Evidence map›Paper›PMID 42355320›Full record

ArticleInsects2026

Learning the Structural Diversity of Olfactory Receptors: A Genomic Case Study in Two Longhorn Beetles (Cerambycidae: Lamiinae).

Mataya Duncan, Terrence Sylvester, Emilee Walden, Jenniffer Roa Lozano, Emma Turner, Samuel Duncan, Robert F Mitchell, Duane D McKenna, Rich Adams

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insects, 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

9 authors.

Mataya DuncanDepartment of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA.
Terrence SylvesterDepartment of Biological Sciences, University of Memphis, Memphis, TN 38152, USA.
Emilee WaldenDepartment of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA.
Jenniffer Roa LozanoDepartment of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA.
Emma TurnerDepartment of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA.
Samuel DuncanDepartment of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA.
Robert F MitchellDepartment of Entomology, The Pennsylvania State University, University Park, PA 16802, USA.ORCID 0000-0001-9485-0687
Duane D McKennaDepartment of Biological Sciences, University of Memphis, Memphis, TN 38152, USA.ORCID 0000-0002-7823-8727
Rich AdamsDepartment of Entomology and Plant Pathology, University of Arkansas, Fayetteville, AR 72701, USA.

Funding

U.S. National Science Foundation DEB-2110053; DEB-2529693; MRI-2318210
6 · The paper itself

Abstract

Recent advances in machine learning are transforming biological research by offering powerful tools to address complex challenges across the life sciences. In particular, deep learning approaches now enable accurate predictions of protein structure and function, opening new avenues for investigating proteomic diversity in non-model organisms. In this study, we conducted a genomic case study that examines the predicted structure and diversity of odorant receptor (OR) proteins in two species of longhorn beetles (Cerambycidae) with divergent life histories: the highly specialized red milkweed beetle (

Indexed as

AlphaFoldmolecular evolutionnon-model genomicsolfactionprotein predictionunsupervised clustering

Identifiers

PMID42355320
PMCPMC13299372

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.