Evidence map›Paper›PMID 39991436›Full record

ArticleMethodsX2025

Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance.

Komi Mensah Agboka, Elfatih M Abdel-Rahman, Daisy Salifu, Brian Kanji, Frank T Ndjomatchoua, Ritter A Y Guimapi, Sunday Ekesi, Landmann Tobias

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MethodsX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

8 authors.

Komi Mensah AgbokaInternational Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Kenya.
Elfatih M Abdel-RahmanInternational Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Kenya.
Daisy SalifuInternational Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Kenya.
Brian KanjiInternational Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Kenya.
Frank T NdjomatchouaDepartment of Plant Sciences, School of the Biological Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom.
Ritter A Y GuimapiBiotechnology and Plant Health Division, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, Ås NO-1431, Norway.
Sunday EkesiInternational Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Kenya.
Landmann TobiasInternational Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772 00100, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study introduces a hybrid approach that combines unsupervised self-organizing maps (SOM) with a supervised convolutional neural network (CNN) to enhance model accuracy in vector-borne disease modeling. We applied this method to predict insecticide resistance (IR) status in key malaria vectors across Africa. Our results show that the combined SOM/CNN approach is more robust than a standalone CNN model, achieving higher overall accuracy and Kappa scores among others. This confirms the potential of the SOM/CNN hybrid as an effective and reliable tool for improving model accuracy in public health applications.•The hybrid model, combining SOM and CNN, was implemented to predict IR status in malaria vectors, providing enhanced accuracy across various validation metrics.•Results indicate a notable improvement in robustness and predictive accuracy over traditional CNN models.•The combined SOM/CNN approach demonstrated higher Kappa scores and overall model accuracy.

Indexed as

Combining Self-Organizing Maps (SOM) and Convolutional Neural Network (CNN) for improving model accuracyData scienceDecision makingInsecticide resistanceUnsupervised and supervised learning

Identifiers

PMID39991436
PMCPMC11847465

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

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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.