This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology
An improved dimensionality reduction method for meta-transcriptome indexing based diseases classification
1 College of Life Science and Biotechnology, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China
2 Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
3 Shanghai Center for Bioinformation Technology, Shanghai 200235, China
4 Department of Medical Microbiology and Parasitology, Institutes of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200240, China
5 State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, China
6 Department of Cardiology, Gansu Provincial Hospital, Lanzhou 730000, China
BMC Systems Biology 2012, 6(Suppl 3):S12 doi:10.1186/1752-0509-6-S3-S12Published: 17 December 2012
Bacterial 16S Ribosomal RNAs profiling have been widely used in the classification of microbiota associated diseases. Dimensionality reduction is among the keys in mining high-dimensional 16S rRNAs' expression data. High levels of sparsity and redundancy are common in 16S rRNA gene microbial surveys. Traditional feature selection methods are generally restricted to measuring correlated abundances, and are limited in discrimination when so few microbes are actually shared across communities.
Here we present a Feature Merging and Selection algorithm (FMS) to deal with 16S rRNAs' expression data. By integrating Linear Discriminant Analysis method, FMS can reduce the feature dimension with higher accuracy and preserve the relationship between different features as well. Two 16S rRNAs' expression datasets of pneumonia and dental decay patients were used to test the validity of the algorithm. Combined with SVM, FMS discriminated different classes of both pneumonia and dental caries better than other popular feature selection methods.
FMS projects data into lower dimension with preservation of enough features, and thus improve the intelligibility of the result. The results showed that FMS is a more valid and reliable methods in feature reduction.