Currently, my research is mainly focused on tissue image analysis. Our team was among the first to develop and validate computational models using tissue images collected from routine clinical procedures to refine lung cancer prognosis (Luo et al, 2017, Luo et al, 2019). We have developed deep-learning-based models to detect tumor regions and micro-blood-vessels and to predict patient outcomes (Wang et al, 2018; Yi et al, 2018; Huang et al, 2017). Recently, we developed algorithms to detect and classify different types of cells from tissue images (Wang et al, 2019; Wang et al, 2020), and developed a set of spatial models (Li et al, 2019, Li et al, 2020) to investigate cell spatial organization and its implications in disease. I am the PI of a CPRIT (Cancer Prevention Institute of Texas) grant focusing on analyzing digital pathology data to improve lung cancer patient care. Our study was highlighted on the cover page of the May 2020 issue of Cancer Research. https://cancerres.aacrjournals.org/content/80/10.cover-expansion
Our team has developed and validated machine learning algorithms to solve practical biological and clinical problems: (1). In 2007, I applied a machine learning method to identify new blood protein biomarkers that picks up very early stages of Alzheimer’s Disease (AD) at 88 to 96 percent accuracy with much lower cost. This blood-based test is current being validated in several large NIH-funded ongoing clinical trials as a front-line screening test for AD. (2) We developed a new algorithm to identify a gene signature to predict lung cancer patient response to adjuvant chemotherapy (Tang et al, 2013), and validate the signature using a Clinical Laboratory Improvement Amendments (CLIA)-grade assay in an independent cohort (Xie et al, 2018). (3) We developed a machine learning algorithm to predict lung cancer patient prognosis using commonly available pathology images (Luo et al, 2017), and validated the model in multiple independent patient cohorts (Luo et al, 2019).
We are actively developing new bioinformatics tools and computational algorithms for big data, such as genome-wide RNAi screening data and next-generation sequencing data. My group is experienced with software development. We have developed several comprehensive web portals, including Lung Cancer Explorer (LCE) and Genomic Regression Analysis of Coordinated Expression (GRACE) (Cai, et al., Nat Communications, 2017), deep learning-based software (ConvPath), online clinical outcome prediction calculators, Galaxy-based software tools (such as PipeCLIP), and R packages. Some of this software can be accessed on my lab website: https://qbrc.swmed.edu/x-software/
We have developed computational methodologies for Bayesian analysis, spatial modeling, and integrative analysis of different biological datasets, especially pathological imaging data.
Our team has a longstanding interest and experience in biomedical image analysis. We have developed new methods and performance analysis in brain images, such as fMRI and DTI images.