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Xiang Lin
Researcher at New Jersey Institute of Technology
Publications - 18
Citations - 217
Xiang Lin is an academic researcher from New Jersey Institute of Technology. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 3, co-authored 11 publications receiving 45 citations.
Papers
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Journal ArticleDOI
Paneth Cell-Derived Lysozyme Defines the Composition of Mucolytic Microbiota and the Inflammatory Tone of the Intestine
Shiyan Yu,Iyshwarya Balasubramanian,Daniel Laubitz,Kevin Tong,Sheila Bandyopadhyay,Xiang Lin,Juan Flores,Rajbir Singh,Yue Liu,Carlos Macazana,Yanlin Zhao,Fabienne Béguet-Crespel,Karuna Patil,Monica T. Midura-Kiela,Daniel Wang,George S. Yap,Ronaldo P. Ferraris,Zhi Wei,Edward M. Bonder,Max M. Häggblom,Lanjing Zhang,Lanjing Zhang,Veronique Douard,Michael P. Verzi,Ken Cadwell,Pawel R. Kiela,Nan Gao +26 more
TL;DR: Targeted disruption of Paneth cell lysozyme (Lyz1) protected mice from experimental colitis and balances intestinal anti- and pro-inflammatory responses, with implications for IBD.
Journal ArticleDOI
Very low mutation burden is a feature of inflamed recurrent glioblastomas responsive to cancer immunotherapy.
Matthias Gromeier,Michael C. Brown,Gao Zhang,Xiang Lin,Yeqing Chen,Zhi Wei,Nike Beaubier,Hai Yan,Yiping He,Annick Desjardins,James E. Herndon,Frederick S. Varn,Roel G.W. Verhaak,Junfei Zhao,Dani P. Bolognesi,Allan H. Friedman,Henry S. Friedman,Frances McSherry,Andrea Muscat,Eric S. Lipp,Smita K. Nair,Mustafa Khasraw,Katherine B. Peters,Dina Randazzo,John Sampson,Roger E. McLendon,Darell D. Bigner,David M. Ashley +27 more
TL;DR: This paper performed genomic analysis of tumor tissue from recurrent WHO grade IV glioblastoma patients acquired prior to immunotherapy intervention and reported that very low tumor mutation burden is associated with longer survival after recombinant polio virotherapy or after immune checkpoint blockade in recurrent glioblastic tumor patients.
Journal ArticleDOI
Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data.
TL;DR: Wang et al. as discussed by the authors proposed a principled clustering method named scDCC, which integrates domain knowledge into the clustering step to facilitate the biological interpretability of clusters, and subsequent cell type identification.
Journal ArticleDOI
Clustering of single-cell multi-omics data with a multimodal deep learning method
TL;DR: Wang et al. as discussed by the authors developed a multimodal deep learning method, scMDC, for single-cell multi-omics data clustering analysis, which is an end-to-end deep model that explicitly characterizes different data sources and jointly learns latent features of deep embedding.
Journal ArticleDOI
The microbiome of HPV-positive tonsil squamous cell carcinoma and neck metastasis.
Karthik Rajasekaran,Karthik Rajasekaran,Ryan M. Carey,Xiang Lin,Tyler Seckar,Zhi Wei,Kevin Chorath,Jason G. Newman,Bert W. O'Malley,Gregory S. Weinstein,Michael Feldman,Erle S. Robertson +11 more
TL;DR: In this article, the authors used pan-pathogen microarray (PathoChip) to determine the bacterial microbiome in patients with Oropharyngeal squamous cell carcinoma (OPSCC) and compared them to benign tonsil specimens.