题目： Modeling cancer micro-environment using machine learning techniques

报告人：Chi Zhang

Assistant Professor, School of Medicine, Indiana University, USA

Adjunct Professor, Department of electronic computer engineering, Purdue University,USA

时间：2018年08月10日上午 10:30-11:30

地点：信息东楼328室

Abstract:

We developed a novel computational pipeline namely ICAD (Inference of Cell types And Deconvolution) for an accurate inference of immune/stromal (I/S) cell types in a cancer tissue. We have validated our method by using bulk tissue RNA-seq data simulated by four sets of single cell RNA-seq data, and demonstrated ICAD can accurately identify of the I/S cell and sub-cell types, and predict their infiltration level. With applying the method to TCGA data sets, and integrated with independent single cell RNA-seq data, we have identified genes, miRNA and lncRNA expressed by cancer cells that may affect I/S cells’ infiltration and activity level, which have potential to be used as targets for the non-responding mechanisms of immunotherapy. 

Biography:

Dr. Zhang received a Ph.D. in Bioinformatics at the University of Georgia in 2015 and a Bachelor degree in Mathematics at Peking University in 2010. Dr. Zhang joint Indiana University School of Medicine as an Assistant Professor in Medical and Molecular Genetics since 2016. He is also an Adjunct Professor in the Department of electronic computer engineering at Purdue University. 

Dr. Zhang’s lab is primarily focusing on elucidation the role of different tissue/cell components and their interactions in cancer progression, and resistance to targeted/immuno-therapy drugs, through modeling and analysis bulk and single cell transcriptomics data of cancer tissue samples. Dr. Zhang also works on method development for statistical modeling of single cell RNA-seq data and differential expression analysis, bi-clustering and gene co-regulation analysis, and tissue data deconvolution.

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