WebTo extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to … WebJan 1, 2024 · Europe PMC is an archive of life sciences journal literature.
DeepLncLoc: a deep learning framework for long non-coding RNA ...
WebBrief Bioinform. 2024 Dec 21:bbac565. doi: 10.1093/bib/bbac565. Online ahead of print.ABSTRACTThe subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding lncRNA functions. Most of existing lncRNA subcellular localization prediction methods use k-mer frequency features to encode lncRNA sequences. … WebFinally, GraphLncLoc uses a fully connected layer to perform the prediction task. The core idea is inspired by the de Bruijn graph in genome assembly [22]. Figure 1(A) shows the … browns ranch trail head az
GraphLncLoc: long non-coding RNA subcellular …
WebJan 1, 2024 · GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation. Li M , Zhao B , Yin R , Yin R , Lu C , Guo F , Zeng M Brief Bioinform, 24 (1):bbac565, 01 Jan 2024 Cited by: 0 articles PMID: 36545797 WebGraphLncLoc/models/lncRNA_lib.py/Jump to Code definitions lncRNALocalizerClass__init__FunctionpredictFunctionitem2graphFunctiontransformFunctionvote_predictFunction Code navigation index up-to-date Go to file Go to fileT Go to lineL Go to definitionR Copy path Copy permalink browns range pilot plant