Generative Incomplete Multi-View Prognosis Predictor for Breast Cancer: GIMPP

Arya, Nikhilanand; Saha, Sriparna

doi:10.1109/tcbb.2021.3090458

Cited by 19 publications

(7 citation statements)

References 26 publications

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“…Other GAN implementations have been applied to the MB or TCGA cohorts in the past, addressing different aims such as the generation of missing data [ 60 ], the identification of multi -omics signatures [ 61 ] and prognostication [ 62 ]. While showing encouraging results, these prior works limited the follow up time to 5 years and focused on death events only.…”

Section: Discussionmentioning

confidence: 99%

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Guttà

Morhard²,

Rehm

2023

PLoS Comput Biol

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Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

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Section: Discussionmentioning

confidence: 99%

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Guttà

Morhard²,

Rehm

2023

PLoS Comput Biol

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show abstract

“…When compared to a classic CNN, the T-GAN-D showed comparable, yet slightly improved performance. Other GAN implementations have been applied to the MB or TCGA cohorts in the past, addressing different aims such as the generation of missing data 56 , the identification of multi -omics signatures 57 and prognostication 58 . While showing encouraging results, these prior works limited the follow up time to 5 years and focused on death events only.…”

Section: Discussionmentioning

confidence: 99%

Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer

Guttà

Morhard²,

Rehm

2022

Preprint

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Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, GAN-based data augmentation therefore allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

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“…Dataset. 26 . Hence, we have selected the complete multi-view data among all the possible combinations of modalities using the intersection technique based on common patient ids.…”

Section: Dataset and Methodsmentioning

confidence: 99%

Improving the robustness and stability of a machine learning model for breast cancer prognosis through the use of multi-modal classifiers

Arya

Saha

Mathur³

et al. 2023

Sci Rep

Self Cite

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Breast cancer is a deadly disease with a high mortality rate among PAN cancers. The advancements in biomedical information retrieval techniques have been beneficial in developing early prognosis and diagnosis systems for cancer patients. These systems provide the oncologist with plenty of information from several modalities to make the correct and feasible treatment plan for breast cancer patients and protect them from unnecessary therapies and their toxic side effects. The cancer patient’s related information can be collected using various modalities like clinical, copy number variation, DNA-methylation, microRNA sequencing, gene expression, and histopathological whole slide images. High dimensionality and heterogeneity in these modalities demand the development of some intelligent systems to understand related features to the prognosis and diagnosis of diseases and make correct predictions. In this work, we have studied some end-to-end systems having two main components : (a) dimensionality reduction techniques applied to original features from different modalities and (b) classification techniques applied to the fusion of reduced feature vectors from different modalities for automatic predictions of breast cancer patients into two categories: short-time and long-time survivors. Principal component analysis (PCA) and variational auto-encoders (VAEs) are used as the dimensionality reduction techniques, followed by support vector machines (SVM) or random forest as the machine learning classifiers. The study utilizes raw, PCA, and VAE extracted features of the TCGA-BRCA dataset from six different modalities as input to the machine learning classifiers. We conclude this study by suggesting that adding more modalities to the classifiers provides complementary information to the classifier and increases the stability and robustness of the classifiers. In this study, the multimodal classifiers have not been validated on primary data prospectively.

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Generative Incomplete Multi-View Prognosis Predictor for Breast Cancer: GIMPP

Cited by 19 publications

References 26 publications

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Applying GAN-based data augmentation to improve transcriptome-based prognostication in breast cancer

Improving the robustness and stability of a machine learning model for breast cancer prognosis through the use of multi-modal classifiers

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