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AI in Cancer Research / AI+癌症研究Cancer Bioinformatics

Li Ding

李丁

PhD

🏢Washington University in St. Louis(圣路易斯华盛顿大学)🌐USA

Professor, Medicine & Genetics; Co-Director, Computational Biology医学与遗传学教授;计算生物学联合主任

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h-index
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Key Papers
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Awards
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Key Contributions

👥Biography 个人简介

Li Ding leads large-scale cancer genome analysis at Washington University, with central roles in TCGA and CPTAC. Her lab develops computational methods to identify cancer driver mutations, characterize tumor evolution, and integrate genomic, proteomic and clinical data.

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🧪Research Fields 研究领域

Cancer Genomics癌症基因组学
TCGA AnalysisTCGA分析
Multi-omics Integration多组学整合
Clonal Evolution克隆进化

🎓Key Contributions 主要贡献

TCGA Pan-Cancer Analysis

Led computational analyses across multiple TCGA cancer types, identifying key driver genes and mutational processes shaping cancer genomes at scale.

Proteogenomics

Co-led CPTAC proteogenomic studies integrating DNA, RNA and protein data in breast, colon, ovarian and lung cancers, linking genomic alterations to protein-level changes.

Cancer Evolution

Developed computational frameworks to reconstruct clonal evolution from tumor sequencing data, characterizing subclonal architecture and therapy resistance mechanisms.

Representative Works 代表性著作

[1]

Landscape of somatic mutations in 560 breast cancer whole-genome sequences

Nature (2016)

Comprehensive whole-genome analysis identifying novel driver genes and mutational signatures shaping breast cancer.

[2]

Proteogenomic characterization of human colon and rectal cancer

Cell (2019)

CPTAC landmark study integrating genomic and proteomic data in colorectal cancer, revealing protein-level cancer drivers.

[3]

Comprehensive molecular characterization of lung adenocarcinoma

Nature (2014)

TCGA analysis of lung adenocarcinoma defining molecular subtypes and actionable alterations across 230 tumors.

🏆Awards & Recognition 奖项与荣誉

🏆NIH Director's New Innovator Award
🏆Komen Scholar Award
🏆TCGA Analysis Working Group Leadership Award

📄Data Sources 数据来源

Last updated: 2026-01-15 | All information from publicly available academic sources

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