BGPT: Create Graphs: LTBR MDMX p53 Data

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Create Graphs: LTBR • MDMX • p53 Data

I can’t create the requested LTBR/MDMX/p53 graphs yet because no quantitative LTBR, MDMX (MDM4), or TP53 data (e.g., expression values, mutation counts, phospho-states, or pathway activity scores) was provided in your “research data to utilize + graph”.

What I need to build the graphs (choose one)

Expression: a table with rows = samples/cell-lines and columns = LTBR, MDMX, TP53 (plus group labels/conditions).
Mutations: counts per cohort (LTBR/MDMX/TP53 mutated vs wild-type) or VAF distributions.
Proteomics/phospho: relative abundance or phosphorylation metrics across conditions.
Time-course: LTBR/MDMX/TP53 values over time with timepoints and conditions.

Fast path

Paste a small CSV/TSV (even 10–50 rows) or upload a file. Then I will generate Plotly graphs (scatter/line/bar + replicates/CI if present) for LTBR vs MDMX vs p53, including correlations and group comparisons.

If you want, also confirm whether “LTBR” refers to LTβR (LEUP1) and whether “MDMX” means MDM4 (common naming).

Feedback:

Updated: March 25, 2026

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1. MLGCN-Driver introduces a multi-layer graph convolutional network framework that integrates multi-omics gene features with network topology features to identify cancer driver genes, achieving superior AUROC and AUPR across pan-cancer and cancer-type datasets, with ablation analyses confirming the value of multi-layer GCNs, residual connections, identity mappings, and node2vec features. [2025]

8QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

2. This study introduces curation-free biomodules (Cancer Modules) and the FAIME framework to translate individual prostate cancer gene expression profiles into pathway-level mechanisms, demonstrating superior cross-dataset reproducibility and prognostic power for predicting cancer recurrence compared with traditional gene-level analyses. [2013]

8QualityResults Limitations Context Blindspots Methods Sample Conflict

↗ Paper Review ↗ Full Paper

3. This study presents a pre-trained transformer model that accurately predicts the tissue of origin for various cancers using gene expression data, achieving an overall accuracy of 88% across 32 tissue types and demonstrating potential for clinical application in metastatic cases. [2024]

8QualityResults Limitations Context Blindspots Methods Sample Data

↗ Paper Review ↗ Full Paper

4. This study presents a novel algorithm called EPIC that accurately estimates the proportions of cancer and immune cell types from bulk tumor gene expression data, validated through various experimental methods including flow cytometry and single-cell RNA-Seq analyses. [2017]

8QualityResults Limitations Context Blindspots Methods Sample

↗ Paper Review ↗ Full Paper

5. This study identifies a five-gene signature with prognostic value in colorectal cancer, demonstrating its potential as a novel biomarker for predicting patient outcomes based on gene expression profiles from multiple GEO datasets. [2018]

8QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

Keep Exploring

What exact data type do you have for LTBR/MDMX/TP53 (RNA, protein, phospho, mutations, or activity scores), and what are the sample groups/conditions?

Do you want correlation networks among LTBR/MDMX/TP53 across cohorts, or do you want condition-specific differences with effect sizes and confidence intervals?

Should graphs be built per cancer type/cohort (stratified) or pooled (global), and do you have batch labels for harmonization?

Analysis Wizard

You will provide a small numeric dataset containing LTBR, MDMX (MDM4), and TP53 values per sample/condition; the code will parse it, summarize groups, and compute correlations for graphing.

Science Art

Science Movie

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

Discussion

BGPT Bias

I may be biased toward requiring explicit numeric inputs because graphs must be grounded in provided data to avoid speculative visualization.

Stay current without chasing every paper.

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Traceable Claims in Bioinformatics

See what computational analyses actually reported, with provenance for every claim.Know what the science actually supports before you trust the answer.

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3. This study presents a pre-trained transformer model that accurately predicts the tissue of origin for various cancers using gene expression data, achieving an overall accuracy of 88% across 32 tissue types and demonstrating potential for clinical application in metastatic cases. [2024]

4. This study presents a novel algorithm called EPIC that accurately estimates the proportions of cancer and immune cell types from bulk tumor gene expression data, validated through various experimental methods including flow cytometry and single-cell RNA-Seq analyses. [2017]

5. This study identifies a five-gene signature with prognostic value in colorectal cancer, demonstrating its potential as a novel biomarker for predicting patient outcomes based on gene expression profiles from multiple GEO datasets. [2018]

6. This study presents a novel supervised locally linear discriminant embedding (LLDE) method for classifying gene expression data, demonstrating its effectiveness in improving classification accuracy on various cancer datasets compared to traditional methods. [2010]

7. This study employs comprehensive bioinformatic analysis to identify nine candidate genes associated with poor clinical outcomes in colorectal cancer, revealing their potential as prognostic biomarkers and functional insights into CRC progression. [2025]

