Evidence map›Paper›PMID 39669208›Full record

ArticleStatistics and computing2025

funBIalign: a hierachical algorithm for functional motif discovery based on mean squared residue scores.

Jacopo Di Iorio, Marzia A Cremona, Francesca Chiaromonte

Abstract read
In one paragraph

Article in Statistics and computing, 2025. 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

3 authors.

Jacopo Di IorioDepartment of Statistics, Penn State University, Joab L. Thomas Building, University Park, 16802 PA USA.
Marzia A CremonaDepartment of Operations and Decision System, Université Laval, 2325 Rue de la Terrasse, Québec, G1V0A6 Québec Canada.
Francesca ChiaromonteDepartment of Statistics, Penn State University, Joab L. Thomas Building, University Park, 16802 PA USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motif discovery is gaining increasing attention in the domain of functional data analysis. Functional motifs are typical "shapes" or "patterns" that recur multiple times in different portions of a single curve and/or in misaligned portions of multiple curves. In this paper, we define functional motifs using an additive model and we propose Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-024-10537-y.

Indexed as

BiclusteringClusteringFunctional data analysisFunctional motif discovery

Identifiers

PMID39669208
PMCPMC11632007

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.