ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2019
Shallow Sparsely-Connected Autoencoders for Gene Set Projection.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders.Nucleic acids research · 2024Article
- Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication.BioData mining · 2023Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- Artificial neural networks enable genome-scale simulations of intracellular signaling.Nature communications · 2022Article
- Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.International journal of molecular sciences · 2021Article
- Single cell RNA sequencing for breast cancer: present and future.Cell death discovery · 2021Review
- Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining.NPJ systems biology and applications · 2021Article
- Style transfer with variational autoencoders is a promising approach to RNA-Seq data harmonization and analysis.Bioinformatics (Oxford, England) · 2020Article
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
When analyzing biological data, it can be helpful to consider gene sets, or predefined groups of biologically related genes. Methods exist for identifying gene sets that are differential between conditions, but large public datasets from consortium projects and single-cell RNA-Sequencing have opened the door for gene set analysis using more sophisticated machine learning techniques, such as autoencoders and variational autoencoders. We present shallow sparsely-connected autoencoders (SSCAs) and variational autoencoders (SSCVAs) as tools for projecting gene-level data onto gene sets. We tested these approaches on single-cell RNA-Sequencing data from blood cells and on RNA-Sequencing data from breast cancer patients. Both SSCA and SSCVA can recover known biological features from these datasets and the SSCVA method often outperforms SSCA (and six existing gene set scoring algorithms) on classification and prediction tasks.
Indexed as
Identifiers
30963076PMC6417803What 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.