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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning

Alfred Ferrer Florensa¹, Jose Juan Almagro Armenteros², Henrik Nielsen³

¹Research Group for Genomic Epidemiology, DTU National Food Institute, Technical University of Denmark, Anker Engelunds Vej 1, 2800 Kongens Lyngby, Denmark.

NAR Genomics and Bioinformatics

|August 19, 2024

Summary

This summary is machine-generated.

Randomly splitting biological data for deep learning can lead to inaccurate model assessments. SpanSeq is a new database partitioning method that prevents data leakage, ensuring reliable generalization in computational biology.

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Area of Science:

Computational biology
Machine learning
Bioinformatics

Background:

Deep learning models are increasingly used in computational biology, but can learn noisy data patterns.
Standard random data splitting for model generalization assessment is often unreliable due to sample similarity.

Purpose of the Study:

To introduce SpanSeq, a novel database partitioning method for machine learning.
To address data leakage issues in biological sequence datasets (genes, proteins, genomes).
To evaluate the impact of data splitting strategies on model assessment and development.

Main Methods:

Developed SpanSeq, a scalable database partition method for biological sequences.
Reproduced the development of two state-of-the-art bioinformatics models.
Analyzed the effects of non-restrained similarity between development and test sets.

Main Results:

Confirmed that random data splitting leads to dubious assessments of model generalization.
Demonstrated that data splitting strategies also impact model development.
SpanSeq effectively prevents data leakage between training and testing sets.

Conclusions:

SpanSeq offers a robust solution for reliable model evaluation in computational biology.
Careful data partitioning is crucial for accurate assessment and development of deep learning models.
The method is applicable to various biological sequence types and scalable.