A Pre-Research Article | Data Science, Data Pipelines & Genomic Informatics
Abstract
Paternity testing is one of the most legally and emotionally consequential applications of forensic science. In Kenya, demand for DNA paternity testing has grown sharply over the past decade, with laboratories such as the Bioinformatics Institute of Kenya now processing upwards of 125 cases every month, many feeding directly into court proceedings around child support, custody, and inheritance. Yet the infrastructure underpinning these tests remains largely manual, time-intensive, and constrained by limited laboratory capacity and a near-total absence of a national forensic DNA database.
This article makes the case that machine learning (ML), combined with a well-engineered data pipeline, offers a credible and practical path toward faster, more scalable, and more consistent paternity determination. It surveys the existing research landscape, identifies the specific gaps that remain unaddressed in the African genomic context, and outlines the architecture of a data pipeline that could serve as the foundation for an ML-assisted paternity testing system designed for the Kenyan environment.
1. The Problem: Paternity Disputes in the Kenyan Legal System
Paternity testing accounts for over 90% of all DNA tests conducted in Kenya. Cases span a wide range of contexts: child maintenance and custody battles in family courts, inheritance disputes, immigration documentation, and increasingly, personal verification outside any legal process.
The bottleneck is not awareness; demand is clearly there and growing. The bottleneck is capacity. Until recently, Kenya had only one institution authorised to conduct forensic DNA testing: the Government Chemist, a government-run laboratory that has historically struggled with case backlog. A second public laboratory was later established at the Kenya Medical Research Institute (KEMRI), and a small number of private providers, notably the Bioinformatics Institute of Kenya (BIK) and EasyDNA Kenya, now operate in the market.
Despite this growth, several structural problems persist:
No national forensic DNA database exists. Bodies are disposed of without DNA records. Cold cases cannot be cross-referenced against stored profiles.
Manual interpretation remains the norm. Short Tandem Repeat (STR) profiles are compared by trained analysts, introducing potential for human error and analyst inconsistency.
Turnaround times are slow. Legal-grade tests can take days to weeks, delaying court proceedings.
Population-specific allele frequency data for East African populations is sparse. Most STR allele frequency databases are built on European, East Asian, or American reference panels; which affects the statistical accuracy of paternity index calculations.
2. How DNA Paternity Testing Currently Works
Before discussing machine learning applications, it is worth briefly describing how the current process works.
Modern paternity testing uses Short Tandem Repeats (STRs), sections of the genome where a short sequence of base pairs (the "repeat unit") is repeated a variable number of times from person to person. Because the number of repeats at each STR locus is highly variable across individuals, and because a child inherits one allele at each locus from each parent, comparing STR profiles across child, mother, and alleged father allows analysts to determine whether the father's alleles are present in the child's DNA.
The output of this process is a Combined Paternity Index (CPI), a likelihood ratio that expresses how much more likely it is that the tested man is the biological father versus a random unrelated man from the same population. A CPI above 10,000 (corresponding to a probability of paternity above 99.99%) is typically required for a legal determination.
The standard in Kenya involves 24 genetic markers. The Bioinformatics Institute of Kenya, for example, offers a 24-marker test that it describes as superior to the panels used by most law enforcement laboratories in the country and across Africa.
The weakness in this process is two-fold: it requires skilled human analysts at every step, and the statistical power of the CPI calculation depends on accurate, population-specific allele frequency tables, which, for East African populations, are still being developed.
3. What the Research Says: Machine Learning and DNA Kinship Analysis
A growing body of peer-reviewed research now demonstrates that machine learning can meaningfully contribute to DNA-based kinship and paternity analysis. The work spans several different approaches.
3.1 Deep Neural Networks on STR Data
A 2023 paper published in the Journal of Intelligent Systems proposed replacing manual STR matching with a Deep Neural Network (DNN) trained on 15-locus STR data. The researchers created a synthetic familial dataset, augmented it to increase sample size, and trained a DNN to predict paternity. This was among the first studies to directly position deep learning as a substitute for, rather than a supplement to, manual forensic interpretation.
The paper explicitly acknowledged that in developing countries, conventional kinship analysis techniques result in inadequate accuracy when dealing with large STR datasets, largely because of the human labour required for profile-by-profile comparison.
3.2 Random Forest and SVM on mtDNA Sequences
A study published on PubMed (NIH) applied four machine learning classifiers, Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Random Forest (RF), to mitochondrial DNA hypervariable region I sequences. The data covered African, Asian, and Caucasian samples.
The results were encouraging: a Bag-of-Words + PCA + Random Forest combination achieved 94.4% accuracy in predicting genetic relatedness, outperforming all other configurations. Critically, this study is one of the few to explicitly include African DNA samples, making its findings directly relevant to the Kenyan context.
3.3 SNP-Based Kinship Panels with Supervised ML
A 2024 paper in Expert Systems with Applications introduced a novel panel of 4,849 Single Nucleotide Polymorphisms (SNPs) and applied supervised machine learning to classify kinship relationships across more than 150,000 simulated pairs. The panel was designed to overcome the limitations of STR-based methods for detecting second-degree and more distant relationships.
A key feature of this study was its transparency:
the full codebase was made publicly available on GitHub (
ML to Predict Genetic Relatedness Using Human mtDNA, African, Asian, Caucasian Samples:
Making AI Accessible for Forensic DNA Profile Analysis (bioRxiv, 2025):
NASTRA: Accurate STR Analysis by Nanopore Sequencing (PMC, 2024):
Advanced Paternity DNA Sequence Classification Using Dynamic Programming and ML — Part 2 (Bonat, 2024):
Likelihood ratio model for STR and SNP kinship testing:
Pairwise relatedness from ancient DNA with contamination correction:
Kenya-Specific Context
KEMRI DNA Lab and the Case for a National Forensic Database:
Bioinformatics Institute of Kenya — 24-Marker Paternity Test:
9. Closing Note
This project is an attempt to begin filling that gap. The pipeline described here is a starting point, not an endpoint. The research paper that follows this pre-article will provide a more formal treatment of the methods, a detailed experimental evaluation, and where the results support it, a clear argument for why ML-assisted paternity testing should be considered as a complement to existing forensic laboratory practice in Kenya.
Pre-research article. All research questions and pipeline architecture are prospective. Full methodology and results will be published in the forthcoming research paper.
by Kipngeno Gregory Data and Software Engineer
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