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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 ...
Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models
(Elsevier, 2023-07)
[Abstract]: COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it.
In such situation, the clinicians must take important decisions, in an environment where medical ...
Machine Learning Analysis of the Human Infant Gut Microbiome Identifies Influential Species in Type 1 Diabetes
(Elsevier, 2021)
[Abstract] Diabetes is a disease that is closely linked to genetics and epigenetics, yet mechanisms for clarifying the onset and/or progression of the disease have sometimes not been fully managed. In recent years and due ...
Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes
(FRONTIERS MEDIA S.A., 2022)
[Abstract] Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple ...
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 ...
Fed-mRMR: A lossless federated feature selection method
(Elsevier, 2024-05)
[Abstract]: Feature selection has become a mandatory task in data mining, due to the overwhelming amount of features in Big Data problems. To handle this high-dimensional data and avoid the well-known curse of dimensionality, ...
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 ...
CUDA-JMI: Acceleration of feature selection on heterogeneous systems
(Elsevier, 2020-01)
[Abstract]: Feature selection is a crucial step nowadays in machine learning and data analytics to remove irrelevant and redundant characteristics and thus to provide fast and reliable analyses. Many research works have ...
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 ...
Multithreaded and Spark parallelization of feature selection filters
(2016)
[Abstract]: Vast amounts of data are generated every day, constituting a volume that is challenging to analyze. Techniques such as feature selection are advisable when tackling large datasets. Among the tools that provide ...