Pith Number

pith:OK3PRUHJ

pith:2026:OK3PRUHJDA7I5E5RJAI5GHJVVW

not attested not anchored not stored refs resolved

High-Order Epistasis Detection Using Factorization Machine with Quadratic Optimization Annealing and MDR-Based Evaluation

Shuta Kikuchi, Shu Tanaka

Factorization machine quadratic annealing recovers ground-truth high-order epistasis by optimizing MDR error rates as a black-box objective.

arxiv:2601.01860 v2 · 2026-01-05 · cs.LG · quant-ph

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Claims

C1strongest claim

The proposed method successfully identified ground-truth epistasis across various interaction orders and the numbers of genetic loci within a limited number of iterations.

C2weakest assumption

That performance on simulated datasets with artificially planted high-order epistasis will translate to real genetic data where interaction patterns are unknown and noise structures differ.

C3one line summary

FMQA optimizes MDR classification error rates to identify predefined high-order epistatic interactions in simulated genetic datasets within limited iterations.

References

29 extracted · 29 resolved · 1 Pith anchors

[1] An introduction to variable and feature selection, 2003

[2] A review of feature selection techniques in bioinformatics, 2007

[3] Research techniques made simple: feature selection for biomarker discovery, 2068

[4] Genetic interactions involving five or more genes contribute to a complex trait in yeast, 2014

[5] Higher-order genetic interactions and their contribution to com- plex traits, 2015

Formal links

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Cited by

1 paper in Pith

Improving FMQA via Initial Training Data Design Considering Marginal Bit Coverage in One-Hot Encoding

Receipt and verification

First computed	2026-05-18T03:09:32.336070Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

72b6f8d0e9183e8e93b14811d31d35ad871dc0865bf7e7f21ba6284356d01122

Aliases

arxiv: 2601.01860 · arxiv_version: 2601.01860v2 · doi: 10.48550/arxiv.2601.01860 · pith_short_12: OK3PRUHJDA7I · pith_short_16: OK3PRUHJDA7I5E5R · pith_short_8: OK3PRUHJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OK3PRUHJDA7I5E5RJAI5GHJVVW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 72b6f8d0e9183e8e93b14811d31d35ad871dc0865bf7e7f21ba6284356d01122

Canonical record JSON

{
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    "abstract_canon_sha256": "2f349249b9269a9d63ec7ae782887365346a9eb7e912ed9cc3cb550ca7941568",
    "cross_cats_sorted": [
      "quant-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-01-05T07:41:34Z",
    "title_canon_sha256": "7f566c354d0f6a13b0a5a6780ffc1e84b482977d818a5f35d53d83ebb75845d8"
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  "source": {
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    "kind": "arxiv",
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