Michael W. Lloyd
Research Subjects
Michael W. Lloyd has a total of 264 co-authors. Here are the top co-authors by number of shared publications:
- Jeffrey H. Chuang
The Jackson Laboratory for Genomic Medicine 1 , Farmington, Connecticut
150 shared publications - Luis Guillermo Carvajal Carmona
Department of Biochemistry and Molecular Medicine, School of Medicine, the Health Equity Leadership, Science, and Community Research Laboratory, Genome Center, University of California, Davis, CA
148 shared publications - Carol J. Bult
The Jackson Laboratory
133 shared publications - Brian J. Sanderson
Affiliation Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas, United States of America
132 shared publications - Sergii Domanskyi
The Jackson Laboratory for Genomic Medicine Farmington CT USA
131 shared publications - Steven B. Neuhauser
The Jackson Laboratory, Bar Harbor, ME, USA
131 shared publications
Latest Publications
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Figure S11 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S11 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S11 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S11 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Figure S12 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S12 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S12 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S12 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Figure S2 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S2 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S2 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S2 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
- Open Access
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Figure S8 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S8 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
- 17 June 2026
- Published by American Association for Cancer Research (AACR)
Open AccessFigure S8 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Figure S8 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis