José M. Vázquez-Naya

Artificial Intelligence Techniques for Colorectal Cancer Drug Metabolism: Ontologies and Complex Networks

Colorectal cancer is one of the most frequent types of cancer in the world and generates important social impact. The understanding of the specific metabolism of this disease and the transformations of the specific drugs will allow finding effective prevention, diagnosis and treatment of the colorectal cancer. All the terms that describe the drug metabolism contribute to the construction of ontology in order to help scientists to link the correlated information and to find the most useful data about this topic. The molecular components involved in this metabolism are included in complex network such as metabolic pathways in order to describe all the molecular interactions in the colorectal cancer. The graphical method of processing biological information such as graphs and complex networks leads to the numerical characterization of the colorectal cancer drug metabolic network by using invariant values named topological indices. Thus, this method can help scientists to study the most important elements in the metabolic pathways and the dynamics of the networks during mutations, denaturation or evolution for any type of disease. This review presents the last studies regarding ontology and complex networks of the colorectal cancer drug metabolism and a basic topology characterization of the drug metabolic process sub-ontology from the Gene Ontology.

An approach for the automatic recommendation of ontologies using collaborative knowledge

In recent years, ontologies have become an essential tool to structure and reuse the exponential growth of information in the Web. As the number of publicly available ontologies increases, researchers face the problem of finding the ontology (or ontologies) which provides the best coverage for a particular context. In this paper, we propose an approach to automatically recommend the best ontology for an initial set of terms. The approach is based on measuring the adequacy of the ontology according to three different criteria: (1) How well the ontology covers the given terms, (2) the semantic richness of the ontology and, importantly, (3) the popularity of the ontology in the Web 2.0. In order to evaluate this approach, we implemented a prototype to recommend ontologies in the biomedical domain. Results show the importance of using collaborative knowledge in the field of ontology recommendation.

Ontologies of drug discovery and design for neurology, cardiology and oncology.

The complex diseases in the field of Neurology, Cardiology and Oncology have the most important impact on our society. The theoretical methods are fast and they involve some efficient tools aimed at discovering new active drugs specially designed for these diseases. The ontology of all the items that are linked with the molecule metabolism and the treatment of these diseases gives us the possibility to correlate information from different levels and to discover new relationships between complex diseases such as common drug targets and disease patterns. This review presents the ontologies used to process drug discovery and design in the most common complex diseases.

Design and implementation of a DICOM PACS with secure access via Internet

Among the last new developments in the field of teleradiology, a new system of telediagnostic which allows the remote transmission of digital images via Internet is prevailing. These communications have to be made through mechanisms that guarantee the confidentiality and the integrity of the clinical data, as well as the authenticity of the transmitter. This system is, in many cases, the only way to diagnose the patient pathology in emergency rooms with no radiologist on call. Access to the medical data from any computer is possible through the implantation of a picture archiving and communication system (PACS) with direct acquisition from DICOM equipment and Web technology. So, the radiologist with a computer connected to the WWW (from inside or outside the hospital) has access to the clinical histories and images. Free distribution software (Apache-PHP-MySQL) and PC platforms in WinNT environment has been used. All the medical imaging equipment of a medium size hospital has been connected to be system, integrating them with the medical history data. Restricted access based on privileges were design to to make reports or only to consult data.

A Multi-criteria Approach for Automatic Ontology Recommendation Using Collective Knowledge

Nowadays, ontologies are considered an important tool for knowledge structuring and reusing, especially in domains in which the proper organization and processing of information are critical issues (e.g. biomedicine). In these domains, the number of available ontologies has grown rapidly during the last years. This is very positive because it enables a more effective (or more intelligent) knowledge management. However, it raises a new problem: what ontology should be used for a given task? In this work, an approach for the automatic recommendation of ontologies is proposed. This approach is based on measuring the adequacy of an ontology to a given context according to three independent criteria: (i) the extent to which the ontology covers the context, (ii) the semantic richness of the ontology in the context, and (iii) the popularity of the ontology in the Web 2.0. Results show the importance of using collective knowledge in the fields of ontology evaluation and recommendation.

A genetic algorithms-based approach for optimizing similarity aggregation in ontology matching

Ontology matching consists of finding the semantic relations between different ontologies and is widely recognized as an essential process to achieve an adequate interoperability between people, systems or organizations that use different, overlapping ontologies to represent the same knowledge. There are several techniques to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple similarity measures into a single metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or through general methods that do not provide optimal results. In this paper, a genetic algorithms based approach to aggregate different similarity metrics into a single function is presented. Starting from an initial population of individuals, each one representing a combination of similarity measures, our approach allows to find the combination that provides the optimal matching quality.

