Introduction of research team and methods of InfectDiagno.
Team
Prof. Longjiang Fan
Executive Director, Institute of Bioinformatics, Zhejiang University
PI, Air Pollution and Health Research Center, Zhejiang University
Professor, Department of Medical Oncology,
the First Affiliated Hospital, Zhejiang University
Prof. Yu Chen
Professor, Centre of Clinical Laboratory
First Affiliated Hospital, Zhejiang University
Director, Key Laboratory of Clinical In Vitro Diagnostic Techniques
of Zhejiang Province, Zhejiang University
Director, Institute of Laboratory Medicine, Zhejiang University
PI, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases,
National Clinical Research Center for Infectious Diseases
Associate Prof. Yifei Shen
Associate Professor, Centre of Clinical Laboratory
First Affiliated Hospital, Zhejiang University
PI, Key Laboratory of Clinical In Vitro Diagnostic Techniques
of Zhejiang Province, Zhejiang University
PI, Institute of Laboratory Medicine, Zhejiang University
Department of Bioinformatics and Computational Biology,
The University of Texas MD Anderson Cancer Center
Methods
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) via host gene expression patterns. Genes used in the InfectDaigno could be downloaded here: InfectDaigno genes. The source code for InfectDiagno could be downloaded here: InfectDaigno source code.The example data set used in the source code could be downloaded here: Sample data.The training data and classifeir used in the source code could be downloaded here: InfectDaigno training data and classifier.The clinical validation data set could be downloaded here: Data, Label.