Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithm

TL;DR

This paper presents a novel SVM-based framework with N-best algorithm and time series feature extraction for classifying microbial marker genome fragments, achieving high accuracy in species identification.

Contribution

It introduces a new classification framework combining multi-class SVM, N-best algorithm, and time series features for microbial genome fragment recognition.

Findings

Recognition accuracy above 28% in top-1 candidate

Recognition accuracy above 91% in top-10 candidates

Effective classification across multiple species and fragment recognition strategies

Abstract

Microbial clades modeling is a challenging problem in biology based on microarray genome sequences, especially in new species gene isolates discovery and category. Marker family genome sequences play important roles in describing specific microbial clades within species, a framework of support vector machine (SVM) based microbial species classification with N-best algorithm is constructed to classify the centroid marker genome fragments randomly generated from marker genome sequences on MetaRef. A time series feature extraction method is proposed by segmenting the centroid gene sequences and mapping into different dimensional spaces. Two ways of data splitting are investigated according to random splitting fragments along genome sequence (DI) , or separating genome sequences into two parts (DII).Two strategies of fragments recognition tasks, dimension-by-dimension and sequence--by--sequence, are investigated. The k-mer size selection, overlap of segmentation and effects of random split percents are also discussed. Experiments on 12390 maker genome sequences belonging to marker families of 17 species from MetaRef show that, both for DI and DII in dimension-by-dimension and sequence-by-sequence recognition, the recognition accuracy rates can achieve above 28\% in top-1 candidate, and above 91\% in top-10 candidate both on training and testing sets overall.