Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge

Anderson, Paul S.; Gadgil, Richa; Johnson, William A.; Schwab, Ella; Davidson, Jean M.

doi:10.1016/j.compbiomed.2021.104850

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…The identification of subtypes can provide an understanding of the underlying molecular mechanisms and thereby help design precise treatment strategies for efficient cancer management. Contrary to classification that is more histologic, the subtypes are influenced by oncogenic alterations and/or modifications in the gene/protein expression ( Anderson et al, 2021 ). Molecular classification has partially elucidated tumor heterogeneity, however, different subtypes can be identified depending on different layers of biological elements: genomics, alterations, gene and protein expression profile as well as cellular composition ( Skoulidis and Heymach, 2019 ).…”

Section: Multi-omics For Greater Accuracymentioning

confidence: 99%

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

Raufaste-Cazavieille

Santiago

Droit

2022

Front. Mol. Biosci.

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The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

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Section: Multi-omics For Greater Accuracymentioning

confidence: 99%

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

Raufaste-Cazavieille

Santiago

Droit

2022

Front. Mol. Biosci.

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“…Deep learning model was utilized in reducing the variability of BC subtype predictors by embedding prior knowledge into the loss function. 11 A nature inspired algorithm namely EHSSA (enhanced Salp Swarm algorithm) 12 was employed in microscopic image segmentation of breast cancer and the results showed significant accuracy in assisting physician for patients' rehabilitation. A new differential evolution algorithm inspired by slime mold foraging behavior leading to the development of superior BC image segmentation model was proposed in Liu et al 13 to achieve high convergence accuracy avoiding local optimum.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Subtypes Classification with Hybrid Machine Learning Model

Sarkar¹,

Mali

2022

Methods Inf Med

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Background Breast cancer is the most prevailing heterogeneous disease among females characterized with distinct molecular subtypes and varied clinicopathological features. With the emergence of various artificial intelligence techniques especially machine learning, the breast cancer research has attained new heights in cancer detection and prognosis. Objective Recent development in computer driven diagnostic system has enabled the clinicians to improve the accuracy in detecting various types of breast tumors. Our study is to develop a computer driven diagnostic system which will enable the clinicians to improve the accuracy in detecting various types of breast tumors. Methods In this article, we proposed a breast cancer classification model based on the hybridization of machine learning approaches for classifying triple-negative breast cancer and non-triple negative breast cancer patients with clinicopathological features collected from multiple tertiary care hospitals/centers. Results The results of genetic algorithm and support vector machine (GA-SVM) hybrid model was compared with classics feature selection SVM hybrid models like support vector machine-recursive feature elimination (SVM-RFE), LASSO-SVM, Grid-SVM, and linear SVM. The classification results obtained from GA-SVM hybrid model outperformed the other compared models when applied on two distinct hospital-based datasets of patients investigated with breast cancer in North West of African subcontinent. To validate the predictive model accuracy, 10-fold cross-validation method was applied on all models with the same multicentered datasets. The model performance was evaluated with well-known metrics like mean squared error, logarithmic loss, F1-score, area under the ROC curve, and the precision–recall curve. Conclusion The hybrid machine learning model can be employed for breast cancer subtypes classification that could help the medical practitioners in better treatment planning and disease outcome.

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Vision Transformers for Breast Cancer Human Epidermal Growth Factor Receptor 2 Expression Staging without Immunohistochemical Staining

Ayana,

Lee,

Choe

2024

The American Journal of Pathology

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Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge

Cited by 5 publications

References 51 publications

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

Breast Cancer Subtypes Classification with Hybrid Machine Learning Model

Vision Transformers for Breast Cancer Human Epidermal Growth Factor Receptor 2 Expression Staging without Immunohistochemical Staining

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