Lusheng Li

MOTLAB: A Weighted Multi-Omics Transfer Learning Approach to Mitigate Breast Cancer Racial Disparities

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

Molecular Characterization of T-Lineage Acute Lymphoblastic Leukemia by an Optimal-Transport Based Multi-Omics Integration Framework

OTTER improves T-ALL molecular characterization by integrating transcriptomic and genomic data using optimal transport, outperforming existing methods and identifying subtype-specific features.

A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma

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

ECD co-operates with ERBB2 to promote tumorigenesis through upregulation of unfolded protein response and glycolysis

This study shows ECD and ERBB2 cooperate in breast cancer by enhancing unfolded protein response and glycolysis through direct mRNA binding and increased glucose uptake.

AttentionAML: An Attention-based Deep Learning Framework for Accurate Molecular Categorization of Acute Myeloid Leukemia

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

RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-cell Acute Lymphoblastic Leukemia

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