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Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning
(MDPI, 2019)
[Abstract] In this work, we improved a previous model used for the prediction of proteomes as new
B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its
sequence, a known ...
Information Fusion and Ensembles in Machine Learning
(2019)
[Abstract] Traditionally, machine learning methods have used a single learning model to solve
a particular problem. However, the idea of combining multiple models instead of a
single one to solve a problem has its rationale ...
Parallel feature selection for distributed-memory clusters
(2019)
[Abstract]: Feature selection is nowadays an extremely important data mining stage in the field of machine learning due to the appearance of problems of high dimensionality. In the literature there are numerous feature ...
A scalable decision-tree-based method to explain interactions in dyadic data
(Elsevier, 2019-12)
[Abstract]: Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, ...
Gene Signatures Research Involved in Cancer Using Machine Learning
(M D P I AG, 2019)
[Abstract] With the cheapening of mass sequencing techniques and the rise of computer technologies, capable of analyzing a huge amount of data, it is necessary nowadays that both branches mutually benefit. Transcriptomics, ...
Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment
(M D P I AG, 2019-07-31)
[Abstract]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 ...