Paolo Besana

Istituto Universitario di Studi Superiori di Pavia

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Papers

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Research Field

OWL Model of Clinical Trial Eligibility Criteria Compatible with Partially-known Information.

SWAT4LS (2012)

Olivier Dameron Paolo Besana Oussama Zékri Annabel Bourdé Anita Burgun

Background Clinical trials are important for patients, for researchers and for companies. One of the major bottlenecks is patient recruitment. This task requires the matching of a large volume of information about the patient with numerous eligibility criteria, in a logically-complex combination. Moreover, some of the patient’s information necessary to determine the status of the eligibility criteria may not be available at the time of pre-screening.

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Implementation of the Ontology Matching Component

Pavel Shvaiko Fausto Giunchiglia Mikalai Yatskevich Juan Pane Paolo Besana

This deliverable provides a brief documentation for the ontology matching component implementation. Specifically, it discusses (i) the purpose and functionality of the component, (ii) its usage example, and finally (iii) plans for its future development.

Component (thermodynamics)

Deliverable

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Data‐Intensive Trends

Malcolm Atkinson Paolo Besana

This chapter commences with a review of what the authors have learnt about data-intensive methods and their potential power. Data-intensive applications are challenging in different dimensions: the heterogeneity of the applications, the volume of data, the complexity and heterogeneity of the data being processed, the statistical sensitivity required, and so on. They are and will remain demanding in skills for choosing, handling, interpreting, and presenting data. These critical skills are already in short supply and without well-targeted action, shortages of these skills deprive countries, companies, and societies of the potential benefits from data. Controlled Vocabulary Terms statistical data

Economic shortage

10.1002/9781118540343.ch23

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Probabilistic dialogue models for dynamic ontology mapping

Paolo Besana Dave Robertson

Agents need to communicate in order to accomplish tasks that they are unable to perform alone. Communication requires agents to share a common ontology, a strong assumption in open environments where agents from different backgrounds meet briefly, making it impossible to map all the ontologies in advance. An agent, when it receives a message, needs to compare the foreign terms in the message with all the terms in its own local ontology, searching for the most similar one. However, the content of a message may be described using an interaction model: the entities to which the terms refer are correlated with other entities in the interaction, and they may also have prior probabilities determined by earlier, similar interactions. Within the context of an interaction it is possible to predict the set of possible entities a received message may contain, and it is possible to sacrifice recall for efficiency by comparing the foreign terms only with the most probable local ones. This allows a novel form of dynamic ontology matching.

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Citations (2)

Exploiting interaction contexts in P2P ontology mapping.

Paolo Besana Dave Robertson Michael Rovatsos

Agents in peer-to-peer networks may gather into virtual communities, interacting continuously with agents that represent disparate actors, each of them with different interests, needs and views, and having dissimilar ontologies. Mapping all the combination of ontologies in advance is not feasible simply because all the possible combinations cannot be foreseen. Mapping complete ontologies at run time is a computationally expensive task. The framework proposed in this paper maps only the terms encountered in a dialogue, or those needed to map them. The efficiency in the mapping process is increased by accumulating experience and exploiting it in order to reduce the number of mapping candidates to verify, focusing only on the most likely ones.

Semantic mapping

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Citations (15)

OpenKnowledge for peer-to-peer experimentation in protein identification by MS/MS

Automated Experimentation (2011)

Siu-wai Leung Xueping Quan Paolo Besana Qian Li Mark O. Collins

Traditional scientific workflow platforms usually run individual experiments with little evaluation and analysis of performance as required by automated experimentation in which scientists are being allowed to access numerous applicable workflows rather than being committed to a single one. Experimental protocols and data under a peer-to-peer environment could potentially be shared freely without any single point of authority to dictate how experiments should be run. In such environment it is necessary to have mechanisms by which each individual scientist (peer) can assess, locally, how he or she wants to be involved with others in experiments. This study aims to implement and demonstrate simple peer ranking under the OpenKnowledge peer-to-peer infrastructure by both simulated and real-world bioinformatics experiments involving multi-agent interactions.A simulated experiment environment with a peer ranking capability was specified by the Lightweight Coordination Calculus (LCC) and automatically executed under the OpenKnowledge infrastructure. The peers such as MS/MS protein identification services (including web-enabled and independent programs) were made accessible as OpenKnowledge Components (OKCs) for automated execution as peers in the experiments. The performance of the peers in these automated experiments was monitored and evaluated by simple peer ranking algorithms.Peer ranking experiments with simulated peers exhibited characteristic behaviours, e.g., power law effect (a few dominant peers dominate), similar to that observed in the traditional Web. Real-world experiments were run using an interaction model in LCC involving two different types of MS/MS protein identification peers, viz., peptide fragment fingerprinting (PFF) and de novo sequencing with another peer ranking algorithm simply based on counting the successful and failed runs. This study demonstrated a novel integration and useful evaluation of specific proteomic peers and found MASCOT to be a dominant peer as judged by peer ranking.The simulated and real-world experiments in the present study demonstrated that the OpenKnowledge infrastructure with peer ranking capability can serve as an evaluative environment for automated experimentation.

Identification

10.1186/1759-4499-3-3

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Citations (1)

Using Semantic Web Technologies for Clinical Trial Recruitment

Lecture notes in computer science (2010)

Paolo Besana Marc Cuggia Oussama Zékri Annabel Bourdé Anita Burgun

Patient recruitment

10.1007/978-3-642-17749-1_3

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Citations (30)

Towards Decentralised Clinical Decision Support Systems