A metabolomics diagnostic method using UPLC-MS/MS to detect specific plasma biomarker concentrations.
Transfer learning with graph neural networks predicts binding affinity and epitope recognition to reduce wet-lab screening costs.
Trained ML models predict genomic pipeline success, eliminating manual stress testing delays.
A bioinformatics method extracts somatic mutations from single-cell transcriptome data by integrating results from multiple comparison and identification modes.
A program counts atomic group pairs at specific sequence distances in cyclic molecules to generate structural feature matrices.
ELISA-based screening identifies high-titer bispecific antibody clones with low side products, resolving the trade-off between throughput and sample volume.