ArticleCancers2022
Multiomics Topic Modeling for Breast Cancer Classification.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder.Scientific reports · 2025Article
- Exploring the latent space of transcriptomic data with topic modeling.NAR genomics and bioinformatics · 2025Article
- Gene expression and agent-based modeling improve precision prognosis in breast cancer.Scientific reports · 2025Article
- Topic modeling analysis of the Allen Human Brain Atlas.Scientific reports · 2025Article
- Combining Implementation and Data Sciences to Accelerate Evidence Integration into Healthcare - ImpleMATE.F1000Research · 2025Article
- Article
- Review
- Pancancer survival prediction using a deep learning architecture with multimodal representation and integration.Bioinformatics advances · 2023Article
- Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases.Bioinformatics advances · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The integration of transcriptional data with other layers of information, such as the post-transcriptional regulation mediated by microRNAs, can be crucial to identify the driver genes and the subtypes of complex and heterogeneous diseases such as cancer. This paper presents an approach based on topic modeling to accomplish this integration task. More specifically, we show how an algorithm based on a hierarchical version of stochastic block modeling can be naturally extended to integrate any combination of 'omics data. We test this approach on breast cancer samples from the TCGA database, integrating data on messenger RNA, microRNAs, and copy number variations. We show that the inclusion of the microRNA layer significantly improves the accuracy of subtype classification. Moreover, some of the hidden structures or "topics" that the algorithm extracts actually correspond to genes and microRNAs involved in breast cancer development and are associated to the survival probability.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.