Yi Zhang

This paper introduces IDR-DCA, a statistical framework for detecting significant evolutionary couplings, showing improved performance over traditional methods in contact prediction and RNA structure prediction.

Stand-alone, point-and-click Java toolkit bundling over 100 sequence, BLAST, GO/KEGG enrichment and interactive plotting functions, so wet-lab biologists can analyse sequencing-scale data without the command line.

This study uses a deep neural network to predict in vivo mRNA structural stability by analyzing six features, finding ribosome density as the most influential factor.

This study uses metatranscriptomic sequencing to analyze the dynamic transcription patterns of an ingested Lactobacillus casei strain in the human gut, revealing significant changes in mRNA and sRNA expression over time.