ArticleCancers2020
A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Pretrained language models for semantics-aware data harmonisation of observational clinical studies in the era of big data.BMC medical informatics and decision making · 2025Article
- 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
- Topic modeling analysis of the Allen Human Brain Atlas.Scientific reports · 2025Article
- Article
- Article
- Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning.Frontiers in genetics · 2022Article
- Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology.International journal of molecular sciences · 2021Review
- Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach.Proceedings of machine learning research · 2021Article
- Topic Evolution Analysis for Omics Data Integration in Cancers.Frontiers in cell and developmental biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.
Indexed as
Identifiers
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.