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Jiyun Zhou

As seen in: MDPI, BioRxiv
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Articles

GCGACNN: A Graph Neural Network and Random Forest for Predicting Microbe-Drug Associations

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By Shujuan Su, Meiling Liu, Jiyun Zhou, Jingfeng Zhang
| MDPI
1. Introduction Microbes exist either as single-celled organisms or as colonies of cells and are composed of bacteria, archaea, fungi, viruses, and protozoa [1]. The constituents of the microbiota—bacteria, viruses, and eukaryotes—have been shown to interact with one another and with the host immune system in ways that influence the development of disease [2]. A variety of microbes exist throughout the human body and have a fundamental role in human health [3].
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scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia

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By Jiyun Zhou, Chongyuan Luo, Hanqing Liu, Matthew G. Heffel
| BioRxiv
Abstract DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells.
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Deep learning predicts DNA methylation regulatory variants in specific brain cell types and enhances fine mapping for brain disorders

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By Daniel M. Weinberger, Shizhong Han, Jiyun Zhou
| BioRxiv
Abstract DNA methylation (DNAm) is essential for brain development and function and potentially mediates the effects of genetic risk variants underlying brain disorders. We present INTERACT, a transformer-based deep learning model to predict regulatory variants impacting DNAm levels in specific brain cell types, leveraging existing single-nucleus DNAm data from the human brain.
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