about
Network biomarkers reveal dysfunctional gene regulations during disease progressionReconstructing dynamic gene regulatory networks from sample-based transcriptional dataMaximization of negative correlations in time-course gene expression data for enhancing understanding of molecular pathwaysUnravelling personalized dysfunctional gene network of complex diseases based on differential network modelBig-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals.Integrative enrichment analysis: a new computational method to detect dysregulated pathways in heterogeneous samples.Interferon-microRNA signalling drives liver precancerous lesion formation and hepatocarcinogenesis.Comparative network stratification analysis for identifying functional interpretable network biomarkers.Pattern fusion analysis by adaptive alignment of multiple heterogeneous omics data.Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks.Prediction of dynamical drug sensitivity and resistance by module network rewiring-analysis based on transcriptional profiling.Edge biomarkers for classification and prediction of phenotypes.Detecting disease genes of non-small lung cancer based on consistently differential interactions.Local network component analysis for quantifying transcription factor activities.Dysfunction of PLA2G6 and CYP2C44-associated network signals imminent carcinogenesis from chronic inflammation to hepatocellular carcinoma.Serum chemokine network correlates with chemotherapy in non-small cell lung cancer.Inferring Sequential Order of Somatic Mutations during Tumorgenesis based on Markov Chain Model.Diagnosing phenotypes of single-sample individuals by edge biomarkers.Mixture classification model based on clinical markers for breast cancer prognosis.Individual-specific edge-network analysis for disease prediction.A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.Discovering personalized driver mutation profiles of single samples in cancer by network control strategy.Deciphering early development of complex diseases by progressive module network.Detecting tissue-specific early warning signals for complex diseases based on dynamical network biomarkers: study of type 2 diabetes by cross-tissue analysis.Integrative Analysis of Omics Big Data.Revisit of Machine Learning Supported Biological and Biomedical Studies.Unravelling miRNA regulation in yield of rice (Oryza sativa) based on differential network model.High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data AnalysisCell-specific network constructed by single-cell RNA sequencing data.Roles of TGFβ1 in the expression of phosphoinositide 3-kinase isoform genes and sensitivity and response of lung telocytes to PI3K inhibitorsNetwork control principles for identifying personalized driver genes in cancerA novel network control model for identifying personalized driver genes in cancerChronic hepatitis B: dynamic change in Traditional Chinese Medicine syndrome by dynamic network biomarkersGenomic and transcriptomic investigations of the evolutionary transition from oviparity to viviparitySingle-Cell RNA Sequencing-Based Computational Analysis to Describe Disease HeterogeneityDynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testingIdentifying Methylation Pattern and Genes Associated with Breast Cancer SubtypesMulti-view Subspace Clustering Analysis for Aggregating Multiple Heterogeneous Omics Data
P50
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P50
description
researcher ORCID ID = 0000-0002-0295-3994
@en
name
Tao Zeng
@ast
Tao Zeng
@en
Tao Zeng
@es
Tao Zeng
@nl
type
label
Tao Zeng
@ast
Tao Zeng
@en
Tao Zeng
@es
Tao Zeng
@nl
prefLabel
Tao Zeng
@ast
Tao Zeng
@en
Tao Zeng
@es
Tao Zeng
@nl
P31
P496
0000-0002-0295-3994