An integrative deep learning framework for classifying molecular subtypes of breast cancer

Islam, Md. Mohaiminul; Huang, Shujun; Ajwad, Rasif; Chi, Chen; Wang, Yang; Hu, Pingzhao

doi:10.1016/j.csbj.2020.08.005

Cited by 51 publications

(34 citation statements)

References 35 publications

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“…Then, a final neural network is used on the concatenated deep features for prediction of drug activity. Using a similar approach, Islam et al (2020) [128] predicted breast cancer subtypes using the concatenated features, but they learned them through convolution neural networks applied on gene expression and copy number variation datasets. Instead of concatenating the deep features obtained for each omics block, another approach is to simply connect them to a shared layer.…”

Section: Main Integration Strategiesmentioning

confidence: 99%

Integration strategies of multi-omics data for machine learning analysis

Picard

Scott‐Boyer

Bodein

et al. 2021

Computational and Structural Biotechnology Journal

308

185

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Graphical abstract Schematic representation of the main strategies for multi-omics datasets integration. A) Early integration concatenates all omics datasets into a single matrix on which machine learning model can be applied. B) Mixed integration first independently transforms or maps each omics block into a new representation before combining them for downstream analysis. C) Intermediate integration simultaneously transforms the original datasets into common and omics-specific representations. D) Late integration analyses each omics separately and combines their final predictions. E) Hierarchical integration bases the integration of datasets on prior regulatory relationships between omics layers.

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Section: Main Integration Strategiesmentioning

confidence: 99%

Integration strategies of multi-omics data for machine learning analysis

Picard

Scott‐Boyer

Bodein

et al. 2021

Computational and Structural Biotechnology Journal

308

185

View full text Add to dashboard Cite

show abstract

“…Additionally, the authors in reference [6] confirm that deep neural networks perform better than traditional methods as it automatically extracts features from raw data. The data used is copy number alteration and gene expression data for breast cancer patients (METABRIC).…”

Section: Introductionmentioning

confidence: 94%

“…Breast cancer classifiers use different methods and different data. Some methods use images [1][2][3], some use biological data [4,5], and some integrate many types of data [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

El-Nabawy¹,

Belal

El-Bendary

2021

Mathematics

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Automated diagnosis systems aim to reduce the cost of diagnosis while maintaining the same efficiency. Many methods have been used for breast cancer subtype classification. Some use single data source, while others integrate many data sources, the case that results in reduced computational performance as opposed to accuracy. Breast cancer data, especially biological data, is known for its imbalance, with lack of extensive amounts of histopathological images as biological data. Recent studies have shown that cascade Deep Forest ensemble model achieves a competitive classification accuracy compared with other alternatives, such as the general ensemble learning methods and the conventional deep neural networks (DNNs), especially for imbalanced training sets, through learning hyper-representations through using cascade ensemble decision trees. In this work, a cascade Deep Forest is employed to classify breast cancer subtypes, IntClust and Pam50, using multi-omics datasets and different configurations. The results obtained recorded an accuracy of 83.45% for 5 subtypes and 77.55% for 10 subtypes. The significance of this work is that it is shown that using gene expression data alone with the cascade Deep Forest classifier achieves comparable accuracy to other techniques with higher computational performance, where the time recorded is about 5 s for 10 subtypes, and 7 s for 5 subtypes.

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“…DL approaches have shown advantages over supervised ML methods for their ability of automatically extracting features from input data [ 114 ]. An autoencoder-based DL approach combining supervised classification and unsupervised clustering revealed the presence of novel breast and bladder cancer subtypes associated with different prognosis [ 66 ].…”

Section: Ai Mining Of Cancer Transcriptomesmentioning

confidence: 99%

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

Giudice

Peirone

Perrone

et al. 2021

IJMS

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Artificial intelligence, or the discipline of developing computational algorithms able to perform tasks that requires human intelligence, offers the opportunity to improve our idea and delivery of precision medicine. Here, we provide an overview of artificial intelligence approaches for the analysis of large-scale RNA-sequencing datasets in cancer. We present the major solutions to disentangle inter- and intra-tumor heterogeneity of transcriptome profiles for an effective improvement of patient management. We outline the contributions of learning algorithms to the needs of cancer genomics, from identifying rare cancer subtypes to personalizing therapeutic treatments.

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An integrative deep learning framework for classifying molecular subtypes of breast cancer

Abstract: Graphical abstract

Cited by 51 publications

References 35 publications

Integration strategies of multi-omics data for machine learning analysis

Integration strategies of multi-omics data for machine learning analysis

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology

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