ArticleScientific reports2020
Prediction of breast cancer proteins involved in immunotherapy, metastasis, and RNA-binding using molecular descriptors and artificial neural networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 59 citations in OpenAlex.
- Different Methods of Physical Training Applied to Women Breast Cancer Survivors: A Systematic Review.Frontiers in physiology · 2021Pooled it
- Artificial Intelligence Algorithm-Based Ultrasound Image Segmentation Technology in the Diagnosis of Breast Cancer Axillary Lymph Node Metastasis.Journal of healthcare engineering · 2021Trial
- Computational techniques to study breast cancer scaffolds for antiangiogenesis: a review.Journal of molecular modeling · 2026Review
- Estrogen receptor β target gene expression reveals novel repressive functions in aggressive breast cancer.NPJ breast cancer · 2026Article
- Next-Generation Immune Checkpoints and Tumor Microenvironment Modulation in Cancer Immunotherapy.Journal of immunology research · 2026Review
- Identifying potential therapeutic targets for prostate cancer with mediating role in tumor immunity.Discover oncology · 2025Article
- A Rac-specific competitive inhibitor of guanine nucleotide binding reduces metastasis in triple-negative breast cancer.Cell reports. Medicine · 2025Article
- Identification of RASL11A as a gene conferring radiosensitivity in glioblastoma.Journal of neuro-oncology · 2025Article
- METTL3/YTDHF1 Stabilizes CSRP1 mRNA to Regulate Glycolysis and Promote Acute Myeloid Leukemia Progression.Cell biochemistry and biophysics · 2025Article
- Telomere-related gene risk model predicts prognostic and immune microenvironment alterations in prostate cancer.Scientific reports · 2025Article
- The Involvement of CSRP1 in Neuroblastoma Differentiation and Apoptosis Impacting Tumor-Suppressive Therapeutic Responses.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- Cancer genomics and bioinformatics in Latin American countries: applications, challenges, and perspectives.Frontiers in oncology · 2025Review
- Deciphering organotropism reveals therapeutic targets in metastasis.Frontiers in cell and developmental biology · 2025Review
- Global analysis of actionable genomic alterations in thyroid cancer and precision-based pharmacogenomic strategies.Frontiers in pharmacology · 2025Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
- Proteomic analysis of extracellular vesicles derived from canine mammary tumour cell lines identifies protein signatures specific for disease state.BMC veterinary research · 2024Article
- Worldwide analysis of actionable genomic alterations in lung cancer and targeted pharmacogenomic strategies.Heliyon · 2024Article
- Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.Scientific reports · 2024Article
- Proteogenomic characterization of difficult-to-treat breast cancer with tumor cells enriched through laser microdissection.Breast cancer research : BCR · 2024Article
- Cysteine- and glycine-rich protein 1 predicts prognosis and therapy response in patients with acute myeloid leukemia.Clinical and experimental medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. This work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine-learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features. The performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037, and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4,504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. This powerful model predicts several BC-related proteins that should be deeply studied to find new biomarkers and better therapeutic targets. Scripts can be downloaded at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.
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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.