Data-driven AI system for learning how to run transcript assemblers
Yihang Shen, Zhiwen Yan, Carl Kingsford
AutoTuneX improves transcript assembly accuracy by predicting optimal parameters for RNA-seq samples.
Author
Carnegie Mellon UniversityORCID 0000-0002-6161-3906Unclaimed ORCID profile · follow to get notified of new preprints
5 papers
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Yihang Shen, Zhiwen Yan, Carl Kingsford
AutoTuneX improves transcript assembly accuracy by predicting optimal parameters for RNA-seq samples.
Yihang Shen, Lingge Yu, Yutong Qiu…·Carl Kingsford
The paper introduces a novel algorithm to improve Hi-C contact matrices by inferring linear genomes from Hi-C data using genome graphs, resulting in more accurate structural representations.
The paper introduces a probabilistic method to correct a previously uncharacterized Hi-C artifact caused by incorrect alignment in repeat regions.
Natalie Sauerwald, Yihang Shen, Carl Kingsford
The paper uses topological data analysis to study chromosome structure changes during cellular differentiation across three cell lines, identifying global and local topological differences.