5 Papers
Shuo Wang is an academic researcher from University of North Carolina at Chapel Hill. The author has contributed to research in topics: Biology & Deep learning. The author has an hindex of 1, co-authored 1 publications.
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Papers
Functional classification of long non-coding RNAs by k-mer content.
Jessime M. Kirk,Susan O Kim,Susan O Kim,Kaoru Inoue,Kaoru Inoue,Matthew J. Smola,Matthew J. Smola,David M Lee,Megan D. Schertzer,Joshua Wooten,Allison R Baker,Allison R Baker,Daniel Sprague,David W Collins,Christopher R Horning,Shuo Wang,Qidi Chen,Kevin M. Weeks,Peter J. Mucha,J. Mauro Calabrese +19 more
TL;DR: A sequence comparison method to deconstruct linear sequence relationships in lncRNAs and evaluate similarity based on the abundance of short motifs called k-mers found that lnc RNAs of related function often had similar k-mer profiles despite lacking linear homology.
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GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction
TL;DR: A model with three feature extractors called GCNfold, which has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy, is proposed.
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SurvConvMixer: robust and interpretable cancer survival prediction based on ConvMixer using pathway-level gene expression images
Shuo Wang,Yuanning Liu,Hao Zhang,Zhen Liu +3 more
TL;DR: This paper proposes SurvConvMixer, a robust and interpretable model for cancer survival prediction using pathway-level gene expression images and ConvMixer, achieving remarkable performance on lung adenocarcinoma, squamous cell carcinoma, and skin cutaneous melanoma with external validation.
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A Novel Deep Learning Method to Predict Lung Cancer Long-Term Survival With Biological Knowledge Incorporated Gene Expression Images and Clinical Data
TL;DR: A novel method to predict lung cancer long-term survival using gene expression data from TCGA is proposed and results indicated that the method performed much better than the ML models and unimodal DL models.
scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder with differentiable edge sampling
Shuo Wang,Yuan Liu,Hao Zhang,Zhen Liu +3 more
TL;DR: This study proposes scE2EGAE, an end-to-end cell-graph-learnable graph autoencoder, to enhance single-cell RNA-Seq data analysis by learning cell graphs and denoising data, outperforming existing methods in denoising, clustering, and cell trajectory inference tasks.