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Related Experiment Video

Updated: Jun 23, 2025

Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Contrastive pre-training for sequence based genomics models.

Ksenia Sokolova, Kathleen M Chen, Olga Troyanskaya

Biorxiv : the Preprint Server for Biology

|June 25, 2024

Summary

This summary is machine-generated.

We developed cGen, a new unsupervised method to pre-train deep learning models for genomics. This approach improves performance in tasks like gene expression prediction, especially when data is limited.

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Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

Area of Science:

Genomics
Deep Learning
Bioinformatics

Background:

Deep learning is increasingly used in genomics.
Complex models require substantial data or strategic initialization for optimal performance.

Purpose of the Study:

Introduce cGen, a novel unsupervised, model-agnostic contrastive pre-training method for sequence-based models.
Improve deep learning model performance in genomics, particularly in data-scarce scenarios.

Main Methods:

cGen uses unsupervised contrastive pre-training to learn intrinsic genome features.
It initializes model weights, reducing the need for large datasets.
The method is model-agnostic and makes no assumptions about genomic structure.

Main Results:

Embeddings from unsupervised cGen are informative for gene expression prediction.
Learned sequence features enable meaningful clustering.
cGen enhances performance in chromatin profiling prediction and gene expression tasks.

Conclusions:

cGen improves deep learning model performance in genomics without architecture modification.
This method is particularly beneficial for applications with limited data availability.
Unsupervised pre-training offers a powerful strategy for advancing genomic deep learning.