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Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment

Department of Computer Science, Faculty of Computer Science, University of A Coruña, CITIC, A Coruña 15071, Spain
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Presented at the 2nd XoveTIC conference, A Coruña, 5–6 September 2019.
Proceedings 2019, 21(1), 15; https://doi.org/10.3390/proceedings2019021015
Published: 31 July 2019
(This article belongs to the Proceedings of The 2nd XoveTIC Conference (XoveTIC 2019))
The prediction of metabolic activities in silico form is crucial to be able to address all research possibilities without exceeding the experimental costs. In particular, for cancer research, the prediction of certain activities can be of great help in the discovery of different treatments. In this work it has been proposed to predict, through Machine Learning, the anti-angiogenic activity of peptides is currently being used in cancer treatment and is giving hopeful results. From a list of peptide sequences, three types of molecular descriptors were obtained (AAC, DC and TC) that offered the possibility of training different ML algorithms. After a Feature Selection process, different models were obtained with a predictive value that surpassed the current state of the art. These results shown that ML is useful for the classification and prediction of the activity of new peptides, making experimental screening cheaper and faster.
Keywords: machine learning; feature selection; activity prediction; peptides; cancer; screening machine learning; feature selection; activity prediction; peptides; cancer; screening
  • Externally hosted supplementary file 1
    Doi: https://doi.org/10.6084/m9.figshare.6016994
MDPI and ACS Style

Liñares-Blanco, J.; Fernandez-Lozano, C. Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment. Proceedings 2019, 21, 15.

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