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A generalized linear model for cardiovascular complications prediction in PD patients 

Fernández-Lozano, Carlos; Alonso Valente, Rafael; Fidalgo Díaz, Manuel; Pazos, A. (ACM, 2018)
[Abstract] This study was conducted using machine learning models to identify patient non-invasive information for cardiovascular complications prediction in peritoneal dialysis patients. Nowadays is well known that ...
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Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics 

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M.; Loza, María Isabel; Munteanu, Cristian-Robert; Pazos, A.; García-Mera, Xerardo; González-Díaz, Humberto (American Chemical Society, 2018-05-23)
[Abstract] Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning ...
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Interpretable market segmentation on high dimension data 

Eiras-Franco, Carlos; Guijarro-Berdiñas, Bertha; Alonso-Betanzos, Amparo; Bahamonde, Antonio (M D P I AG, 2018-09-17)
[Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ...
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Integrative Multi-Omics Data-Driven Approach for Metastasis Prediction in Cancer 

Fernández-Lozano, Carlos; Liñares Blanco, José; Gestal, M.; Dorado, Julián; Pazos, A. (ACM, 2018)
[Abstract] Nowadays biomedical research is generating huge amounts of omic data, covering all levels of genetic information from nucleotide sequencing to protein metabolism. In the beginning, data were analyzed independently ...

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AuthorPazos, A. (3)Fernández-Lozano, Carlos (2)Alonso Valente, Rafael (1)Alonso-Betanzos, Amparo (1)Bahamonde, Antonio (1)Brea, José M. (1)Caamaño, Olga (1)Dorado, Julián (1)Eiras-Franco, Carlos (1)Ferreira da Costa, Joana (1)... View MoreSubject
Machine learning (4)
Big Data (1)Cancer (1)Cardiovascular risk prediction (1)ChEMBL (1)Data fusion (1)Data integration (1)Data-driven (1)Explainability (1)Feature selection (1)... View MoreDate Issued
2018 (4)
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