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New deep-learning framework predicts gene regulation at single-cell level

New deep-learning framework predicts gene regulation at single-cell level
Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level.
Deep learning, a family of machine-learning methods based on artificial neural networks, has revolutionized applications such as image interpretation, natural language processing and autonomous driving. In a study published recently in
Science Advances, UCI researchers describe how the 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.
According to co-senior 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 is a protein that controls ....

United States , Xiaohui Xie , Emily Henderson , Simons Center , University Of California , Multiscale Cell Fate Research , National Science Foundation , Science Advances , Qing Nie , National Science Foundation Simons Center , Deep Learning , ஒன்றுபட்டது மாநிலங்களில் , எமிலி ஹென்டர்சன் , சிமன்கள் மையம் , பல்கலைக்கழகம் ஆஃப் கலிஃபோர்னியா , தேசிய அறிவியல் அடித்தளம் , குயிங் நியீ , தேசிய அறிவியல் அடித்தளம் சிமன்கள் மையம் , ஆழமான கற்றல் ,

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

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Irvine, Calif., Jan. 5, 2021 Scientists at the University of California, Irvine have developed a new deep-learning framework that predicts gene regulation at the single-cell level.
Deep learning, a family of machine-learning methods based on artificial neural networks, has revolutionized applications such as image interpretation, natural language processing and autonomous driving. In a study published recently in
Science Advances, UCI researchers describe how the 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.
According to co-senior 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 is a protein that controls the translation of genetic informa ....

United States , Orange County , Howard Gillman , Laiyi Fu , Emmanuel Dollinger , Xiaohui Xie , Lihua Zhang , University Of California , Information Engineering At China Xian Jiaotong University , Multiscale Cell Fate Research , National Science Foundation , Department Of Computer Science , Simons Center , School Of Electronic , Association Of American Universities , Science Advances , Qing Nie , National Science Foundation Simons Center , Computer Science , Information Engineering , Xian Jiaotong University , Chancellor Howard Gillman , Mathematics Statistics , Algorithms Models , ஒன்றுபட்டது மாநிலங்களில் , ஆரஞ்சு கவுண்டி ,