ReviewEvolutionary bioinformatics online2018
A Novel Approach for Identifying Relevant Genes for Breast Cancer Survivability on Specific Therapies.
Review in Evolutionary bioinformatics online, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
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Who cites it
11 citing papers in PubMed, 20 citations in OpenAlex.
- Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches.Interdisciplinary sciences, computational life sciences · 2026Article
- Unsupervised Random Forest Identifies Important Genetic Prognostic Factors for Breast Cancer Survival Time.Cancer informatics · 2025Article
- Targeting ZC3H11A elicits immunogenic cancer cell death through augmentation of antigen presentation and interferon response.Molecular therapy. Nucleic acids · 2024Article
- Identification of the Shared Gene Signatures and Biological Mechanisms in Hyperplastic Enlarged Lobular Units and Breast Cancer.Biochemical genetics · 2024Article
- Analysis of the mRNA export protein ZC3H11A in HCMV infection and pan-cancer.Frontiers in microbiology · 2023Article
- MLSP: A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer.Computational and structural biotechnology journal · 2022Article
- Protective Prognostic Biomarkers Negatively Correlated with Macrophage M2 Infiltration in Low-Grade Glioma.Journal of oncology · 2022Article
- Identification of Dipeptidyl Peptidase (DPP) Family Genes in Clinical Breast Cancer Patients via an Integrated Bioinformatics Approach.Diagnostics (Basel, Switzerland) · 2021Article
- Review
- A novel alternative splicing-based prediction model for uteri corpus endometrial carcinoma.Aging · 2019Article
- A Machine Learning Approach for Identifying Gene Biomarkers Guiding the Treatment of Breast Cancer.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analyzing the genetic activity of breast cancer survival for a specific type of therapy provides a better understanding of the body response to the treatment and helps select the best course of action and while leading to the design of drugs based on gene activity. In this work, we use supervised and nonsupervised machine learning methods to deal with a multiclass classification problem in which we label the samples based on the combination of the 5-year survivability and treatment; we focus on hormone therapy, radiotherapy, and surgery. The proposed nonsupervised hierarchical models are created to find the highest separability between combinations of the classes. The supervised model consists of a combination of feature selection techniques and efficient classifiers used to find a potential set of biomarker genes specific to response to therapy. The results show that different models achieve different performance scores with accuracies ranging from 80.9% to 100%. We have investigated the roles of many biomarkers through the literature and found that some of the discriminative genes in the computational model such as
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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.