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Data-driven AI system for learning how to run transcript assemblers
Yihang Shen1, Zhiwen Yan1, Carl Kingsford2
1Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA.
None:
We introduce AutoTuneX, a data-driven, AI system designed to automatically predict optimal parameters for transcript assemblers - tools for reconstructing transcripts from the reads in a given RNA-seq sample. AutoTuneX is built by learning parameter knowledge from existing RNA-seq samples and transferring this knowledge to unseen samples. On 1588 human RNA-seq samples tested with two transcript assemblers, AutoTuneX predicts parameters that resulted in 98% of samples achieving more accurate transcript assembly compared to using default parameters, with some samples experiencing up to a 600% improvement in AUC. AutoTuneX offers a new strategy for automatically optimizing use of sequence analysis tools.
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