Diego Calvo BarrenoUniversidad de Valladolid | UVA · Department of Theory of Signal and Communications and Telematic Engineering
Diego Calvo Barreno
Data Scientist
About
6
Publications
8,103
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117
Citations
Introduction
Additional affiliations
January 2009 - present
Centro de Observación y Teledetección Espacial (COTESA)
Position
- Scientific data
Description
- Control of mathematical modeling techniques, machine learning, data mining. Using ETL processes large volumes of data. Experience NoSQL database such as Mongo DB, Cassandra, Neo4j, ...
Education
March 2016 - February 2018
Universitat Oberta de Catalunya
Field of study
September 2006 - June 2010
September 2005 - September 2013
Publications
Publications (6)
Mental Health disorders such as schizophrenia, depression, and dementia have a great impact on society worldwide. Recent developments have witnessed numerous advances in telemedicine that allow remote monitoring of elderly people with diseases of this pathology. One of key factors influencing recent advances in health information systems is the use...
Mental Health disorders such as schizophrenia, depression, and dementia have a great impact on society worldwide. Recent developments have witnessed numerous advances in telemedicine that allow remote monitoring of elderly people with diseases of this pathology. One of key factors influencing recent advances in health information systems is the use...
Background: Data Mining in medicine is an emerging field of great importance to provide a prognosis and deeper understanding of disease classification, specifically in Mental Health areas.
Objective: The main objective of this paper is to present a review of the existing research works in the literature, referring to the techniques and algorithms...
Application of Deep Learning Techniques such as Convolutional Neural Networks to predict viruses, cancer and therapies.
1) Virus prediction.
2) Cancer prediction
3) Therapy prediction
This study is focused on the development of a technology to identify characteristics in nucleotide sequences using deep learning provided by Convolutional Neural Networks. In order to demonstrate the effectiveness of this technology, a classifier has been developed to identify viruses in sequencing reads of 100 nucleotides, a proxy for a real NGS s...