Welcome to TB-Scope website.
TB-Scope is a host-gene-expression-rank based ensemble machine learning algorithm
for robust diagnosis of pulmonary tuberculosis.
Introduction
Active pulmonary tuberculosis is difficult to diagnose and treatment response is difficult to effectively monitor. The World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been widely applied successfully in clinical settings. This is because batch effects and use of different data types greatly decreased the predictive performance gene expression profile-based signatures having in inter-laboratory and data type dependent validation. To address this problem and provide a more precise tuberculosis diagnosis, we developed TB-Scope, a host-gene-expression-rank based ensemble machine learning algorithm for robust tuberculosis diagnosis.
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)
Host-gene-expression-rank based 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
TB-Scope: Host-gene-expression-rank based integrated ensemble machine learning algorithm for robust diagnosis of pulmonary tuberculosis. Yifei Shen, Wenxin Qu, Fei Yu, Dongsheng Han, Dan Zhang, Enhui Shen, Qinjie Chu, Michael P. Timko, Longjiang Fan, Shufa Zheng, Yu Chen. In progress