Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning

Obregón, Flavio Pazos; Silvera, Diego; Soto, Pablo; Yankilevich, Patricio; Guerberoff, Gustavo; Cantera, Rafael

doi:10.1038/s41598-022-15329-w

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…To generate consistent predictions, they emphasized the need to develop new characteristics that indicate the GCN structural qualities and the hierarchical structure of biological processes. Obregón et al [ 15 ] used the gene’s location in the genomes to which they belong to predict their function. They executed machine learning models and trained them using attributes derived from the location of genes in the genomes to which they belong to predict thousands of gene functions.…”

Section: Introductionmentioning

confidence: 99%

A methodology for classifying tissue-specific metabolic and inflammatory receptor functions applied to subcutaneous and visceral adipose

Yehuda

Somekh²

2022

PLoS ONE

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To achieve homeostasis, the human biological system relies on the interaction between organs through the binding of ligands secreted from source organs to receptors located on destination organs. Currently, the changing roles that receptors perform in tissues are only partially understood. Recently, a methodology based on receptor co-expression patterns to classify their tissue-specific metabolic functions was suggested. Here we present an advanced framework to predict an additional class of inflammatory receptors that use a feature space of biological pathway enrichment analysis scores of co-expression networks and their eigengene correlations. These are fed into three machine learning classifiers–eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and K-Nearest Neighbors (k-NN). We applied our methodology to subcutaneous and visceral adipose gene expression datasets derived from the GTEx (Genotype-Tissue Expression) project and compared the predictions. The XGBoost model demonstrated the best performance in predicting the pre-labeled receptors, with an accuracy of 0.89/0.8 in subcutaneous/visceral adipose. We analyzed ~700 receptors to predict eight new metabolic and 15 new inflammatory functions of receptors and four new metabolic functions for known inflammatory receptors in both adipose tissues. We cross-referenced multiple predictions using the published literature. Our results establish a picture of the changing functions of receptors for two adipose tissues that can be beneficial for drug development.

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Section: Introductionmentioning

confidence: 99%

A methodology for classifying tissue-specific metabolic and inflammatory receptor functions applied to subcutaneous and visceral adipose

Yehuda

Somekh²

2022

PLoS ONE

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Leveraging Deep Learning for Genomics Analysis

Gandhewar,

Pimpalkar,

Jadhav

et al. 2024

Genomics at the Nexus of AI, Computer Vision, and Machine Learning

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Bacterial protein function prediction via multimodal deep learning

Muzio,

Adamer,

Fernandez

et al. 2024

Preprint

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Bacterial proteins are specialized with extensive functional diversity for survival in diverse and stressful environments. A significant portion of these proteins remains functionally uncharacterized, limiting our understanding of bacterial survival mechanisms. Hence, we developed Deep Expression STructure (DeepEST), a multimodal deep learning framework designed to accurately predict protein function in bacteria by assigning Gene Ontology (GO) terms. DeepEST comprises two modules: a multi-layer perceptron that takes gene expression and location as input features, and a protein structure-based predictor. Within DeepEST, we integrated these modules through a learnable weighted linear combination and introduced a novel masked loss function to fine-tune the structure-based predictor for bacterial species. We showed that DeepEST strongly outperforms existing protein function prediction methods relying solely on amino acid sequence or protein structure. Moreover, DeepEST predicts GO terms for unclassified hypothetical proteins across 25 human bacterial pathogens, facilitating the design of experimental setups for characterization studies.

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Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning

Cited by 3 publications

References 47 publications

A methodology for classifying tissue-specific metabolic and inflammatory receptor functions applied to subcutaneous and visceral adipose

A methodology for classifying tissue-specific metabolic and inflammatory receptor functions applied to subcutaneous and visceral adipose

Leveraging Deep Learning for Genomics Analysis

Bacterial protein function prediction via multimodal deep learning

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