Jieqiong Wang

MOTLAB improves breast cancer outcome prediction in underrepresented Black women by integrating multi-omics data with transfer learning and weighting strategies.

DAG-HEART improves breast cancer outcome prediction in underrepresented populations by integrating multi-omics data with directed acyclic graph constraints and transfer learning.

The paper develops a meta-clustering framework to identify high-resolution molecular subtypes of pediatric low-grade glioma, revealing significant transcriptomic heterogeneity across five subgroups.

SAMP V2 improves antimicrobial peptide identification by integrating biochemical and sequence-order information into split amino acid composition, outperforming existing models on multiple datasets.

PalmaClust detects ultra-rare cell types in scRNA-seq by fusing graphs based on the Palma ratio, improving rare-class F1 scores by 20% over existing methods.

RaMBat accurately identifies medulloblastoma subtypes from diverse data sources with severe batch effects, achieving 99% median accuracy.

MetaPaCS improves pancreatic cancer subtype identification using a meta-learning framework that combines multiple base classifiers, outperforming individual models and existing methods.

The paper proposes a multi-omics transfer learning framework to reduce racial disparities in prostate adenocarcinoma prognosis by integrating mRNA, miRNA, and methylation data.

AttentionAML is a deep learning framework for AML subtype classification using transcriptomic data, achieving high accuracy and outperforming existing methods.

RPSLearner improves NSCLC subtype classification using random projection and stacking learning, achieving higher accuracy and AUC than existing methods.

The paper uses machine learning to analyze functional connectivity in brain networks to distinguish cocaine use disorder from healthy controls, finding that SN and CEN are more critical than DMN in addiction.

SAMP identifies antimicrobial peptides using an ensemble model with new features, outperforming existing methods on multiple datasets.

RanBALL is a model for B-ALL subtype identification using random projection and ensemble learning, achieving high accuracy and outperforming existing methods.