Welcome to InfectDiagno website.
InfectDiagno is a host gene expression based ensemble machine learning algorithm
for robust DIAGNOsis of acute bacterial and viral INFECTions.
Introduction
Early and accurate diagnosis of infection is key to improving patient outcomes and reducing antibiotic resistance. While host gene expression profiling holds great potential as an approach to infection diagnosis, previously developed protocols using multiple diagnostic signatures for host gene expression-based infection diagnosis have not been widely applied successfully because batch effects and different data types greatly decreased the predictive performance gene expression profile based signatures in inter-laboratory and data type dependent validation. To address this problem and assist in more precise infection diagnosis, we developed a rank-based ensemble machine learning algorithm for infection diagnosis (InfectDiagno) based on host gene expression patterns.
Two data types
RNA-Seq
RNA-Seq (named as an abbreviation of RNA sequencing) is a sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analyzing the continuously changing cellular transcriptome. (from Wikipedia)
Microarray
A microarray is a multiplex lab-on-a-chip. Its purpose is to simultaneously detect the expression of thousands of genes from a sample (e.g. from a tissue). It is a two-dimensional array on a solid substrate that assays large amounts of biological material using high-throughput screening miniaturized, multiplexed and parallel processing and detection methods. (from Wikipedia)
rank-based ensemble learning algorithm
TSP (Top Scoring Pairs) classifier was introduced by Geman et al. for the classification of gene expression data based entirely on relative gene expression values, specifically pairwise comparisons between two gene expression levels.
CITE US
InfectDiagno: Robust diagnosis of acute bacterial and viral infections via host gene expression based ensemble machine learning algorithm. Yifei Shen, Dongsheng Han, Wenxin Qu, Fei Yu, Dan Zhang, Enhui Shen, Qinjie Chu, Michael P. Timko, Longjiang Fan, Shufa Zheng, Yu Chen. In progress