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Affiliation

Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro (PD), 35020, Italy

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1 Papers

Machine Learning Methods for Phenotype Prediction from High-Dimensional, Low Population Aquaculture Data

Topics: Genomics and Proteomics; Machine Learning Algorithms, Data Mining Techniques and Deep Learning Tools; Algorithms and Software Tools based on Deep Learning or Artificial Intelligence; Deep Learning in Bioinformatics Applications

Authors: Giovanni Faldani ¹ , Enrico Rossignolo ¹ , Eleonora Signor ¹ , Alessio Longo ² , Sara Faggion ² , Luca Bargelloni ² , Matteo Comin ¹ and Cinzia Pizzi ¹

Affiliations: ¹ Department of Information Engineering, University of Padova, Padova, 35131, Italy , ² Department of Comparative Biomedicine and Food Science, University of Padova, Legnaro (PD), 35020, Italy

Digital Object Identifier: 10.5220/0013248000003911

Proceedings of BIOINFORMATICS 2025, ISBN: 978-989-758-731-3

Quick Abstract

Recent research has increasingly focused on classification rules within the big data framework, yet many bioinformatics applications still address prediction problems that involve small-sample, high-dimensional data. In phenotype prediction, especially with the rise of large-scale genomic data, a central challenge arises from handling high-dimensional datasets where the number of genetic features (such as SNPs) far exceeds the sample size. A significant example of such high-dimensional, low-sample datasets is found in aquaculture, a rapidly growing sector within global food production and a crucial source of high-quality protein. This study uses data from an experiment performed on European seabass as a test case, focusing on predicting resistance to Viral Nervous Necrosis (VNN) as a specific phenotype of interest. We explore a range of machine learning techniques to address the complexities of high-dimensional data, from established methods like gradient boosting, SVM, and deep learning to newer approaches. This paper evaluates various methods for associating SNPs with phenotypic traits, benchmarking their performance on challenging aquaculture genomic data to provide insight into the effectiveness of these techniques.