BGPT: Create Tables: ELECTRA-DTA Refined Metrics Across KIBA/Davis-BindingDB

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Quick Explanation Copied

I can’t yet create the “ELECTRA-DTA Refined Metrics Across KIBA/Davis-BindingDB” tables.

You didn’t provide the actual ELECTRA-DTA evaluation outputs/metrics (e.g., per-dataset RMSE, MAE, Pearson, Spearman, splits, refinement definitions, or the raw prediction files). Without those numbers, any “refined metrics” table would be fabricated—which I won’t do.

Upload your CSV/JSON logs (or the paper’s result tables) and I’ll convert them into rigorous DataTables + Plotly charts for KIBA, Davis, and BindingDB.

Long Explanation

Create Tables: ELECTRA-DTA Refined Metrics Across KIBA/Davis–BindingDB

Data required before I can compute & visualize the refined metrics (no fabrication).

What I’m missing (so I can’t produce real tables yet)

Refinement definition: what exactly was “refined” (new split? filtering? deduping? calibration? re-ranking? aggregation rule across runs?).
Raw predictions or metric outputs per dataset (KIBA, Davis, BindingDB) and per split.
Consistency metadata: which folds/splits, whether targets are transformed, and how missing values are handled.

If you upload prediction files (y_true, y_pred, split id) or result tables (RMSE/MAE/Pearson/Spearman + N), I will generate:

One rigorous DataTable summarizing refined metrics across datasets
Plotly comparisons (bar charts + distribution/uncertainty if multiple runs)
Checks for metric computation consistency (e.g., correlation computed on same scale)

Table template area (will populate only after you provide metrics)

Dataset	Metric (refined)	Value	Units / scale	Split / setting	N	Notes on refinement
Upload metrics/predictions to populate this table.

Plotly figure slots (render after data is provided)

Critical sanity checks I will run once you provide the numbers

Unit/scale alignment: ensure all metrics refer to the same target transformation.
Split fidelity: confirm KIBA/Davis/BindingDB evaluation uses identical split logic for predictions.
Aggregation rule: if multiple runs/folds exist, verify how refined metrics were aggregated.
Correlation calculation: Pearson/Spearman must be computed on matching vectors and the same filtered set.

Note: I did not compute any “refined metrics” because the underlying metric values/predictions were not provided in your message.

Feedback:

Updated: May 01, 2026

Key Insight

In DTA benchmarking, the hardest part is not plotting—it’s making the evaluation vectors exactly match across refinement steps (filters/splits/transformations), or correlations and errors become incomparable.

Keep Exploring

What refinement definition was applied in ELECTRA-DTA (filtering, split changes, calibration, or aggregation), and how can we verify it matches the reported evaluation vectors?

Can we quantify whether refined gains come from reduced test-set noise (variance reduction) versus improved ranking/calibration (mean shift) using per-fold breakdowns?

Analysis Wizard

Once you upload per-sample y_true/y_pred (or metric tables), it will compute refined RMSE/MAE and correlations per dataset/split, then format them into a unified table for Plotly visualization.

Hypothesis Graveyard

Assuming refined metrics are always comparable across datasets without checking split/filter definitions is likely wrong; metric inflation/deflation can occur purely from altered evaluation populations.

Assuming refined metrics were computed from the same target scale across KIBA/Davis/BindingDB is unjustified; mismatched transformations can masquerade as “model improvement.”

Potential Experiments

Provide two prediction files per dataset (pre-refinement and refined) and compute: MAE/RMSE + Pearson/Spearman on (i) the original test set and (ii) the refined subset to isolate whether gains come from model change vs population change.

For each dataset split id, compute per-fold metrics and plot fold-variance for refined vs unrefined; test whether refinement mainly reduces variance (noise filtering) or increases mean performance (true generalization).

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Science Movie

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Discussion

BGPT Bias

I’m biased toward refusing to fabricate missing benchmark numbers; this may delay output but preserves scientific integrity.

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