BGPT: Create Tables: SpaMV Metrics Per Dataset (ARI,NMI,HOM,Vmeasure, Jaccard)

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SpaMV Metrics Per Dataset (ARI, NMI, HOM, V-measure, Jaccard)

Critical check: In the provided research data, I can only see clustering metric names for SpatialFusion (e.g., “SDMBench metrics (ARI, NMI, HOM, COM, PAS, CHAOS)”), but the actual numeric values for ARI, NMI, HOM, V-measure, and Jaccard are not included per dataset/dataset-split.

Therefore, I can’t truthfully “fill” a metrics table without inventing numbers. Below is a structure-complete table showing what’s known vs. missing from your supplied content.

Table: SpaMV metrics per dataset (values not present in provided data)

Dataset	ARI	NMI	HOM	V-measure	Jaccard	Evidence status (from provided text)
Not specified in provided text as “SpaMV dataset”	—	—	—	—	—	Metric names mentioned, but dataset-level numbers not provided
SpatialFusion benchmark datasets (mentioned): OVCA Xenium, BRCA Xenium, Visium HD (CRC/NSCLC/PRAD)	—	—	—	—	—	ARI/NMI/HOM listed as SDMBench metrics, but numeric values for SpaMV-style table not given in supplied content

What I need from you to complete the table: either (a) the SpaMV results block (per dataset) containing ARI/NMI/HOM/V-measure/Jaccard, or (b) the exact paper table/figure values. If you paste the numeric results, I will immediately produce the fully-populated per-dataset SpaMV metrics table.

If you confirm what “SpaMV” refers to, I’ll map datasets correctly

The provided content includes multiple spatial/omics ML papers, but I don’t see an explicit definition of “SpaMV” nor numeric ARI/NMI/HOM/V-measure/Jaccard per dataset.

Please tell me one of the following (any single item is enough):

Which paper/method name exactly corresponds to SpaMV, or a DOI/title/link.
Whether “SpaMV metrics” are the same as the SDMBench niche rediscovery metrics (ARI/NMI/HOM, etc.) for SpatialFusion.
Paste the table/figure values (even as plain text) and I’ll format them into the requested metrics table.

Feedback:

Updated: April 29, 2026

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1. Domain-specific fine-tuning of BERT embeddings within the WEClustering++ framework yields substantial improvements in clustering quality for large text datasets across seven benchmarks, evidenced by higher silhouette scores, purity, and ARI. [2025]

7QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

2. A lightweight multimodal foundation model, SpatialFusion, integrates paired histopathology and spatial transcriptomics with pathway-activation signals to define biologically coherent spatial niches, outperform multiple baselines, and generalize across unseen tissues and technologies while linking niche architecture to cancer progression. [2026]

8QualityResults Limitations Context Blindspots Methods Sample Data

↗ Paper Review ↗ Full Paper

3. GIMIC introduces a smoothed graph-image representation of microbiome samples that yields an interpretable distance metric (MSE/SAM) outperforming existing beta-diversity measures across multiple cohorts, enabling cross-cohort visualization, clustering, and meta-analysis of microbiome–phenotype associations. [2025]

8QualityResults Limitations Context Blindspots Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

4. The study presents a pipeline that converts 3D gene expression profiles into 2D contour images, learns sparse autoencoder embeddings from these contours, and applies a novel Threshold-Adaptive Hierarchical Clustering (TAHC) with cosine similarity to identify co-expressed genes in the Percellome mouse dataset, demonstrating improved clustering behavior and biologically meaningful enrichment. [2025]

8QualityResults Limitations Methods Sample Conflict Data

↗ Paper Review ↗ Full Paper

5. SpotDiff introduces a multi-modal diffusion-based imputation framework for spatial transcriptomics that fuses spot-gene textual prompts with integrated scRNA-seq data to accurately recover spatiotemporal gene expression across six benchmark datasets, outperforming existing methods in accuracy and biologically meaningful recovery. [2025]

8QualityResults Limitations Context Blindspots Methods Sample Conflict

↗ Paper Review ↗ Full Paper

Keep Exploring

Which exact paper/method is named “SpaMV”, and do its authors report V-measure and Jaccard alongside ARI/NMI/HOM?

What dataset identifiers/splits does SpaMV evaluate on, and are they consistent with the SDMBench/SpatialFusion benchmarking setup?

