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Bio-AIMS collection of chemoinformatics web tools based on molecular graph information and artificial intelligence models
(Bentham, 2015-09-01)
[Abstract] The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction ...
On-line learning and anomaly detection methods : applications to fault assessment
(2013)
[Abstract] This work lays at the intersection of two disciplines, Machine Learning (ML) research and predictive maintenance of machinery. On the one hand, Machine Learning aims at detecting patterns in data gathered from ...
Experimental study and random forest prediction model of microbiome cell surface hydrophobicity
(Elsevier, 2016-11-09)
[Abstract] The cell surface hydrophobicity (CSH) is an assessable physicochemical property used to evaluate the microbial adhesion to the surface of biomaterials, which is an essential step in the microbial biofilm formation ...
A generalized linear model for cardiovascular complications prediction in PD patients
(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 ...
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 ...
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 ...
NLOS Identification and Mitigation Using Low-Cost UWB Devices
(M D P I AG, 2019-08-08)
[Abstract] Indoor location systems based on ultra-wideband (UWB) technology have become very popular in recent years following the introduction of a number of low-cost devices on the market capable of providing accurate ...
Classification of mild cognitive impairment and Alzheimer’s Disease with machine-learning techniques using 1H Magnetic Resonance Spectroscopy data
(Elsevier, 2015-03-30)
[Abstract] Several magnetic resonance techniques have been proposed as non-invasive imaging biomarkers for the evaluation of disease progression and early diagnosis of Alzheimer’s Disease (AD). This work is the first ...
Monitorización del estado de rodamientos basada en técnicas de aprendizaje automático
(Universidade da Coruña, Servizo de Publicacións, 2019)
[Resumen] En la actualidad, los procesos de fabricación están adoptando nuevas soluciones basadas en la aplicación de técnicas de aprendizaje automático, que permiten llevar a cabo la monitorización de los procesos en ...
Application of Functional Data Analysis for the Prediction of Maximum Heart Rate
(IEEE-Institute of Electrical and Electronics Engineers, 2019-08-29)
[Abstract]: Maximum heart rate (MHR) is widely used in the prescription and monitoring of exercise intensity, and also as a criterion for the termination of sub-maximal aerobic _tness tests in clinical populations.
Traditionally, ...
