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Proceeding paper ; [Abstract] Information extracted from electronic health records (EHRs) is used for predictive tasks and clinical pattern recognition. Machine learning techniques also allow the extraction of knowledge from EHR. This study is a continuation of previous work in which EHRs were exploited to make predictions about patients with respiratory diseases. In this study, we will try to predict the recurrence of patients with respiratory diseases using four different machine learning algorithms. ; Centro de Investigación de Galicia CITIC and Campus Innova (agreement I+D+ 2019-20) is funded by Consellería de Educación, Universidade e Formación Profesional from Xunta de Galicia and European Union (European Regional Development Fund - FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01 and Universidade da Coruña. Partially supported by the Spanish Ministry of Science (Challenges of Society 2019) PID2019-104323RB-C33 ; Xunta de Galicia; ED431G 2019/01

[Abstract] The exploitation of electronic health records (EHRs) has multiple utilities, from predictive tasks and clinical decision support to pattern recognition. Artificial Intelligence (AI) allows to extract knowledge from EHR data in a practical way. In this study, we aim to construct a Machine Learning model from EHR data to make predictions about patients. Specifically, we will focus our analysis on patients suffering from respiratory problems. Then, we will try to predict whether those patients will have a relapse in less than 6, 12 or 18 months. The main objective is to identify the characteristics that seem to increase the relapse risk. At the same time, we propose an exploratory analysis in search of hidden patterns among data. These patterns will help us to classify patients according to their specific conditions for some clinical variables. ; Centro de Investigación de Galicia CITIC is funded by Consellería de Educación, Universidades e Formación Profesional from Xunta de Galicia and European Union (European Regional Development Fund—FEDER Galicia 2014-2020 Program) by grant ED431G 2019/01. Partially supported by the Spanish Ministry of Science (Challenges of Society 2019) PID2019-104323RB-C33 ; Xunta de Galicia; ED431G 2019/01

The complex information encoded into the element connectivity of a system gives rise to the possibility of graphical processing of divisible systems by using the Graph theory. An application in this sense is the quantitative characterization of molecule topologies of drugs, proteins and nucleic acids, in order to build mathematical models as Quantitative Structure - Activity Relationships between the molecules and a specific biological activity. These types of models can predict new drugs, molecular targets and molecular properties of new molecular structures with an important impact on the Drug Discovery, Medicinal Chemistry, Molecular Diagnosis, and Treatment. The current review is focused on the mathematical methods to encode the connectivity information in three types of graphs such as star graphs, spiral graphs and contact networks and three in-house scientific applications dedicated to the calculation of molecular graph topological indices such as S2SNet, CULSPIN and MInD-Prot. In addition, some examples are presented, such as results of this methodology on drugs, proteins and nucleic acids, including the Web implementation of the best molecular prediction models based on graphs. ; This work is supported by "Collaborative Project on Medical Informatics (CIMED)" PI13/00280 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 20l3- 2016 and the European Regional Development Funds (FEDER) and by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. GRC2014/049), the Galician Network for Colorectal Cancer Research (REGICC) (Ref. R2014/039) and by the Galician Network of Drugs R+D REGID (R2014/025) and the European Fund for Regional Development (FEDER) in the European Union.

Abstract
Minor intron–containing genes (MIGs) account for <2% of all human protein–coding genes and are uniquely dependent on the minor spliceosome for proper excision. Despite their low numbers, we surprisingly found a significant enrichment of MIG-encoded proteins (MIG-Ps) in protein–protein interactomes and host factors of positive-sense RNA viruses, including SARS-CoV-1, SARS-CoV-2, MERS coronavirus, and Zika virus. Similarly, we observed a significant enrichment of MIG-Ps in the interactomes and sets of host factors of negative-sense RNA viruses such as Ebola virus, influenza A virus, and the retrovirus HIV-1. We also found an enrichment of MIG-Ps in double-stranded DNA viruses such as Epstein–Barr virus, human papillomavirus, and herpes simplex viruses. In general, MIG-Ps were highly connected and placed in central positions in a network of human–host protein interactions. Moreover, MIG-Ps that interact with viral proteins were enriched with essential genes. We also provide evidence that viral proteins interact with ancestral MIGs that date back to unicellular organisms and are mainly involved in basic cellular functions such as cell cycle, cell division, and signal transduction. Our results suggest that MIG-Ps form a stable, evolutionarily conserved backbone that viruses putatively tap to invade and propagate in human host cells.