IRK
发表于 2025-3-25 06:08:26
https://doi.org/10.1007/978-3-642-93418-6utional layers to achieve simultaneously a large sequence context while interpreting the DNA sequence at single base pair resolution. Using transfer learning of convolutional weights trained to predict a compendium of chromatin features across cell types allows deepC to predict cell type-specific ch
gain631
发表于 2025-3-25 07:32:11
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Cytology
发表于 2025-3-25 14:07:48
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foodstuff
发表于 2025-3-25 17:52:44
https://doi.org/10.1007/978-3-658-28778-8cts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.
foliage
发表于 2025-3-25 22:52:20
Walter Bien,Angela Hartl,Markus Teubnerse of methylation information from neighboring sites to recover partially observed methylation patterns. Our method and software are proven to be faster and more accurate among all evaluated. Ultimately, our method allows for a more streamlined monitoring of epigenetic changes within cellular populations and their putative role in disease.
stress-test
发表于 2025-3-26 02:23:16
Integrating Single-Cell Methylome and Transcriptome Data with MAPLE,cts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.
COLIC
发表于 2025-3-26 08:07:23
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sparse
发表于 2025-3-26 10:12:47
1064-3745 ation advice from the experts.This volume details state-of-the-art computational methods designed to manage, analyze, and generally leverage epigenomic and epitranscriptomic data. Chapters guide readers through fine-mapping and quantification of modifications, visual analytics, imputation methods, s
tendinitis
发表于 2025-3-26 16:08:33
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fallible
发表于 2025-3-26 19:04:01
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