Marcos Martínez-Romero

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.

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.

Diagnostic Knowledge Extraction from MedlinePlus: An Application for Infectious Diseases

In the creation of diagnostic decision support systems (DDSS) it is crucial to have validated and precise knowledge in order to create accurate systems. Typically, medical experts are the source of this knowledge, but it is not always possible to obtain all the desired information from them. Another valuable source could be medical books or articles describing the diagnosis of diseases managed by the DDSS, but again, it is not easy to extract this information. In this paper we present the results of our research, in which we have used Web scraping and a combination of natural language processing techniques to extract diagnostic criteria from MedlinePlus articles about infectious diseases.

BiOSS: A system for biomedical ontology selection

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service.

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.

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.

Ontologies in medicinal chemistry: current status and future challenges.

Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, synthesis and biological evaluation of compounds. However, with this data explosion, the storage, management and analysis of available data to extract relevant information has become even a more complex task that offers challenging research issues to Artificial Intelligence (AI) scientists. Ontologies have emerged in AI as a key tool to formally represent and semantically organize aspects of the real world. Beyond glossaries or thesauri, ontologies facilitate communication between experts and allow the application of computational techniques to extract useful information from available data. In medicinal chemistry, multiple ontologies have been developed during the last years which contain knowledge about chemical compounds and processes of synthesis of pharmaceutical products. This article reviews the principal standards and ontologies in medicinal chemistry, analyzes their main applications and suggests future directions.

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.