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Evidence for bioinformatics claims

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     Quick Explanation


    Koina “Throughput/Latency scaling” plots — data missing

    The provided research data includes PTM dataset size and model performance claims, but not any Koina runtime metrics (throughput, latency, batch size, hardware, or concurrency).
    So I visualized the only quantitative “scaling” that is explicitly present: ProteomeTools-PTMs scale (peptides, PSMs, raw files, PTM sets) and zero-shot generalization signals (reported accuracy deltas).



     Long Explanation


    Visualize Data: “Koina Throughput / Latency Scaling” — what we can and can’t plot

    Known from the provided research data: the paper reports large-scale PTM dataset size and accuracy/generalization improvements for a PTM prediction + rescoring pipeline that includes Koina as an inference framework (used for predictions / comparisons).
    Missing: any explicit numeric runtime measurements (e.g., throughput/latency) for Koina under specified hardware/batch/concurrency. Without those raw timing measurements, producing true throughput/latency scaling plots would require inventing data, which I will not do.

    Figure 1 — ProteomeTools-PTMs scale (available “scaling” numbers)

    These counts are explicitly stated for the synthetic training/evaluation resource: 977,000 synthesized peptides, ~36M PSMs, 3,945 raw files, and 33 PTM packages/packages.

    Figure 2 — Peptide composition by labeling/modified status (explicit counts)

    The paper’s provided dataset composition (TMT-labeled modified: 300k; unlabeled modified: 377k; dimethyl-labeled: 250k; unmodified: 50k) is explicitly listed.

    Figure 3 — Reported “unseen PTM” generalization gains (accuracy signals provided)

    The paper reports approximate iRT accuracy improvements on unseen PTMs (~30%) and FII accuracy increases (~12%). These are not runtime metrics; they are predictive-performance deltas.

    Figure 4 — Encoding choice: reported “gain-loss” performance on unseen PTMs

    The paper states gain-loss encoding is the most reliable for zero-shot predictions on unseen PTMs and gives an example SA (~0.87) and ΔiRT95 (~6.1). Again: these are accuracy/fit metrics, not throughput/latency.
    Critical note: SA and ΔiRT95 are reported as evaluation metrics; without the paper’s full table(s) for all encodings and all unseen PTMs, we can only visualize the specific numeric examples provided.

    What you need to get true Koina throughput/latency scaling plots

    To build real throughput vs latency scaling curves, we must have raw timing data (e.g., per batch) such as:
    • Latency: mean/median/p95 wall time per request/batch.
    • Throughput: peptides/sec (or spectra/sec) under each load.
    • Independent variables: batch size, number of concurrent workers, device type, CPU threads/GPU model, and fragmentation mode setting.
    • Scale range: multiple batch sizes/concurrency levels to establish curves.
    The current research text you provided does not include those timing numbers; therefore I can’t credibly plot them.

    Limitations / epistemic humility (why these are not “latency scaling” plots)

    • Known/unknown separation: We only plotted scaling quantities explicitly stated (dataset sizes) and accuracy signals explicitly stated; no runtime measurements were provided, so no throughput/latency curves were generated.
    • Publication-stage caveat: The provided source is a preprint (bioRxiv-style DOI prefix), so numbers may evolve with peer review; the figure values that are directly stated remain the only safe plotted claims.
    • Potential missing bias: Performance gains measured on certain evaluation splits may not transfer to other instruments/fragments not covered by the training data; the paper itself notes limitations such as reliance on ProteomeTools-derived synthetic data and possible generalization gaps.


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    Updated: April 28, 2026

     Analysis Wizard



    It ingests Koina timing logs, computes peptides/sec and p50/p95 latency per condition, fits scaling models, and renders throughput–latency curves plus elbow-point detection using the provided raw measurements.



     Hypothesis Graveyard


    Assuming linear scaling of latency inversely with throughput (perfect parallelism) is unlikely because most ML inference pipelines have fixed overheads and/or memory bandwidth limits; without timing data this remains speculative and unsupported by the provided research excerpt.


    Attributing any runtime differences to PTM prediction accuracy improvements would be a category error: the provided source discusses predictive metrics, not operational timing, so runtime attribution cannot be made from it.

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