Brian J. Sanderson
Computer Science · Purdue University West Lafayette
Publications
248
Citations
1,050
Est. group size
~3
Recurring co-author estimate
Active years
45
Publishing since 1982
Brian J. Sanderson's work centers on building and releasing computational workflows and software tools (e.g., Nextflow-based pipelines) that support genomics and cancer research, including single-cell transcriptomics and large-scale image and genomic data analysis in cancer. Much of the associated output is data infrastructure and supplementary material tied to collaborative cancer studies, such as melanoma treatment resistance and pan-cancer histology image repositories, suggesting a role supporting bioinformatics and computational analysis pipelines rather than solely wet-lab research.
Publication output has grown substantially over the last decade, rising sharply from near zero in 2017 to a peak in 2024, with continued high output through 2026, indicating a strongly increasing trend in recent years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- KU-GDSC/workflows: Release v0.1.2
Zenodo (CERN European Organization for Nuclear Research) · 2026
- KU-GDSC/workflows: Release v0.1.3
Zenodo (CERN European Organization for Nuclear Research) · 2026
- jds-nf-workflows
Zenodo (CERN European Organization for Nuclear Research) · 2026
- jds-nf-workflows
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Single-cell transcriptomic profiling of C. elegans Q neuroblast lineage during migration and differentiation
PLoS ONE · 2026
- KU-GDSC/workflows: Release v0.1.5
Zenodo (CERN European Organization for Nuclear Research) · 2026
- KU-GDSC/workflows: Release v0.1.4
Zenodo (CERN European Organization for Nuclear Research) · 2026
- Figure S15 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
2026
- Supplementary Table 8 from Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma
2026
- Figure S10 from A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
2026
- Supplementary Figure 7 from Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma
2026
- Supplementary Table 5 from Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma
2026
- Supplementary Figure 2 from Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma
2026
- Supplementary Table 10 from Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma
2026
- Supplementary Figure 8 from Spatiotemporal Profiling Defines Persistence and Resistance Dynamics during Targeted Treatment of Melanoma
2026
- bioRxiv (Cold Spring Harbor Laboratory)×13
- Zenodo (CERN European Organization for Nuclear Research)×8
- Cancer Research×7
- American Journal of Botany×3
- Nature Communications×2
- Giovanni Lujan
Computer Science · The Ohio State University
- Abdul Akbar
Computer Science · The Ohio State University
- Anil V. Parwani
Computer Science · The Ohio State University
- Can Cui
Computer Science · Purdue University West Lafayette
- Muhammad Khalid Khan Niazi
Computer Science · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
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