Experimental–Computational Study of Carbon Nanotube Effects on Mitochondrial Respiration: In Silico Nano-QSPR Machine Learning Models Based on New Raman Spectra Transform with Markov–Shannon Entropy Invariants

The study of selective toxicity of carbon nanotubes (CNTs) on mitochondria (CNT-mitotoxicity) is of major interest for future biomedical applications. In the current work, the mitochondrial oxygen consumption (E3) is measured under three experimental conditions by exposure to pristine and oxidized CNTs (hydroxylated and carboxylated). Respiratory functional assays showed that the information on the CNT Raman spectroscopy could be useful to predict structural parameters of mitotoxicity induced by CNTs. The in vitro functional assays show that the mitochondrial oxidative phosphorylation by ATP-synthase (or state V3 of respiration) was not perturbed in isolated rat-liver mitochondria. For the first time a star graph (SG) transform of the CNT Raman spectra is proposed in order to obtain the raw information for a nano-QSPR model. Box-Jenkins and perturbation theory operators are used for the SG Shannon entropies. A modified RRegrs methodology is employed to test four regression methods such as multiple linear regression (LM), partial least squares regression (PLS), neural networks regression (NN), and random forest (RF). RF provides the best models to predict the mitochondrial oxygen consumption in the presence of specific CNTs with R2 of 0.998-0.999 and RMSE of 0.0068-0.0133 (training and test subsets). This work is aimed at demonstrating that the SG transform of Raman spectra is useful to encode CNT information, similarly to the SG transform of the blood proteome spectra in cancer or electroencephalograms in epilepsy and also as a prospective chemoinformatics tool for nanorisk assessment. All data files and R object models are available at https://dx.doi.org/10.6084/m9.figshare.3472349 .

Ethical and legal issues in the clinical practice of primary health care.

Since it was conceived, the notion of primary care has been a crucial concept in health services. Most health care is provided at this level and primary care clinicians have an essential role, both in terms of disease prevention and disease management. During the last decades, primary health care has evolved from a traditional paternalistic model, in which patients played the role of passive recipient of care, towards a situation in which patients are partners involved in the decision making-process. This new context opened a considerable number of new ethical and legal aspects, which need to be comprehensively analyzed and discussed in order to preserve the quality of primary health care all around the world. This work reviews the most important ethical and legal issues in primary health care. Legislation issues are explained in the context of the Spanish Health Services.

General Machine Learning Model, Review, and Experimental-Theoretic Study of Magnolol Activity in Enterotoxigenic Induced Oxidative Stress

This study evaluated the antioxidative effects of magnolol based on the mouse model induced by Enterotoxigenic Escherichia coli (E. coli, ETEC). All experimental mice were equally treated with ETEC suspensions (3.45×109 CFU/ml) after oral administration of magnolol for 7 days at the dose of 0, 100, 300 and 500 mg/kg Body Weight (BW), respectively. The oxidative metabolites and antioxidases for each sample (organism of mouse) were determined: Malondialdehyde (MDA), Nitric Oxide (NO), Glutathione (GSH), Myeloperoxidase (MPO), Catalase (CAT), Superoxide Dismutase (SOD), and Glutathione Peroxidase (GPx). In addition, we also determined the corresponding mRNA expressions of CAT, SOD and GPx as well as the Total Antioxidant Capacity (T-AOC). The experiment was completed with a theoretical study that predicts a series of 79 ChEMBL activities of magnolol with 47 proteins in 18 organisms using a Quantitative Structure- Activity Relationship (QSAR) classifier based on the Moving Averages (MAs) of Rcpi descriptors in three types of experimental conditions (biological activity with specific units, protein target and organisms). Six Machine Learning methods from Weka software were tested and the best QSAR classification model was provided by Random Forest with True Positive Rate (TPR) of 0.701 and Area under Receiver Operating Characteristic (AUROC) of 0.790 (test subset, 10-fold crossvalidation). The model is predicting if the new ChEMBL activities are greater or lower than the average values for the magnolol targets in different organisms.

Developing a system for advanced monitoring and intelligent drug administration in critical care units using ontologies

When a patient enters an intensive care unit ICU, either after surgery or due to a serious clinical condition, his vital signs are continually changing, forcing the medical experts to make rapid and complex decisions, which frequently imply modifications on the dosage of drugs being administered. Life of patients at critical units depends largely on the wisdom of such decisions. However, the human factor is sometimes a source of mistakes that lead to incorrect or inaccurate actions. This work presents an expert system based on a domain ontology that acquires the vital parameters from the patient monitor, analyzes them and provides the expert with a recommendation regarding the treatment that should be administered. If the expert agrees, the system modifies the drug infusion rates being supplied at the infusion pumps in order to improve the patients physiological status. The system is being developed at the IMEDIR Center A Coruoa, Spain and it is being tested at the cardiac intensive care unit CICU of the Meixoeiro Hospital Vigo, Spain, which is a specific type of ICU exclusively aimed to treat patients who have underwent heart surgery or that are affected by a serious coronary disorder.