ArticleMolecular therapy. Methods & clinical development2018
Inferring Novel Tumor Suppressor Genes with a Protein-Protein Interaction Network and Network Diffusion Algorithms.
Article in Molecular therapy. Methods & clinical development, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 39 citations in OpenAlex.
- Stochastic Nanoscale Biophysical Cues as a Basis for the Induction of Glioblastoma-Like Transcriptional Programs in Astrocytes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Network Medicine-Based Strategy Identifies Maprotiline as a Repurposable Drug by Inhibiting PD-L1 Expression via Targeting SPOP in Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Effect ofInternational journal of molecular sciences · 2024Article
- Article
- Chromosomal Translocations Detection in Cancer Cells Using Chromosomal Conformation Capture Data.Genes · 2022Article
- Network analysis of atherosclerotic genes elucidates druggable targets.BMC medical genomics · 2022Article
- Identification of Novel Lung Cancer Driver Genes Connecting Different Omics Levels With a Heat Diffusion Algorithm.Frontiers in cell and developmental biology · 2022Article
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies.PLoS genetics · 2021Article
- A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma.Frontiers in genetics · 2021Article
- DeepGP: An Integrated Deep Learning Method for Endocrine Disease Gene Prediction Using Omics Data.Frontiers in cell and developmental biology · 2021Article
- Fractional diffusion on the human proteome as an alternative to the multi-organ damage of SARS-CoV-2.Chaos (Woodbury, N.Y.) · 2020Article
- Identification of Latent Oncogenes with a Network Embedding Method and Random Forest.BioMed research international · 2020Article
- Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy.Computational and mathematical methods in medicine · 2020Article
- Investigation of miRNA and mRNA Co-expression Network in Ependymoma.Frontiers in bioengineering and biotechnology · 2020Article
- Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.Gene therapy · 2019Article
- The RND1 Small GTPase: Main Functions and Emerging Role in Oncogenesis.International journal of molecular sciences · 2019Review
- Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis.Frontiers in genetics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Extensive studies on tumor suppressor genes (TSGs) are helpful to understand the pathogenesis of cancer and design effective treatments. However, identifying TSGs using traditional experiments is quite difficult and time consuming. Developing computational methods to identify possible TSGs is an alternative way. In this study, we proposed two computational methods that integrated two network diffusion algorithms, including Laplacian heat diffusion (LHD) and random walk with restart (RWR), to search possible genes in the whole network. These two computational methods were LHD-based and RWR-based methods. To increase the reliability of the putative genes, three strict screening tests followed to filter genes obtained by these two algorithms. After comparing the putative genes obtained by the two methods, we designated twelve genes (e.g., MAP3K10, RND1, and OTX2) as common genes, 29 genes (e.g., RFC2 and GUCY2F) as genes that were identified only by the LHD-based method, and 128 genes (e.g., SNAI2 and FGF4) as genes that were inferred only by the RWR-based method. Some obtained genes can be confirmed as novel TSGs according to recent publications, suggesting the utility of our two proposed methods. In addition, the reported genes in this study were quite different from those reported in a previous one.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.