ArticleFrontiers in genetics2018
Combining Pathway Identification and Breast Cancer Survival Prediction via Screening-Network Methods.
Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology.BMC bioinformatics · 2023Article
- Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Article
- A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis.Journal of healthcare engineering · 2022Article
- A gene expression signature-based nomogram model in prediction of breast cancer bone metastases.Cancer medicine · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the most common invasive tumors causing high mortality among women. It is characterized by high heterogeneity regarding its biological and clinical characteristics. Several high-throughput assays have been used to collect genome-wide information for many patients in large collaborative studies. This knowledge has improved our understanding of its biology and led to new methods of diagnosing and treating the disease. In particular, system biology has become a valid approach to obtain better insights into breast cancer biological mechanisms. A crucial component of current research lies in identifying novel biomarkers that can be predictive for breast cancer patient prognosis on the basis of the molecular signature of the tumor sample. However, the high dimension and low sample size of data greatly increase the difficulty of cancer survival analysis demanding for the development of
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