ArticleInternational journal of molecular sciences2020
A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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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.
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Who cites it
32 citing papers in PubMed.
- CNN4Essential: a convolutional neural network model for predicting bacterial gene essentiality based on multi-feature fusion.BMC genomics · 2026Article
- A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction.BMC biology · 2025Article
- DeepPD: A Deep Learning Method for Predicting Peptide Detectability Based on Multi-feature Representation and Information Bottleneck.Interdisciplinary sciences, computational life sciences · 2025Article
- Advancements in AI for Computational Biology and Bioinformatics: A Comprehensive Review.Methods in molecular biology (Clifton, N.J.) · 2025Review
- DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models.Frontiers in medicine · 2025Review
- HELP: A computational framework for labelling and predicting human common and context-specific essential genes.PLoS computational biology · 2024Article
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- A Methodology of Condition Monitoring System Utilizing Supervised and Semi-Supervised Learning in Railway.Sensors (Basel, Switzerland) · 2023Article
- Progress of the "Molecular Informatics" Section in 2022.International journal of molecular sciences · 2023Article
- Development and Validation of an Explainable Machine Learning-Based Prediction Model for Drug-Food Interactions from Chemical Structures.Sensors (Basel, Switzerland) · 2023Article
- Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification.Frontiers in genetics · 2023Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics".International journal of molecular sciences · 2022Article
- Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module.International journal of molecular sciences · 2021Article
- Using Machine Learning Algorithms to Predict Hospital Acquired Thrombocytopenia after Operation in the Intensive Care Unit: A Retrospective Cohort Study.Diagnostics (Basel, Switzerland) · 2021Article
- Human-Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot.Sensors (Basel, Switzerland) · 2021Article
- Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma.Cancers · 2021Article
- Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation.Sensors (Basel, Switzerland) · 2021Article
- GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.International journal of molecular sciences · 2021Article
- Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Essential genes contain key information of genomes that could be the key to a comprehensive understanding of life and evolution. Because of their importance, studies of essential genes have been considered a crucial problem in computational biology. Computational methods for identifying essential genes have become increasingly popular to reduce the cost and time-consumption of traditional experiments. A few models have addressed this problem, but performance is still not satisfactory because of high dimensional features and the use of traditional machine learning algorithms. Thus, there is a need to create a novel model to improve the predictive performance of this problem from DNA sequence features. This study took advantage of a natural language processing (NLP) model in learning biological sequences by treating them as natural language words. To learn the NLP features, a supervised learning model was consequentially employed by an ensemble deep neural network. Our proposed method could identify essential genes with sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC), and area under the receiver operating characteristic curve (AUC) values of 60.2%, 84.6%, 76.3%, 0.449, and 0.814, respectively. The overall performance outperformed the single models without ensemble, as well as the state-of-the-art predictors on the same benchmark dataset. This indicated the effectiveness of the proposed method in determining essential genes, in particular, and other sequencing problems, in general.
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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 Socratic graph.