Now showing items 1-4 of 4
A generalized linear model for cardiovascular complications prediction in PD patients
[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 ...
Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics
(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 ...
Interpretable market segmentation on high dimension data
(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, ...
Integrative Multi-Omics Data-Driven Approach for Metastasis Prediction in Cancer
[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 ...