Ke Chen

This paper improves the minimum flow decomposition algorithm for bioinformatics tasks by jointly modeling equations in flow networks, achieving faster and more accurate results.

TENNIS predicts unannotated isoforms and refines existing annotations using an evolution-based model of alternative splicing, validated by deep long-read RNA-seq.

MELO-ED improves edit distance computation by using multi-embeddings to approximate similarity between biological sequences efficiently.

This paper introduces LLMED, a genomic large language model-based embedding method that improves edit distance approximation and similar sequence search accuracy.

This paper introduces SubseqHash2, an improved subsequence-based seeding method that achieves faster computation while maintaining high accuracy for error-prone long reads.

This paper introduces SubseqSketch, an alignment-free method for sequence similarity estimation that uses random subsequences to efficiently approximate edit distances, showing effectiveness in benchmark tasks.