AIMC Topic: Genes, Essential

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Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

Scientific reports Apr 22, 2024
The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understandin...
Humans Machine Learning Computational Biology Evolution, Molecular Genes, Essential Neoplasms Protein Interaction Maps
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Essential genes identification model based on sequence feature map and graph convolutional neural network.

BMC genomics Jan 10, 2024
BACKGROUND: Essential genes encode functions that play a vital role in the life activities of organisms, encompassing growth, development, immune system functioning, and cell structure maintenance. Conventional experimental techniques for identifying...
Genes, Essential Entropy Neural Networks, Computer Algorithms Genomics
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Untangling the Context-Specificity of Essential Genes by Means of Machine Learning: A Constructive Experience.

Biomolecules Dec 22, 2023
Gene essentiality is a genetic concept crucial for a comprehensive understanding of life and evolution. In the last decade, many essential genes (EGs) have been determined using different experimental and computational approaches, and this informatio...
Machine Learning Genes, Essential
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XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

PLoS computational biology Oct 2, 2023
Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the...
Humans Genes, Essential Neoplasms Gene Expression Profiling Artificial Intelligence Databases, Factual
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An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.

BMC bioinformatics Sep 18, 2023
BACKGROUND: The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed t...
Deep Learning Mice Humans Animals Genes, Essential Metagenomics Genomics Cell Line
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EPGAT: Gene Essentiality Prediction With Graph Attention Networks.

IEEE/ACM transactions on computational biology and bioinformatics Jun 3, 2022
Identifying essential genes and proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correla...
Algorithms Protein Interaction Maps Proteins Genes, Essential Neural Networks, Computer Machine Learning Humans
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Prediction of Essential Genes in Comparison States Using Machine Learning.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Identifying essential genes in comparison states (EGS) is vital to understanding cell differentiation, performing drug discovery, and identifying disease causes. Here, we present a machine learning method termed Prediction of Essential Genes in Compa...
Genetic Markers Machine Learning Transcriptome Computational Biology Gene Regulatory Networks Genes, Essential
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Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning.

International journal of molecular sciences May 11, 2021
Experimental studies of and have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are esse...
Genomics Evolution, Molecular Genes, Essential Drosophila Proteins Machine Learning Animals Caenorhabditis elegans Drosophila melanogaster Caenorhabditis elegans Proteins Gene Ontology
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Essential gene prediction using limited gene essentiality information-An integrative semi-supervised machine learning strategy.

PloS one Nov 30, 2020
Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly ...
Genetic Diseases, Inborn Metabolic Networks and Pathways Area Under Curve Algorithms Humans Support Vector Machine Cluster Analysis Machine Learning Genes, Essential
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A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.

International journal of molecular sciences Nov 28, 2020
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. ...
Neural Networks, Computer Deep Learning Area Under Curve Sequence Analysis, DNA Species Specificity Algorithms Genes, Essential Reproducibility of Results
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