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 :



The Machine Ethics Podcast: AI readiness with Tim El-Sheikh

In this episode, Ben chats with Tim El-Sheikh about ethical AI as the smarter AI, the importance of a business AI strategy, getting data ready, and more.
22 October 2021, by

Join our team of AIhub ambassadors!

We are looking for people to join us as AIhub ambassadors.
21 October 2021, by

Interview with Lily Xu – applying machine learning to the prevention of illegal wildlife poaching

Lily Xu tells us about her work applying machine learning and game theory to wildlife conservation.
20 October 2021, by

What bird is singing? Merlin Bird ID app offers instant answers

The Cornell Lab of Ornithology’s free Merlin Bird ID app can identify bird sounds.
19 October 2021, by

Distilling neural networks into wavelet models using interpretations

We propose a method which distills information from a trained DNN into a wavelet transform.
18 October 2021, by

Cynthia Rudin wins AAAI Squirrel AI Award

Duke professor becomes second recipient of AAAI Squirrel AI Award for pioneering socially responsible AI.
15 October 2021, by





©2021 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association