Multiomics Topic Modeling for Breast Cancer Classification

Valle, Filippo; Osella, Matteo; Caselle, Michele

doi:10.3390/cancers14051150

Cited by 6 publications

(11 citation statements)

References 79 publications

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“…We speculate that the CNV modality may contain more irrelevant information to the survival prediction task which may introduce unwanted noise instead of providing useful information for prediction. As mentioned in Valle et al (2022) , those ‘hitchhiker’ genes accounting for the majority of CNV dataset may barely provide information beneficial to the final prediction performance.…”

Section: Discussionmentioning

confidence: 99%

Pancancer survival prediction using a deep learning architecture with multimodal representation and integration

Fan

Jiang

Liang³

et al. 2023

Bioinformatics Advances

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Motivation Use of multi-omics data carrying comprehensive signals about the disease is strongly desirable for understanding and predicting disease progression, cancer particularly as a serious disease with a high mortality rate. However, recent methods currently fail to effectively utilize the multi-omics data for cancer survival prediction and thus significantly limiting the accuracy of survival prediction using omics data. Results In this work, we constructed a deep learning model with multimodal representation and integration to predict the survival of patients using multi-omics data. We first developed an unsupervised learning part to extract high-level feature representations from omics data of different modalities. Then, we employed an attention-based method to integrate feature representations, produced by the unsupervised learning part, into a single compact vector and finally we fed the vector into fully connected layers for survival prediction. We used multimodal data to train the model and predict pancancer survival, and the results show that using multimodal data can lead to higher prediction accuracy compared to using single modal data. Furthermore, we used the concordance index and the 5-fold cross-validation method for comparing our proposed method with current state-of-the-art methods and our results show that our model achieves better performance on the majority of cancer types in our testing datasets. Availability https://github.com/ZhangqiJiang07/MultimodalSurvivalPrediction

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Section: Discussionmentioning

confidence: 99%

Pancancer survival prediction using a deep learning architecture with multimodal representation and integration

Fan

Jiang

Liang³

et al. 2023

Bioinformatics Advances

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“…The statistical inference procedure, as well as the definition of topics and probability distributions, leading to the tri-partite network clustering is an extension of the hSBM algorithm. The n-partite SBM, introduced in (18) extends the hSBM algorithm described in (14). Its implementation is available on GitHub at https://github.com/BioPhys-Turin/nsbm.…”

Section: Methodsmentioning

confidence: 99%

“…Multibranched SBM is the multipartite extension of hBSM: it works with a multipartite graph and find partition on each side of the graph. We also ran Multibranched SBM with seven initializations (18).…”

Section: Hsbm Algorithmmentioning

confidence: 99%

“…First, we clustered the cells based on the expression of mRNAs and lncRNAs separately. We then used a multiomic version of hSBM (18) to perform an integrative clustering that combines the information coming from the two RNA families treated as disjoint omic layers. We show that this strategy improves cell classification compared to clustering obtained by investigating mRNAs and lncRNAs separately but also compared to clustering obtained on the expression measures of mRNAs and lncRNAs joined in a single matrix.…”

Section: Introductionmentioning

confidence: 99%

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Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

Malagoli

Valle

Barillot

et al. 2022

Preprint

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Single-cell RNA sequencing is a powerful tool to explore cancer heterogeneity. However, the expression of lncRNAs in single cells is still to be studied extensively and methods to deal with the sparsity of this type of data are lacking. Here, we propose a topic modeling approach to investigate the transcriptional heterogeneity of luminal and triple negative breast cancer cells using patient-derived xenograft models of acquired resistance to chemotherapy and targeted therapy. We show that using an integrative clustering that combines the information coming from mRNAs and lncRNAs treated as disjoint omic layers greatly improves the accuracy of cell classification. Topics associated with specific breast cancer subpopulations show a clear enrichment for pathways involved in subtyping and progression of breast cancer and to sets of lncRNA encoded in the open chromatin regions of breast cancer cell lines. We identified lncRNAs strongly associated with cell clusters already well known in the literature, such as MALAT1 and NEAT1, and highlighted some others that may be clinically relevant.

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“…This algorithm has been applied to categorize social media information (Zheng et al, 2014), and also to image annotation and classification and computer vision (Roller and Schulte im Walde, 2013). This approach has also recently been applied to the classification of clinical notes (Wen et al, 2021) and RNA dual-omics (RNA, microRNA) data (Valle et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases

Kozawa

Urayama

Tejima

et al. 2021

Preprint

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Human diseases are multifactorial - hence it is important to characterize diseases on the basis of multiple disease-omics. However, the capability of the existing methods is largely limited to classifying diseases based on a single type or a few closely related omics data. Herein, we report a topic model framework that allows for characterizing diseases according to their multiple omics data. We also show that this method can be utilized to predict potential biomarkers and/or therapeutic targets. In this study, we illustrate a computational concept of this augmented topic model and demonstrate its prediction performance by a leave one-disease features out cross-validation scheme. Furthermore, we exploit this method together with human disease tissue/organ-transcriptome data and identify putative biomarkers and/or therapeutic targets across 79 diseases. In conclusion, this method and the prediction framework shown reported herein provide important tools for understanding complex human diseases and also facilitate diagnostic and/or therapeutic development.

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Multiomics Topic Modeling for Breast Cancer Classification

Cited by 6 publications

References 79 publications

Pancancer survival prediction using a deep learning architecture with multimodal representation and integration

Pancancer survival prediction using a deep learning architecture with multimodal representation and integration

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases

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