Analysis Wizard

It ingests SpaMV per-dataset ARI/NMI/HOM/V-measure/Jaccard values you provide, validates numeric ranges, and outputs a publication-ready table for downstream comparative analysis across datasets.

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Science Movie

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

Discussion

BGPT Bias

I default to strict non-invention, so I leave metrics blank when the provided raw data doesn’t include numeric values.

Stay current without chasing every paper.

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Traceable Claims in Bioinformatics

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SpaMV Metrics Per Dataset (ARI, NMI, HOM, V-measure, Jaccard)

Table: SpaMV metrics per dataset (values not present in provided data)

If you confirm what “SpaMV” refers to, I’ll map datasets correctly

Top Data Sources Export MCP

1. Domain-specific fine-tuning of BERT embeddings within the WEClustering++ framework yields substantial improvements in clustering quality for large text datasets across seven benchmarks, evidenced by higher silhouette scores, purity, and ARI. [2025]

6. BatchBench is a modular Nextflow-based pipeline that benchmarks eight single-cell RNA-seq batch-correction methods across three well-studied datasets, evaluating integration via entropy metrics, downstream analyses (clustering, marker genes), and resource usage to guide method choice. [2021]

9. Bio-kNN introduces clustering-based triplet selection and a CNN-based multi-head network to markedly improve biological sequence kNN search by preserving distance orders in embedding space, achieving state-of-the-art results on UniProt and UniRef without additional training cost. [2024]

12. A diabetes prediction pipeline combining Boruta feature selection, K-Means++ clustering, and stacking ensemble learning achieves high predictive accuracy on the PIMA Indian Diabetes Dataset and validates on a Bangladesh-based dataset, outperforming several baselines. [2023]

Ask a Follow-Up

Keep Exploring

Which exact paper/method is named “SpaMV”, and do its authors report V-measure and Jaccard alongside ARI/NMI/HOM?

What dataset identifiers/splits does SpaMV evaluate on, and are they consistent with the SDMBench/SpatialFusion benchmarking setup?

Analysis Wizard

It ingests SpaMV per-dataset ARI/NMI/HOM/V-measure/Jaccard values you provide, validates numeric ranges, and outputs a publication-ready table for downstream comparative analysis across datasets.

Science Art

Science Movie

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

Discussion

BGPT Bias

I default to strict non-invention, so I leave metrics blank when the provided raw data doesn’t include numeric values.

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

SpaMV Metrics Per Dataset (ARI, NMI, HOM, V-measure, Jaccard)

Table: SpaMV metrics per dataset (values not present in provided data)

If you confirm what “SpaMV” refers to, I’ll map datasets correctly

Top Data Sources ExportMCP

1. Domain-specific fine-tuning of BERT embeddings within the WEClustering++ framework yields substantial improvements in clustering quality for large text datasets across seven benchmarks, evidenced by higher silhouette scores, purity, and ARI. [2025]

6. BatchBench is a modular Nextflow-based pipeline that benchmarks eight single-cell RNA-seq batch-correction methods across three well-studied datasets, evaluating integration via entropy metrics, downstream analyses (clustering, marker genes), and resource usage to guide method choice. [2021]

9. Bio-kNN introduces clustering-based triplet selection and a CNN-based multi-head network to markedly improve biological sequence kNN search by preserving distance orders in embedding space, achieving state-of-the-art results on UniProt and UniRef without additional training cost. [2024]

12. A diabetes prediction pipeline combining Boruta feature selection, K-Means++ clustering, and stacking ensemble learning achieves high predictive accuracy on the PIMA Indian Diabetes Dataset and validates on a Bangladesh-based dataset, outperforming several baselines. [2023]

Ask a Follow-Up

Keep Exploring

Which exact paper/method is named “SpaMV”, and do its authors report V-measure and Jaccard alongside ARI/NMI/HOM?

What dataset identifiers/splits does SpaMV evaluate on, and are they consistent with the SDMBench/SpatialFusion benchmarking setup?

Analysis Wizard

It ingests SpaMV per-dataset ARI/NMI/HOM/V-measure/Jaccard values you provide, validates numeric ranges, and outputs a publication-ready table for downstream comparative analysis across datasets.

Science Art

Science Movie

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

Discussion

BGPT Bias

I default to strict non-invention, so I leave metrics blank when the provided raw data doesn’t include numeric values.

Stay current without chasing every paper.

My BGPT

Trending

Top Data Sources Export MCP