BGPT: Create Graphs: Plot Indisulam tumor outcomes by model

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Indisulam tumor outcomes by model — data check

I can’t plot “Indisulam tumor outcomes by model” yet because the provided research data contains no Indisulam-specific tumor outcome values (e.g., tumor volume change, survival, response rates) broken down by model name.

What I do have in your input are pan-cancer/other model summaries (e.g., MutationProjector, scBGDL, SubNetDL, etc.) and unrelated drug design/PRM/radiomics/spatial proteomics papers—but none include an Indisulam dosing/efficacy table by model.

Missing inputs required to generate the requested Plotly graphs

List of models to appear on the x-axis or y-grouping (e.g., “Model A”, “Model B”, ...).
Indisulam tumor outcome metric per model (and units), such as: tumor volume at end-point, Δ tumor volume, hazard ratio for survival, ORR/CR rate, etc.
Uncertainty information if available (SD/SEM/CI; number of mice/patients per model).
Directionality (is “lower tumor volume” better?) if you want the y-axis interpreted consistently.

Next step (fast): upload or paste the Indisulam outcome-by-model table

Paste a table like: model | outcome | error_low | error_high | n. Once you provide it, I will immediately generate Plotly figures (e.g., grouped bar chart with CIs, dot/forest plot, and rank-order plot).

Feedback:

Updated: April 22, 2026

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1. MutationProjector is a cancer foundation model trained on >30,000 tumor genomes across ten cancer types that translates tumor mutation profiles into compact subtype representations using graph attention over integrated molecular networks, enabling leading-edge downstream predictions of immunotherapy and chemotherapy responses, metastasis, and biomarker discovery while offering interpretability of mutational patterns. [2025]

9QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

2. A graph-based deep learning framework (scBGDL) that integrates single-cell and bulk transcriptomic data to identify clinical cancer subtypes and predict survival and treatment outcomes across 16 cancer types, with interpretable driver-edge analyses and applications to LUAD prognosis, EOC platinum-based chemotherapy response, and SKCM immunotherapy outcomes. [2025]

9QualityResults Limitations Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

3. Ageing of the pancreatic tumour microenvironment reprograms PDAC malignant and stromal cells across mouse and human models, revealing inflammatory rewiring and IRAK4 as an age-specific therapeutic vulnerability. [2026]

9QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

4. A subclonality-aware network-propagation deep learning framework (SubNetDL) integrates tumor subclone mutation profiles with a protein-protein interaction network to predict cancer therapy response across multiple cancer types, achieving robust cross-cohort performance and revealing interpretable biomarker genes. [2026]

8QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

5. G2D-Diff is a diffusion-based generative model that designs drug-like small molecules conditioned on cancer genotype and desired drug response, producing diverse, feasible, and genotype-matched hit compounds (including zero-shot TNBC cases) with interpretable attention linking mutations to pathways. [2025]

9QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

Keep Exploring

What exact Indisulam endpoint (tumor volume, survival, or response class) and model list should be used for your analysis?

Can you provide the raw outcome table so I can generate a confidence-interval forest plot and a model ranking plot?

Analysis Wizard

It will parse an uploaded Indisulam-by-model outcome table, compute summary statistics and confidence intervals, and render Plotly charts comparing each model’s tumor outcome metric.

Science Art

Science Movie

Make a narrated HD Science movie for this answer ($32 per minute)

Discussion

BGPT Bias

I prioritize strict data availability and will not fabricate outcome-by-model values when they are not present in the provided inputs.

Stay current without chasing every paper.

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The Evidence Layer for Scientific AI

Ask questions across claims linked to experiments, exact results, limitations, and sources.Know what the science actually supports before you trust the answer.

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3. Ageing of the pancreatic tumour microenvironment reprograms PDAC malignant and stromal cells across mouse and human models, revealing inflammatory rewiring and IRAK4 as an age-specific therapeutic vulnerability. [2026]

11. This study integrates graph neural networks with transcriptomics analysis to identify key pathways and a gene signature, termed responseScore, that predicts immunotherapy responses in melanoma patients. [2025]

12. Radiomics and deep learning in medical imaging are reviewed as complementary approaches for early cancer detection and treatment response prediction, using breast cancer as a case study to contrast feature-based versus end-to-end modeling and discuss challenges to clinical deployment. [2021]

Ask a Follow-Up

Keep Exploring

What exact Indisulam endpoint (tumor volume, survival, or response class) and model list should be used for your analysis?

Can you provide the raw outcome table so I can generate a confidence-interval forest plot and a model ranking plot?

Analysis Wizard

It will parse an uploaded Indisulam-by-model outcome table, compute summary statistics and confidence intervals, and render Plotly charts comparing each model’s tumor outcome metric.

Science Art

Science Movie

Make a narrated HD Science movie for this answer ($32 per minute)

Discussion

BGPT Bias

I prioritize strict data availability and will not fabricate outcome-by-model values when they are not present in the provided inputs.

Stay current without chasing every paper.

My BGPT

Trending

The Evidence Layer for Scientific AI

Ask questions across claims linked to experiments, exact results, limitations, and sources.Know what the science actually supports before you trust the answer.

Explore by Goal

Long Response Copied

Top Data Sources ExportMCP

3. Ageing of the pancreatic tumour microenvironment reprograms PDAC malignant and stromal cells across mouse and human models, revealing inflammatory rewiring and IRAK4 as an age-specific therapeutic vulnerability. [2026]

11. This study integrates graph neural networks with transcriptomics analysis to identify key pathways and a gene signature, termed responseScore, that predicts immunotherapy responses in melanoma patients. [2025]

12. Radiomics and deep learning in medical imaging are reviewed as complementary approaches for early cancer detection and treatment response prediction, using breast cancer as a case study to contrast feature-based versus end-to-end modeling and discuss challenges to clinical deployment. [2021]

Ask a Follow-Up

Keep Exploring

What exact Indisulam endpoint (tumor volume, survival, or response class) and model list should be used for your analysis?

Can you provide the raw outcome table so I can generate a confidence-interval forest plot and a model ranking plot?

Analysis Wizard

It will parse an uploaded Indisulam-by-model outcome table, compute summary statistics and confidence intervals, and render Plotly charts comparing each model’s tumor outcome metric.

Science Art

Science Movie

Make a narrated HD Science movie for this answer ($32 per minute)

Discussion

BGPT Bias

I prioritize strict data availability and will not fabricate outcome-by-model values when they are not present in the provided inputs.

Stay current without chasing every paper.

My BGPT

Trending

Top Data Sources Export MCP