A5060109811- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Computational Drug Discovery Methods
- Cancer-related molecular mechanisms research
- Complex Network Analysis Techniques
- Single-cell and spatial transcriptomics
- Genomics and Chromatin Dynamics
- Gene Regulatory Network Analysis
- Opinion Dynamics and Social Influence
- Machine Learning in Bioinformatics
- RNA modifications and cancer
- Microbial Metabolic Engineering and Bioproduction
- Protein Structure and Dynamics
- Epigenetics and DNA Methylation
- Biomedical Text Mining and Ontologies
- DNA and Biological Computing
- Genomics and Phylogenetic Studies
- Cancer Genomics and Diagnostics
- RNA Research and Splicing
- MicroRNA in disease regulation
- Data Mining Algorithms and Applications
- Genomic variations and chromosomal abnormalities
- Mental Health Research Topics
- Enzyme Catalysis and Immobilization
- Advanced biosensing and bioanalysis techniques
Xidian University
2016-2025
Beijing University of Chinese Medicine
2025
University of Chinese Academy of Sciences
2012-2024
Occidental Petroleum (United States)
2024
Shandong University
2023-2024
Chinese Academy of Sciences
2021-2024
Chongqing University
2023
Chongqing University of Arts and Sciences
2023
Zhejiang Lab
2021
Institute of Computing Technology
2012-2021
CytoTalk is a novel computational method that infers cell type–specific signaling networks from single-cell transcriptomic data.
Structural variations (SVs) play an essential role in the evolution of human genomes and are associated with cancer genetics rare disease. High-throughput chromosome capture (Hi-C) technology probed all genome-wide crosslinked chromatin to study spatial architecture chromosomes. Hi-C read pairs can span megabases, making useful for detecting large-scale SVs. So far, identification SVs from data is still early stages only a few methods available. Especially, no algorithm has been developed...
Abstract Motivation The emergence of drug-resistant bacteria makes the discovery new antibiotics an urgent issue, but finding molecules with desired antibacterial activity is extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. Results By designing latent space guiding optimization functional compounds based on space, model can novel desirable without need...
More and more evidences demonstrate that the long non-coding RNAs (lncRNAs) play many key roles in diverse biological processes. There is a critical need to annotate functions of increasing available lncRNAs. In this article, we try apply global network-based strategy tackle issue for first time. We develop bi-colored network based function predictor, RNA predictor ('lnc-GFP'), predict probable lncRNAs at large scale by integrating gene expression data protein interaction data. The...
Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation lncRNA-disease associations, only a few studies had studied roles these associations pathogenesis. In this paper, we investigated from network view to understand contribution lncRNAs Specifically, both properties diseases which were implicated, Regarding fact protein coding genes involved diseases, constructed coding-non-coding...
Inferring drug-disease associations is critical in unveiling disease mechanisms, as well discovering novel functions of available drugs, or drug repositioning. Previous work primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their through interacting others. To overcome this issue, we propose a methodology that discover the association protein complexes. Firstly, integrated heterogeneous network consisting complexes, and are...
Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these complicated problem due to lack gold standards. Moreover, questions practical importance remain be addressed regarding impact selecting appropriate data types and combinations on performance studies. Here, we constructed three classes benchmarking datasets nine cancers TCGA by...
Hepatocellular carcinoma (HCC) is a significant health problem worldwide with poor prognosis. Drug repositioning represents profitable strategy to accelerate drug discovery in the treatment of HCC. In this study, we developed new approach for predicting therapeutic drugs HCC based on tissue-specific pathways and identified three newly predicted that are likely be We validated these by analyzing their overlapping indications reported PubMed literature. By using cancer cell line data database,...
Phenotypic features associated with genes and diseases play an important role in disease-related studies most of the available methods focus solely on Online Mendelian Inheritance Man (OMIM) database without considering controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized vocabulary covering phenotypic abnormalities human diseases, becomes comprehensive resource for computational analysis disease phenotypes. Most existing HPO-based software tools cannot be used...
While long non-coding RNAs (lncRNAs) may play important roles in cellular function and biological process, we still know little about them. Growing evidences indicate that subcellular localization of lncRNAs provide clues to their functionality. To facilitate researchers functionally characterize thousands lncRNAs, developed a database-driven application, lncSLdb, which stores manages user-collected qualitative quantitative information from literature mining. The current release contains >11...
Disease relationship studies for understanding the pathogenesis of complex diseases, diagnosis, prognosis and drug development are important. Traditional approaches consider one type disease data or aggregating multiple types into a single network, which results in important temporal- context-related information loss may distort actual organization. Therefore, it is necessary to apply multilayer network model relationships between diseases interplays different relationships. Further, modules...
The diverse structures and functions inherent in RNAs present a wealth of potential drug targets. Some small molecules are anticipated to serve as leading compounds, providing guidance for the development novel RNA-targeted therapeutics. Consequently, determination RNA-small molecule binding affinity is critical undertaking landscape discovery development. Nevertheless, date, only one computational method prediction has been proposed. remains significant challenge. model deemed essential...
Since genes associated with similar diseases/disorders show an increased tendency for their protein products to interact each other through protein-protein interactions (PPI), clustering analysis obviously as efficient technique can be easily used predict human disease-related gene clusters/subnetworks.Firstly, we algorithms, Markov cluster algorithm (MCL), Molecular complex detection (MCODE) and Clique percolation method (CPM) decompose PPI network into dense clusters the candidates of...
In this paper, we present a novel rough-fuzzy clustering (RFC) method to detect overlapping protein complexes in protein-protein interaction (PPI) networks. RFC focuses on fuzzy relation model rather than graph by integrating sets and rough sets, employs the upper lower approximations of deal with complexes, calculates number automatically. Fuzzy between proteins is established then transformed into equivalence relation. Non-overlapping correspond classes satisfying certain To obtain...
Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and an important medical problem worldwide. However, use of current therapies for HCC no possible to be cured, despite numerous attempts clinical trials, there are not so many approved targeted treatments HCC. So, it necessary identify additional treatment strategies prevent growth tumors. We looking a systematic drug repositioning bioinformatics method new candidates HCC, which considers only aberrant genomic...
Abstract Most combination therapies are developed based on targets of existing drugs, which only represent a small portion the human proteome. We introduce network controllability-based method, OptiCon, for de novo identification synergistic regulators as candidates therapy. These jointly exert maximal control over deregulated genes but minimal unperturbed in disease. Using data from three cancer types, we show that 68% predicted either known drug or have critical role development. Predicted...
Since miRNAs can participate in the posttranscriptional regulation of gene expression, they may provide ideas for development new drugs or become biomarkers drug targets disease diagnosis. In this work, we propose an miRNA-disease association prediction method based on meta-paths (MDPBMP). First, miRNA-disease-gene heterogeneous information network was constructed, and seven symmetrical were defined according to different semantics. After constructing initial feature vector node, carried by...