ΑΙhub.org
 

Researchers use deep learning to identify gene regulation at single-cell level

by
16 February 2021



share this:
t-SNE plots for different ATAC-seq data
Clustering performance comparison when different thresholds and parameters are changed. Figure taken from Predicting transcription factor binding in single cells through deep learning, published under a CC BY-NC 4.0 licence.

Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level. In a study published recently in Science Advances, UCI researchers describe how their deep-learning technique can also be successfully used to observe gene regulation at the cellular level. Until now, that process had been limited to tissue-level analysis.

AIhub focus issue on good health and well-being

According to co-author Xiaohui Xie, UCI professor of computer science, the framework enables the study of transcription factor binding at the cellular level, which was previously impossible due to the intrinsic noise and sparsity of single-cell data. A transcription factor (TF) is a protein that controls the translation of genetic information from DNA to RNA; TFs regulate genes to ensure they’re expressed in proper sequence and at the right time in cells.

“The breakthrough was in realizing that we could leverage deep learning and massive datasets of tissue-level TF binding profiles to understand how TFs regulate target genes in individual cells through specific signals,” Xie said.

By training a neural network on large-scale genomic and epigenetic datasets, and by drawing on the expertise of collaborators across three departments, the researchers were able to identify novel gene regulations for individual cells or cell types.

“Our capability of predicting whether certain transcriptional factors are binding to DNA in a specific cell or cell type at a particular time provides a new way to tease out small populations of cells that could be critical to understanding and treating diseases,” said co-author Qing Nie, UCI Chancellor’s Professor of mathematics and director of the campus’s National Science Foundation-Simons Center for Multiscale Cell Fate Research, which supported the project.

He said that scientists can use the deep-learning framework to identify key signals in cancer stem cells – a small cell population that is difficult to specifically target in treatment or even quantify.

“This interdisciplinary project is a prime example of how researchers with different areas of expertise can work together to solve complex biological questions through machine-learning techniques,” Nie added.

Collaborators were Laiyi Fu, a visiting scholar in UCI’s Department of Computer Science who is now a researcher in the School of Electronic and Information Engineering at China’s Xi’an Jiaotong University; Lihua Zhang, a postdoctoral scholar in mathematics; and Emmanuel Dollinger, a graduate student in mathematical, computational & systems biology.

Read the paper in full here.



tags: ,


University of California, Irvine




            AIhub is supported by:


Related posts :



DataLike: Interview with Wuraola Oyewusi

Ndane and Isabella talk to Wuraola Oyewusi about challenging and rewarding aspects of research and how her background in pharmacy has helped her data and AI career

European Union AI Act receives final approval

On 21 May, the Council of the EU formally signed off the artificial intelligence Act.
22 May 2024, by

#ICLR2024 invited talk: Priya Donti on why your work matters for climate more than you think

How is AI research related to climate, and how can the AI community better align their work with climate change-related goals?
21 May 2024, by

Congratulations to the #ICRA2024 best paper winners

The winners and finalists in the different categories have been announced.
20 May 2024, by

Trotting robots offer insights into animal gait transitions

A four-legged robot trained with machine learning has learned to avoid falls by spontaneously switching between walking, trotting, and pronking
17 May 2024, by

Machine learning enhances monitoring of threatened marbled murrelet

CNN analysis of data gathered by acoustic recording devices is a promising new tool for monitoring secretive species.
16 May 2024, by




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association