Evidence map›Paper›PMID 42370286›Full record

ArticleResearch square2026

Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.

Shipeng Ren, Guoqing Yang, Deyin Yu, Anqi Wang, Qiwei Li, Sijia Zhang, Chao Li

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shipeng RenDalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning Province, P. R. China.
Guoqing YangDalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning Province, P. R. China.
Deyin YuDalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning Province, P. R. China.
Anqi WangDalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning Province, P. R. China.
Qiwei LiDalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning Province, P. R. China.
Sijia ZhangDalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning Province, P. R. China.
Chao LiDalian Key Laboratory of Smart Fisheries, Dalian 116023, Liaoning Province, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Discovering meaningful feature (molecule) combinations to define simple, accurate, and easily interpretable decision rules for disease classification and prediction can improve the study of disease diagnosis and prognosis. However, the computational time complexity of constructing feature combinations for each feature pair in existing methods is often problematic or prohibitive, as the number of features is often in the order of tens of thousands. To significantly reduce the computational cost and maintain the classification performance, this paper proposed a novel acceleration algorithm and a new omics data analysis method based on pseudo kernel functions (PKF-

Indexed as

Computational costFeature combinationKernel functionOmics data analysisPathways in cancer

Identifiers

PMID42370286
PMCPMC13308403

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.