ArticleScientific reports2019
Cell Identity Codes: Understanding Cell Identity from Gene Expression Profiles using Deep Neural Networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 1 citations in OpenAlex.
- Article
- Article
- Novel Gene Biomarkers Specific to Human Mesenchymal Stem Cells Isolated from Bone Marrow.International journal of molecular sciences · 2024Article
- ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery.Nature communications · 2024Article
- Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation.Cell reports methods · 2024Article
- Review
- RNA-Responsive gRNAs for Controlling CRISPR Activity: Current Advances, Future Directions, and Potential Applications.The CRISPR journal · 2022Review
- Topological network measures for drug repositioning.Briefings in bioinformatics · 2021Article
- Application of Machine-Learning Methods to Recognize mitoBK Channels from Different Cell Types Based on the Experimental Patch-Clamp Results.International journal of molecular sciences · 2021Article
- Comparison of similar cells: Mesenchymal stromal cells and fibroblasts.Acta histochemica · 2020Review
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- Organelle Cooperation in Stem Cell Fate: Lysosomes as Emerging Regulators of Cell Identity.Frontiers in cell and developmental biology · 2020Review
- The assessment of efficient representation of drug features using deep learning for drug repositioning.BMC bioinformatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on the whole gene expression profiles (GEPs). The accuracies of simple classification algorithms such as linear discriminators or support vector machines are limited due to the complexity of biological systems. We used deep neural networks to analyze 1040 GEPs from 16 different human tissues and cell types. After comparing different architectures, we identified a specific structure of deep autoencoders that can encode a GEP into a vector of 30 numeric values, which we call the cell identity code (CIC). The original GEP can be reproduced from the CIC with an accuracy comparable to technical replicates of the same experiment. Although we use an unsupervised approach to train the autoencoder, we show different values of the CIC are connected to different biological aspects of the cell, such as different pathways or biological processes. This network can use CIC to reproduce the GEP of the cell types it has never seen during the training. It also can resist some noise in the measurement of the GEP. Furthermore, we introduce classifier autoencoder, an architecture that can accurately identify cell type based on the GEP or the CIC.
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.