Dataset Information

Classification of metagenomics data at lower taxonomic level using a robust supervised classifier.

ABSTRACT: As more and more completely sequenced genomes become available, the taxonomic classification of metagenomic data will benefit greatly from supervised classifiers that can be updated instantaneously in response to new genomes. Currently, some supervised classifiers have been developed to assess the organism of metagenomic sequences. We have found that the existing supervised classifiers usually cannot discriminate the training data from different classes accurately when the data contain some outliers. However, the training genomic data (bacterial and archaeal genomes) usually contain a portion of outliers, which come from sequencing errors, phage invasions, and some highly expressed genes, etc. The outliers, treated as noises, prohibit the development of classifiers with better prediction accuracy. To solve the problem, we present a robust supervised classifier, weighted support vector domain description (WSVDD), which can eliminate the interference from some outliers for training genomic data and then generate more accurate data domain descriptions for each taxonomic class. The experimental results demonstrate WSVDD is more robust than other classifiers for simulated Sanger and 454 reads with different outlier rates. In addition, in experiments performed on simulated metagenomes and real gut metagenomes, WSVDD also achieved better prediction accuracy than other classifiers.

SUBMITTER: Hou T

PROVIDER: S-EPMC4309676 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Classification of metagenomics data at lower taxonomic level using a robust supervised classifier.

Hou Tao T Liu Fu F Liu Yun Y Zou Qing Yu QY Zhang Xiao X Wang Ke K

Evolutionary bioinformatics online 20150126

As more and more completely sequenced genomes become available, the taxonomic classification of metagenomic data will benefit greatly from supervised classifiers that can be updated instantaneously in response to new genomes. Currently, some supervised classifiers have been developed to assess the organism of metagenomic sequences. We have found that the existing supervised classifiers usually cannot discriminate the training data from different classes accurately when the data contain some outl ...[more]

PMID: 25673967

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Project description:Over recent decades, machine learning, an integral subfield of artificial intelligence, has revolutionized diverse sectors, enabling data-driven decisions with minimal human intervention. In particular, the field of educational assessment emerges as a promising area for machine learning applications, where students can be classified and diagnosed using their performance data. The objectives of Diagnostic Classification Models (DCMs), which provide a suite of methods for diagnosing students' cognitive states in relation to the mastery of necessary cognitive attributes for solving problems in a test, can be effectively addressed through machine learning techniques. However, the challenge lies in the latent nature of cognitive status, which makes it difficult to obtain labels for the training dataset. Consequently, the application of machine learning methods to DCMs often assumes smaller training sets with labels derived either from theoretical considerations or human experts. In this study, the authors propose a supervised diagnostic classification model with data augmentation (SDCM-DA). This method is designed to utilize the augmented data using a data generation model constructed by leveraging the probability of correct responses for each attribute mastery pattern derived from the expert-labeled dataset. To explore the benefits of data augmentation, a simulation study is carried out, contrasting it with classification methods that rely solely on the expert-labeled dataset for training. The findings reveal that utilizing data augmentation with the estimated probabilities of correct responses substantially enhances classification accuracy. This holds true even when the augmentation originates from a small labeled sample with occasional labeling errors, and when the tests contain lower-quality items that may inaccurately measure students' true cognitive status. Moreover, the study demonstrates that leveraging augmented data for learning can enable the successful classification of students, thereby eliminating the necessity for specifying an underlying response model.

Dataset Information

Classification of metagenomics data at lower taxonomic level using a robust supervised classifier.

Publications

Classification of metagenomics data at lower taxonomic level using a robust supervised classifier.

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