# Masked autoencoders and masked gene modelling

Source: https://onco.cc/terms/masked-modelling/  
OnCo record `masked-modelling` (Term). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

A masked autoencoder hides a random part of the input (image patches, or half the genes in a profile) and learns to reconstruct it from the rest.

## Summary

An autoencoder learns an encoding that compresses the input and a decoding that recreates it (Wikipedia); the masked variant reconstructs only the hidden portion, which forces the encoder to learn the structure that relates parts of the input. For expression data the hidden parts are genes, so the model learns co-expression structure; for multimodal data one modality's token can be reconstructed from the others. It is the pretraining objective behind many pathology and transcriptome foundation models.

## Fields

- Kind: Term
- Last checked: 2026-09-24
- Also known as: masked autoencoder; masked autoencoders; masked gene modelling; masked gene modeling; masked reconstruction; cross-modal masked reconstruction; autoencoder
- Tags: cansim-terms

## Notes

- Listed in the CanSim terms map 1.0.0 (docs/onco/terms.json, generated 2026-09-24), CC BY 4.0, attribution: CanSim project, an open, public-data-first cancer foundation-model programme; CanSim page path /terms/masked-autoencoder.

## Sources

- Wikipedia: https://en.wikipedia.org/wiki/Autoencoder
- Wikipedia: https://en.wikipedia.org/wiki/Autoencoder

## Connected records

- terms: [Cancer AI vocabulary (CanSim terms map)](https://onco.cc/terms/cancer-ai-vocabulary/), [Gene co-expression structure](https://onco.cc/terms/gene-co-expression/), [Loss functions: cross-entropy and mean squared error](https://onco.cc/terms/cross-entropy-mse/), [Self-supervised pretraining (SSL)](https://onco.cc/terms/self-supervised-pretraining/), [Tokenisation (genes, tiles and sequence as tokens)](https://onco.cc/terms/tokenisation/)

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JSON: https://onco.cc/api/v1/entities/masked-modelling.json