Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: A focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins
bioRxiv, ISSN: 2692-8205
2019
- 2Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
Background: Breast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. Methods: 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. Results: 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 4504 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. Conclusions: This powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.
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