8. This study identifies SLC4A4 as a potential biomarker for colorectal cancer (CRC) through the analysis of gene expression profiles from multiple microarray datasets, revealing its downregulation correlates with poorer overall survival in CRC patients. [2025]

9. This study identifies 29 molecular biomarkers associated with different pathological stages and prognosis of clear cell renal cell carcinoma (ccRCC) through weighted gene co-expression network analysis of gene expression data from ccRCC and normal kidney samples. [2018]

10. This study presents a novel algorithm called robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) for identifying differentially expressed genes, demonstrating its effectiveness on cancer datasets compared to existing methods. [2017]

11. This study investigates the molecular differences in high-grade serous ovarian cancers (HGSC) associated with BRCA1 and BRCA2 mutations, revealing distinct gene expression profiles and copy number alterations that may influence clinical outcomes. [2013]

Ask a Follow-Up

Keep Exploring

What exact data type do you have for LTBR/MDMX/TP53 (RNA, protein, phospho, mutations, or activity scores), and what are the sample groups/conditions?

Do you want correlation networks among LTBR/MDMX/TP53 across cohorts, or do you want condition-specific differences with effect sizes and confidence intervals?

Should graphs be built per cancer type/cohort (stratified) or pooled (global), and do you have batch labels for harmonization?

Analysis Wizard

You will provide a small numeric dataset containing LTBR, MDMX (MDM4), and TP53 values per sample/condition; the code will parse it, summarize groups, and compute correlations for graphing.

Science Art

Science Movie

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

Discussion

BGPT Bias

I may be biased toward requiring explicit numeric inputs because graphs must be grounded in provided data to avoid speculative visualization.

Stay current without chasing every paper.

My BGPT

Trending

Traceable Claims in Bioinformatics

See what computational analyses actually reported, with provenance for every claim.Know what the science actually supports before you trust the answer.

Explore by Goal

Long Response Copied

Top Data Sources ExportMCP

3. This study presents a pre-trained transformer model that accurately predicts the tissue of origin for various cancers using gene expression data, achieving an overall accuracy of 88% across 32 tissue types and demonstrating potential for clinical application in metastatic cases. [2024]

4. This study presents a novel algorithm called EPIC that accurately estimates the proportions of cancer and immune cell types from bulk tumor gene expression data, validated through various experimental methods including flow cytometry and single-cell RNA-Seq analyses. [2017]

5. This study identifies a five-gene signature with prognostic value in colorectal cancer, demonstrating its potential as a novel biomarker for predicting patient outcomes based on gene expression profiles from multiple GEO datasets. [2018]

6. This study presents a novel supervised locally linear discriminant embedding (LLDE) method for classifying gene expression data, demonstrating its effectiveness in improving classification accuracy on various cancer datasets compared to traditional methods. [2010]

7. This study employs comprehensive bioinformatic analysis to identify nine candidate genes associated with poor clinical outcomes in colorectal cancer, revealing their potential as prognostic biomarkers and functional insights into CRC progression. [2025]

8. This study identifies SLC4A4 as a potential biomarker for colorectal cancer (CRC) through the analysis of gene expression profiles from multiple microarray datasets, revealing its downregulation correlates with poorer overall survival in CRC patients. [2025]

9. This study identifies 29 molecular biomarkers associated with different pathological stages and prognosis of clear cell renal cell carcinoma (ccRCC) through weighted gene co-expression network analysis of gene expression data from ccRCC and normal kidney samples. [2018]

10. This study presents a novel algorithm called robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) for identifying differentially expressed genes, demonstrating its effectiveness on cancer datasets compared to existing methods. [2017]

11. This study investigates the molecular differences in high-grade serous ovarian cancers (HGSC) associated with BRCA1 and BRCA2 mutations, revealing distinct gene expression profiles and copy number alterations that may influence clinical outcomes. [2013]

Ask a Follow-Up

Keep Exploring

What exact data type do you have for LTBR/MDMX/TP53 (RNA, protein, phospho, mutations, or activity scores), and what are the sample groups/conditions?

Do you want correlation networks among LTBR/MDMX/TP53 across cohorts, or do you want condition-specific differences with effect sizes and confidence intervals?

Should graphs be built per cancer type/cohort (stratified) or pooled (global), and do you have batch labels for harmonization?

Analysis Wizard

You will provide a small numeric dataset containing LTBR, MDMX (MDM4), and TP53 values per sample/condition; the code will parse it, summarize groups, and compute correlations for graphing.

Science Art

Science Movie

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

Discussion

BGPT Bias

I may be biased toward requiring explicit numeric inputs because graphs must be grounded in provided data to avoid speculative visualization.

Stay current without chasing every paper.

My BGPT

Trending

Top Data Sources Export MCP