ArticleScientific reports2019
Identifying potential drug targets in hepatocellular carcinoma based on network analysis and one-class support vector machine.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 42 citations in OpenAlex.
- Prediction of Multi-targeting Pharmacological Activity of Bioactive Compounds from Medicinal Plants Against Hepatocellular Carcinoma Through Advanced Network Pharmacology and Bioinformatics-Based Investigation.Applied biochemistry and biotechnology · 2025Article
- PRMT5 inhibition has a potent anti-tumor activity against adenoid cystic carcinoma of salivary glands.Journal of experimental & clinical cancer research : CR · 2025Article
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.Life (Basel, Switzerland) · 2022Article
- Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis.International journal of molecular sciences · 2022Article
- Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities.World journal of gastrointestinal oncology · 2022Review
- Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods.BMC medical genomics · 2021Article
- Artificial Intelligence in Cancer Research and Precision Medicine.Cancer discovery · 2021Review
- Dynamics of a Protein Interaction Network Associated to the Aggregation of polyQ-Expanded Ataxin-1.Genes · 2020Article
- Mechanism of Dayuanyin in the treatment of coronavirus disease 2019 based on network pharmacology and molecular docking.Chinese medicine · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is one major cause of cancer-related death worldwide. But now, the systematic therapy for the advanced stages of HCC is rather limited. Thus, the discovery of novel drug targets and thereafter targeted drugs against HCC is continuously needed. In this study, we combined clinical association data, gene expression profiles and manually collected drug target genes with the human protein-protein interaction (PPI) network to establish an in-silico HCC drug target predictor. First, we found drug target genes (DTGs), disease-associated genes (DAGs), prognostic unfavorable genes (PUGs) and cancer up-regulated genes (URGs) have higher degree, betweenness, closeness centrality, while cancer down-regulated genes (DRGs), prognostic favorable genes (PFGs) have lower degrees, in comparison with background genes. Moreover, DTG nodes were shown to be closer to DAG, PUG and URG nodes, but farther away from PFG and DRG nodes. Compared to the background, PFGs and DRGs were shown to have relatively bigger genetic dependency scores, while PUGs and URGs have smaller genetic dependency scores. Finally, based on the observed features of DTGs, we constructed a drug target predictor using one-class support vector machine (one-class SVM). Performance evaluation results suggested our predictor could effectively identify putative drug target genes for further research.
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