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Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

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Genome-enabled prediction using probabilistic neural network classifiers.

Juan Manuel González-Camacho¹, José Crossa², Paulino Pérez-Rodríguez³

¹Colegio de Postgraduados, Campus Montecillo, Texcoco, México, 056230, México. jmgc@colpos.mx.

|March 10, 2016

Summary

Probabilistic neural networks (PNN) outperformed multi-layer perceptrons (MLP) in classifying individuals within maize and wheat datasets for genomic selection. PNN demonstrated superior accuracy in identifying elite individuals, particularly when traits were categorized into three classes.

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Area of Science:

Genetics and Plant Breeding
Computational Biology
Machine Learning in Agriculture

Background:

Genome-enabled prediction utilizes machine learning models like Multi-layer Perceptron (MLP) and Radial Basis Function Neural Networks (RBFNN).
Accurate classification of individuals into phenotypic classes is crucial for genomic selection strategies.
Evaluating alternative classifiers like Probabilistic Neural Network (PNN) can enhance predictive accuracy.

Purpose of the Study:

To compare the classification performance of MLP and PNN for predicting phenotypic class membership.
To assess the utility of these classifiers in genome-enabled prediction using genomic and phenotypic data.
To determine the optimal number of classes for continuous traits in classification tasks.

Main Methods:

Utilized 16 maize and 17 wheat datasets with varying sample sizes and SNP densities (1.4k and 55k).
Categorized continuous traits into two or three classes (upper, middle, lower) based on percentiles (15-85% and 30-70%).
Evaluated classifier accuracy using Area Under the Receiver Operating Characteristic Curve (AUC) and Area Under the Precision-Recall Curve (AUCpr), optimizing parameters for AUC.

Main Results:

PNN demonstrated higher accuracy than MLP across all 33 datasets for both maize and wheat.
PNN showed superior performance in classifying individuals into upper, middle, and lower phenotypic classes.
Classification into three classes yielded better results than two classes, especially for identifying extreme (upper and lower) individuals.

Conclusions:

PNN is a more accurate classifier than MLP for genomic selection tasks involving phenotypic class prediction.
The use of PNN with Gaussian radial basis functions shows significant promise for identifying elite individuals in genomic selection.
Categorizing traits into three balanced classes generally improves classification accuracy compared to two classes.