Francisco J. Novoa

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 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.

Automatic multiscale vascular image segmentation algorithm for coronary angiography

Abstract Cardiovascular diseases, particularly severe stenosis, are the main cause of death in the western world. The primary method of diagnosis, considered to be the standard in the detection and quantification of stenotic lesions, is a coronary angiography. This article proposes a new automatic multiscale segmentation algorithm for the study of coronary trees that offers results comparable to the best existing semi-automatic method. According to the state-of-the-art, a representative number of coronary angiography images that ensures the generalisation capacity of the algorithm has been used. All these images were selected by clinics from an Haemodynamics Unit. An exhaustive statistical analysis was performed in terms of sensitivity, specificity and Jaccard. Algorithm improvements imply that the clinician can perform tests on the patient and, bypassing the images through the system, can verify, in that moment, the intervention of existing differences in a coronary tree from a previous test, in such a way that it could change its clinical intra-intervention criteria.

Advancing Network Flow Information Using Collaborative Filtering

Collaborative Filtering algorithms are frequently employed in e-commerce. However, this kind of algorithms can also be useful in other domains. In an information system thousands of bytes are sent through the network every second. Analyzing this data can require too much time and many resources, but it is necessary for ensuring the right operation of the network. Results are used for profiling, security analysis, traffic engineering and many other purposes. Nowadays, as a complement to a deep inspection of the data, it is more and more common to monitor packet flows, since it consumes less resources and it allows to react faster to any network situation. In a typical ow monitoring system, flows are exported to a collector, which stores the information before being analyzed. However, many collectors work based on time slots, so they do not analyze the flows when they are just received, generating a delay. In this work we demonstrate how Collaborative Filtering algorithms can be applied to this new domain. ...

Using Collaborative Filtering in a new domain: traffic analysis

The importance of information systems is increasing every day. In order to ensure their right operation, it is necessary to analyze a huge amount of traffic generated by different devices. However, classical techniques for operation and management are reactive and not proactive, what can evolve in a failure in the system. In this work we propose a new approach where we analyze network traffic using Collaborative Filtering. In other domains, these systems have proved to filter thousands of items according to user needs and tastes. They can predict user preferences and recommend relevant items for the user. In this sense, in this new domain, relevant items are data flows, so our goal is to recommend flows which are related to the traffic already captured.

Efficiency in the transmission of information through digital imaging and communications in medicine using security mechanisms: tests with DISCUS.

This article describes our experience in using a Picture Archiving and Communications System, known as Secure Medical Image Information System, based on the Digital Imaging and Communications in Medicine standard that supports the use of secure transmissions, from the point of view of how the use of secure sending methods has an effect on the efficiency in the transmission according to the network employed, to quantify productivity loss due to the encryption, the secure transmission, and the subsequent decryption. To test the Secure Medical Image Information System, a series of medical data transmission were conducted from A Coruña (Spain) to the Virgen de las Nieves Hospital, situated 1,000 km away, in Granada (Spain). Once we studied the networking infrastructure of the hospital and its available image generation devices, we subsequently carried out a series of measurements during the transmissions, which allowed us to analyze the behavior of the system with different network schemes and connection speeds. The results obtained from these investigations demonstrate that the impact of secure data-sending methods on the productivity of the system is higher in networks whose capacities are higher and it is not affected by sending data during different periods in the day. In this regard, the presented approach may serve as a model for other small, and possibly mid-sized, medical centers.