on deep learning.PLoS computational biology · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research.
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.