Jul 11, 2019 · In this study, we present a deep belief networks (DBNs) based prediction method to identify candidate disease-associated non-coding SNPs in human genome.
Jul 26, 2019 · In this study, we present a deep belief networks (DBNs) based prediction method to identify candidate disease-associated non-coding SNPs in ...
A deep belief networks (DBNs) based prediction method to identify candidate disease-associated non-coding SNPs in human genome using a digital coding based ...
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Dec 8, 2021 · We built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method ...
Sep 13, 2024 · This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding ...
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A novel computational method to identify candidate disease-associated non-coding single nucleotide polymorphisms (SNPs) of human genome is presented and can ...
Jul 13, 2022 · Here, we explore whether protein knowledge graphs can be used to identify genes that are targeted by disease-associated non-coding SNPs by ...
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References (27). A Deep Belief Networks Based Prediction Method for Identification of Disease-Associated Non-coding SNPs in Human Genome. Chapter. Jul 2019.
May 28, 2021 · DeepFun aims to predict the effects of genetic variants on a wide range of chromatin features based on deep convolutional neural networks (CNN).
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Discover novel disease-associated genes based on regulatory ...
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A new computational infrastructure, APRIL, is developed to predict disease genes. 3D chromatin contact maps are integrated to construct long-range regulatory ...
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