scispace - formally typeset
Q

Qiuyu Lian

Researcher at Tsinghua University

Publications -  16
Citations -  601

Qiuyu Lian is an academic researcher from Tsinghua University. The author has contributed to research in topics: Biology & Gene. The author has an hindex of 5, co-authored 12 publications receiving 307 citations. Previous affiliations of Qiuyu Lian include University of Pittsburgh & Shanghai Jiao Tong University.

Papers
More filters
Journal ArticleDOI

Recurrently deregulated lncRNAs in hepatocellular carcinoma.

TL;DR: By analysing 60 clinical samples' RNA-seq data from 20 HCC patients, 8,603 candidate lncRNAs are identified and characterized and further validated using RNAi-based loss-of-function assays provide a valuable resource of functional lnc RNAs and biomarkers associated with HCC tumorigenesis and metastasis.
Journal ArticleDOI

HCCDB: A Database of Hepatocellular Carcinoma Expression Atlas.

TL;DR: The database HCCDB is developed to serve as a one-stop online resource for exploring HCC gene expression with user-friendly interfaces and provides links to third-party databases on drug, proteomics, and literatures, and graphically displays the results from computational analyses.
Posted ContentDOI

Comprehensive Analysis of Spatial Architecture in Primary Liver Cancer

TL;DR: In this article, the authors constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from 7 patients and found that the bidirectional ligand-receptor interactions at the 100 μm wide cluster-cluster boundary contribute to maintaining intratumor architecture.
Journal ArticleDOI

Modeling gene regulatory networks using neural network architectures

TL;DR: DeepSEM as discussed by the authors is a deep generative model that can jointly infer gene regulatory networks and biologically meaningful representation of single-cell RNA sequencing (scRNA-seq) data, which can provide a useful and powerful tool to analyze scRNAseq data and infer a GRN.
Journal ArticleDOI

GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing

TL;DR: GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq, and uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset.