Conference proceeding
An Ensemble Feature Selection Method Based on Deep Forest for Microbiome-Wide Association Studies
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 248
01 Jan 2018
Abstract
Conference Title: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Conference Start Date: 2018, Dec. 3 Conference End Date: 2018, Dec. 6 Conference Location: Madrid, Spain With the rapid advancement of DNA sequencing, metagenomics and metatranscriptomics have made great progress, which deepen our understanding on the human microbiome and its impact on human health and diseases. The microbiome, which is characterized by small samples, high dimensions and complicated relationships with hosts, refers to the species, genes and genomes of the microbiota, as well as the products of the microbiota and the host environment. In fact, many machine learning methods have been used to conduct Microbiome-Wide Association Studies which can link the microbiome with the phenotypes, such as the status of human health and diseases. However, existing methods such as Support Vector Machines (SVMs) have some limitations on deep representation learning with deep architectures which can promote the reuse of features and potentially lead to progressively more abstract features at higher layers of representations. Recently, Deep Neural Networks (DNNs), a kind of deep learning models, are widely used for metagenomic data analysis and can perform well on representation learning. But they are considered as a black box and sufferring from criticisms due to theirs lacking of interpretability. Thus, it is interesting to explore other deep learning models for metagenomic data analysis. In this work, we introduce a deep learning model called Deep Forest to study the microbiome associations and we also present an ensemble method for feature selection. Experimental results show that Deep Forest outperforms the traditional machine learning methods. In addition, compared to DNNs, Deep Forest has better interpretability and less hyperparameters.
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6 Citations (source Web of Science)
Details
- Title
- An Ensemble Feature Selection Method Based on Deep Forest for Microbiome-Wide Association Studies
- Creators
- Qiang Zhu - Drexel UniversityMin PanLei Liu - Supreme Council Of HealthBojing LiTingting He - Central China Normal UniversityXingpeng Jiang - Central China Normal UniversityXiaohua Hu - Drexel University, Information Science (Informatics)
- Publication Details
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings, 248
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Identifiers
- 991019170340604721