life-benchmark-against-the-incumbent

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Use at study design when a life-science method result is about to be reported on its own numbers. Covers the head-to-head against the incumbent tool, the cost table that goes with it, and finding an orthogonal truth set the method was not fitted to.

AI & Automation 804 stars 25 forks Updated today NOASSERTION

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# A life-science method result is a head-to-head, a cost table, and an orthogonal truth set In the life sciences a new method, construct or pipeline is only reportable relative to the incumbent. Before running anything, name the established tool, assay or predictor the field uses today and run it on the same inputs: your primary result is the head-to-head, not your own absolute number. Plan four deliverables. Accuracy head-to-head: incumbent and yours, same metric, in one table and one figure. Separately, report a concordance statistic against the reference implementation or published values (correlation, percent identical calls, median absolute deviation) as evidence that your pipeline is correct, distinct from evidence that it is better. Cost as a scientific result: wall and CPU time, peak memory, and the size or amount of the artefact produced (output files, reagent, input material), measured on identical inputs for every tool x condition cell, with the explicit ratio to each competitor. Orthogonal truth set: score against an experimentally derived ground truth rather than the model's own outputs, with imbalance-aware metrics - precision-recall with AUPRC, recall at a fixed precision, the absolute count of additional true positives - and state the positive rate. Operating point: every score has a stringency knob (probability cut-off, similarity threshold, coverage depth, allele fraction). Sweep it, publish the whole curve with bands derived from replicates and the rep...

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Author
tangxiangru
Repository
tangxiangru/AutoR
Created
5 months ago
Last Updated
today
Language
Python
License
NOASSERTION

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