Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature

Lee, Minhyeok

doi:10.3390/biology12070893

Cited by 15 publications

(7 citation statements)

References 121 publications

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“…In the current landscape of cancer research, the convergence of genetic studies and machine learning presents an unprecedented opportunity to decode the intricate relationship between genomic variations and cancer phenotypes [1][2][3]. The genomic era has witnessed an exponential increase in the availability of genetic data, offering a comprehensive catalog of mutations associated with various cancers [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Kim,

Han,

Nam

et al. 2024

Preprint

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In the dynamic field of cancer research, the fusion of genetics and machine learning presents a groundbreaking opportunity to understand the intricate links between genomic variations and cancer phenotypes. Despite the wealth of genetic data, translating it into actionable insights remains a challenge, particularly in the context of complex cellular metabolism. VaMP (Variants,Metabolic Fluxes &Phenotypes), our novel approach, addresses this by integrating neural networks and metabolic models, offering a comprehensive framework for deciphering cancer biology. The developed method leverages a novel end-to-end neural network architecture, integrating genome-scale metabolic models (GSMs) to capture the intricate relationship between genetic variations and cellular phenotypes. VaMP’s encoder maps genetic variants to metabolic fluxes through a series of carefully designed steps utilizing neural network and GSM, while the decoder predicts phenotype probabilities using the differences between input and reference fluxes. The training data comprise mutation information and phenotypes, eliminating the need for explicit metabolic flux data preparation. Validation experiments on five cancer types demonstrate VaMP’s ability to identify significant genes and metabolic signatures. Further utilizing SCREENER and co-occurrence analyses, the assessment reveals VaMP’s capacity to anticipate established gene-disease relationships. The identified metabolic signatures are robustly substantiated by diverse literature grounded in experimental studies. Furthermore, an in-depth exploration of the outcomes from five VaMP models involved the correlation analysis for variants and fluxes to establish connections between significant genes and cancer-causing variants. Overall, VaMP can be used as a promising tool for unraveling the complex interplay between genetic alterations and cancer phenotypes, with implications for understanding disease mechanisms and identifying novel therapeutic targets.

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Section: Introductionmentioning

confidence: 99%

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Kim,

Han,

Nam

et al. 2024

Preprint

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“…Gene symbols are names that represent the genes of an organism and encode specific functions or proteins. (Lee, 2023) reviews the existing machine learning methods for diagnosis of cancer by using genome expression data and mentions the accuracy of most of these algorithms are between 50-70% depending on the dataset. We achieve the accuracy of 72% on a dataset which includes 300+ genome expressions by building a deep learning MLP model with a customized architecture, hyperparameters, and training algorithm.…”

Section: Introductionmentioning

confidence: 99%

Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach

2024

IJFMR

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This paper proposes a deep learning method to predict cancer risk from gene symbols using a multilayer perceptron (MLP) feed forward neural network. The paper uses a data set of gene symbols and their corresponding cancer risk labels, obtained from a DNA microarray analysis. The paper then builds and compares different machine learning models, such as logistic regression, linear discriminant analysis, quadratic discriminant analysis, decision tree classifier, gaussian nb, ada boost classifier, gaussian process classifier, support vector machine, and random forest. Deep learning MLP model is built, tuned and optimized for hyperparameters which improves the accuracy significantly by 9.09% compared to the best machine learning model. The paper evaluates the performance of the MLP model on the data set using accuracy, precision, recall, and F1-score metrics. This paper contributes to the field of machine learning and bioinformatics by providing a novel and effective way to predict cancer risk from gene symbols.

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“…Models that can predict survival in patients with advanced HGSOC have been described recently (7)(8)(9)(10)(11), but they have limited performance and usually require transcriptomic analysis or tedious image processing, which can be time consuming and costly, especially in regions with limited resources. Thus, a cost-effective and interpretable method for the prediction of ovarian cancer survival is urgently needed for both patients and clinicians.…”

Section: Introductionmentioning

confidence: 99%

Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E–stained images for ovarian cancer prognosis

Ng,

Wong,

Lawson

et al. 2024

Preprint

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Background: The ability to predict the prognosis of patients with ovarian cancer can greatly improve disease management. However, the knowledge on the mechanism of the prediction is limited. We sought to deconvolute the attention feature learnt by a deep learning convolutional neural networks trained with whole-slide images (WSIs) of hematoxylin-and-eosin (H&E)–stained tumor samples using spatial transcriptomic data. Methods: In this study, 773 WSIs of H&E–stained tumor sections from 335 patients with high-grade serous ovarian cancer who were included in The Cancer Genome Atlas (TCGA) Pan-Cancer study were used to train and validate and to test a ResNet101 CNN model modified with attention mechanism. WSIs from patients in an independent cohort were used to further evaluate the model. Results: The prognostic value of the predicted H&E–based survival scores from the trained model on patient survival was evaluated. The attention signals learnt by the model were then examined their correlation with immune signatures using spatial transcriptome. After validating the model with the testing datasets, pathway enrichment analysis showed that the H&E—based survival score significantly correlated with certain immune signatures and this was validated spatially using spatial transcriptome data of ovarian cancer FFPE sample by correlating the selected signature and attention signal. Conclusions: In conclusion, the attention mechanism might be useful to identify regions for their specific immune activities. This could guide future pathological study for the useful immunological features that are important in modulating the prognosis of ovarian cancer patients.

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Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature

Cited by 15 publications

References 121 publications

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach

Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E–stained images for ovarian cancer prognosis

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Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021–2023 Literature

Cited by 15 publications

References 121 publications

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Predicting Cancer Risk from Genome Data: A Multilayer Perceptron Approach

Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&amp;E–stained images for ovarian cancer prognosis

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Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E–stained images for ovarian cancer prognosis