Free alignment classification of dikarya fungi using some machine learning methods

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Abstract

Gene clustering based on amino acid sequence similarity has been one of the most important problems and always challenging in molecular biology. The most conventional methods are based on alignment-technique. These methods cannot identify and classify sequences, especially when the lengths of sequence are long and unequal. Therefore, in order to classify fungal hexosaminidase amino acid sequences and put them in the right taxonomical group we evaluate the feasibility of computational free alignment methods based on machine learning classifiers such as SVM, KNN, SOM and ensemble technique. The classifiers have appropriately categorized large Dikarya hexosaminidase amino acid sequences as data sets according to their taxonomical groups in two phyla named, the “Ascomycota” and the “Basidiomycota”. Two statistical methods including paired t test and PCA were used for the feature selection and reduce the dimensionality of the features, respectively. Seven classifier performance metrics, randomized complete block design, pairwise Tukey’s honestly significant difference tests and the technique for order preference by similarity to ideal solution with modified k-fold cross validation have been used as tools in order to evaluate and ranking of classifiers. In this study, the effect of training data size on the classifier performance was investigated. The results showed that the rank and the performance of classifiers were depended on the training data size. The highest obtained values for the average overall accuracy of the following training data sizes, 80, 60, 40 and 20% using KNN, KNN, ensemble and ensemble classifier were 96.96, 95.81, 94.47 and 92.47%, respectively.

Keywords

Fungal hexosaminidase Dikarya Classification Classifier 

Abbreviations

ANN

Artificial neural network

ANOVA

Analysis of variance

ARB

Adaptive rule-based

AUC

Area under an ROC curve

DNA

Deoxyribonucleic acid

Ens

Ensemble classifier

FH

Fungal hexosaminidases

FN

Number of positive samples

FP

Number of negative samples

HSD

Honestly significant difference

KNN

K-nearest neighbor

MCC

Matthew’s correlation coefficient

MCDM

Multi-criteria decision-making

MLP

Multilayer perceptron

NB

Naïve Bayes

PC

Principal component

PCA

Principal component analysis

PNN

Probability neural network

Poly2

Polynomial degree 2

Poly3

Polynomial degree 3

PSO

Particle swarm optimization

RBF

Radial basic function

RCBD

Randomized complete block design

RF

Random forest

SOFM

Self-organizing feature map

SOM

Self-organized map

SST

Total sum of squares

SSW

Within-groups sum of squares

SVM

Support vector machine

TDS

Training data size

TLCF

Two-layer classification framework

TN

Negative samples

TOPSIS

Technique for order preference by similarity to ideal solution

TP

Number of positive samples

YI

Youden’s index

Notes

Acknowledgements

Financial support from the vice president for research and technology of Ferdowsi University of Mashhad, is highly appreciated.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Biosystems Engineering, Faculty of AgricultureFerdowsi University of MashhadMashhadIran
  2. 2.Department of Plant Protection, Faculty of AgricultureFerdowsi University of MashhadMashhadIran

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