ArticleMolecules (Basel, Switzerland)2018
The Integrative Method Based on the Module-Network for Identifying Driver Genes in Cancer Subtypes.
Article in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 30 citations in OpenAlex.
- Article
- Explainable deep learning for identifying cancer driver genes based on the Cancer Dependency Map.bioRxiv : the preprint server for biology · 2025Article
- MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer.Frontiers in pharmacology · 2024Article
- Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.Computational intelligence and neuroscience · 2022Article
- Evaluating machine learning methodologies for identification of cancer driver genes.Scientific reports · 2021Article
- A novel method to identify gene interaction patterns.BMC genomics · 2021Article
- sgRNA-PSM: Predict sgRNAs On-Target Activity Based on Position-Specific Mismatch.Molecular therapy. Nucleic acids · 2020Article
- A Method for Prediction of Thermophilic Protein Based on Reduced Amino Acids and Mixed Features.Frontiers in bioengineering and biotechnology · 2020Article
- Developing a Multi-Layer Deep Learning Based Predictive Model to Identify DNA N4-Methylcytosine Modifications.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With advances in next-generation sequencing(NGS) technologies, a large number of multiple types of high-throughput genomics data are available. A great challenge in exploring cancer progression is to identify the driver genes from the variant genes by analyzing and integrating multi-types genomics data. Breast cancer is known as a heterogeneous disease. The identification of subtype-specific driver genes is critical to guide the diagnosis, assessment of prognosis and treatment of breast cancer. We developed an integrated frame based on gene expression profiles and copy number variation (CNV) data to identify breast cancer subtype-specific driver genes. In this frame, we employed statistical machine-learning method to select gene subsets and utilized an module-network analysis method to identify potential candidate driver genes. The final subtype-specific driver genes were acquired by paired-wise comparison in subtypes. To validate specificity of the driver genes, the gene expression data of these genes were applied to classify the patient samples with 10-fold cross validation and the enrichment analysis were also conducted on the identified driver genes. The experimental results show that the proposed integrative method can identify the potential driver genes and the classifier with these genes acquired better performance than with genes identified by other methods.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.