ArticleBMC genomics2017
Exploring the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme patients by a novel survival analysis model.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.Neurosurgical review · 2021Pooled it
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- Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research.Frontiers in genetics · 2021Article
- The clinical significance of glutathione peroxidase 2 in glioblastoma multiforme.Translational neuroscience · 2021Article
- Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme.BMC bioinformatics · 2020Article
- Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis.Frontiers in genetics · 2020Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
- Computed tomography angiography-based analysis of high-risk intracerebral haemorrhage patients by employing a mathematical model.BMC bioinformatics · 2019Article
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study is to explore the key genes and signaling transduction pathways related to the survival time of glioblastoma multiforme (GBM) patients.
resultsOur results not only showed that mutually explored GBM survival time related genes and signaling transduction pathways are closely related to the GBM, but also demonstrated that our innovated constrained optimization algorithm (CoxSisLasso strategy) are better than the classical methods (CoxLasso and CoxSis strategy).
conclusionWe analyzed why the CoxSisLasso strategy can outperform the existing classical methods and discuss how to extend this research in the distant future.
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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 OpenQuestion graph.