From genetic data to kinship clarity: employing machine learning for detecting incestuous relations

It was established that population structure does not affect the model's accuracy and that it can be applied even in isolated populations.

Authors

Institutions

Publication Details

Journal
Frontiers in Genetics
Published
2025-06-02
DOI
https://doi.org/10.3389/fgene.2025.1578581
Citations
1
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
5.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From genetic data to kinship clarity: employing machine learning for detecting incestuous relations

Aleksandra Stefanović, Dušan Keckarević, Dejan Šorgić, Mladen Popović
1 citations
Frontiers in Genetics
Computational and Text Analysis Methods
5.34
article

From genetic data to kinship clarity: employing machine learning for detecting incestuous relations

Aleksandra Stefanović, Dušan Keckarević, Dejan Šorgić, Mladen Popović
article en
1 citations

Abstract

It was established that population structure does not affect the model's accuracy and that it can be applied even in isolated populations.

Frontiers in GeneticsVol. 16
University of Nis (RS), University of Belgrade (RS)
Openalex Percentile: Top 5%
Computational and Text Analysis Methods
5.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.