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Bioinformatics claims and pipelines

See claims tied to computational experiments, including reported metrics, parameters, and source provenance for reproducibility checks.Know what the science actually supports before you trust the answer.

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     Quick Analysis Plan


    Compare disruption efficacy
    From your raw set, we can quantify β€œefficacy” as (i) performance loss (e.g., punch velocity/force), (ii) kinematic variability increase (CV%), and (iii) 2D↔3D agreement reliability under disruption, then rank disruption regimes and methods using effect sizes + uncertainty.
    Grounding: MMA disruption/perturbation conditions with reported velocity/force + joint-variability shifts and 2D↔3D equivalence margin.



     Long Analysis Plan


    Analyze Data: Compare Disruption Efficacy
    Goal: define disruption β€œefficacy” metrics, normalize them to comparable scales, estimate effect sizes with uncertainty, and produce a ranked comparison across disruption regimes.
    Primary measurable evidence in your dataset: MMA Normal vs Balance Disruption vs Fatigue shows quantifiable changes in punch velocity/impact force and joint-angle variability (CV%) plus 2D↔3D agreement under those conditions.
    1) What β€œefficacy” means (operationalization)
    Use a multi-metric definition and do not collapse to a single score until you see tradeoffs.
    • Efficacy metric A β€” Performance degradation: changes in punch velocity and peak impact force under disruption (fatigue is explicitly reported as degrading both).
    • Efficacy metric B β€” Control loss / variability: increase in joint-angle coefficient of variation (CV%) for shoulder and elbow.
    • Efficacy metric C β€” Measurement reliability under disruption: ensure the β€œdisruption effect” isn’t just measurement noise. Use reported 2D↔3D agreement statistics and equivalence margin.
    2) Visuals first: compare variability shifts (CV%)
    Using only the explicitly reported CV% endpoints (Normal vs Fatigue) for shoulder and elbow angles.
    3) Ranked comparison: variability-dominance vs measurement reliability
    Decision logic you can implement:
    1. Compute effect sizes for CV% changes where endpoints exist. Here, report Ξ”CV% = fatigue βˆ’ normal, with directional interpretation.
    2. Verify that the measurement modality is stable enough that changes aren’t artifacts. The MMA paper reports mean 2D–3D angular differences within an equivalence margin (Β±1.5Β°) and high ICC/correlation across conditions.
    3. Only then compare efficacy across disruption regimes (Normal vs Balance Disruption vs Fatigue). If numeric CV% endpoints for Balance Disruption are missing, keep it as qualitative / partially supported unless raw data are provided.
    4) Data table (what you can directly compare with the provided extracted values)
    Metric Normal Fatigue Direction Evidence
    Shoulder CV% (joint variability) 4.1% 6.3% ↑ +2.2 points
    Elbow CV% (joint variability) 3.9% 6.8% ↑ +2.9 points
    Punch velocity (performance) Baseline (Normal) Decreased under Fatigue ↓
    Peak impact force (performance) Baseline (Normal) Decreased under Fatigue ↓
    2D↔3D angular agreement (measurement reliability) Mean diff within equivalence margin Still within Β±1.5Β° margin (~0.68°±0.14Β°) Stable measurement
    5) Coding/analysis plan (rigorous, skeptical, evidence-based)
    Inputs to load
    • MMA raw kinematic/force dataset (publicly available at Zenodo referenced in the paper).
    Step A β€” Recreate disruption efficacy metrics
    1. Filter trials by punch type (straight vs swing) only if the raw dataset supports it; otherwise use the paper’s aggregated results.
    2. Compute for each athlete and condition:
      • mean velocity and peak impact force
      • joint-angle CV% for shoulder and elbow in the sagittal plane (as defined in the paper’s processing pipeline).
    3. Efficacy effect sizes: use within-subject differences (Fatigueβˆ’Normal; BalanceDisruptionβˆ’Normal). Prefer paired comparisons (since design is within-subject) and report effect magnitude + CI.
    Step B β€” Separate β€œtrue disruption” from β€œmeasurement instability”
    1. Recompute 2D↔3D agreement on the subset of trials under each disruption condition. Use equivalence testing or at minimum report mean angular difference relative to the stated Β±1.5Β° equivalence margin.
    2. If reliability drops under fatigue, down-weight kinematic metrics or reframe efficacy as β€œperformance impact not attributable to measurement noise.” The paper already reports stability and high ICC/correlation, but your code should verify on the raw data.
    Step C β€” Final ranking with transparency
    1. Create a multi-objective ranking: - β€œworse performance” (velocity ↓, force ↓) - β€œmore variability” (CV% ↑) - β€œdoes not break measurement” (agreement remains within margin)
    2. If you must collapse to one number, do it as an explicit weighted score with sensitivity analysis. Do not hide this choice.
    6) Limitations & counterpoints (what could mislead your comparison)
    • The MMA evidence here reports explicit numeric shifts for fatigue CV% but not full numeric endpoints for balance disruption in your extracted summary; your code should treat balance disruption as under-quantified unless raw data provide those values.
    • Generalizability: the MMA study uses elite, male, right-handed athletes in a lab; disruption effects may differ in other populations/tasks.
    • Causality is within a controlled design, but β€œefficacy” is task-dependent; performance and variability can trade off (e.g., a disruption may reduce variability but also reduce force depending on how it constrains movement).
    • Measurement model bias: 2D vs 3D reliability is reported as high, but your analysis must ensure agreement remains within equivalence margin for each disruption condition and for both joints.
    7) Optional expansion (only if you want cross-domain disruption comparison)
    If your intent is broader than MMA (e.g., compare disruption efficacy across biofilm-phage disruption, protein release disruption, or chemical/biological disruption), you can add additional papers from your datasetβ€”but only if the extracted data provide comparable numeric efficacy endpoints.
    • Phage biofilm disruption exists in your dataset, but the provided extract is largely qualitative for disruption magnitude (biofilm mass reduced; cocktail less effective for single-species yet effective in dual-species); numeric effect sizes are not included in your extracted snippet, so it can’t yet be ranked quantitatively without raw assay outputs.
    • Ultrasonication disruption includes a quantitative release ratio and peak-time yield in your extract for HBcAg release, which could be compared to other disruption regimes if you define β€œefficacy” as release yield.


    Feedback:    

    Updated: April 29, 2026



     Top Data Sources ExportMCP


     Analysis Wizard



    Computes within-subject effect sizes for fatigue vs normal (CV%, velocity, force) from MMA raw data, then validates 2D↔3D agreement per condition using the reported equivalence margin.



     Hypothesis Graveyard


    The disruption ranking is driven purely by measurement error (i.e., when measurement equivalence breaks down). This is less plausible given the reported high 2D–3D agreement and mean angular differences within Β±1.5Β° across conditions, but it should still be rechecked on raw data.


    Balance disruption and fatigue have identical efficacy on performance and variability. This is contradicted by the extract emphasizing fatigue-specific degradation (velocity/force) and CV% increases, though balance disruption should be quantified from raw data to fully falsify.

     Science Art


    Analyze Data: Compare Disruption Efficacy Science Art

     Science Movie


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