Rich Information Space Research Articles

Systems Patientomics: The virtual in-silico patient

The integration of clinical and molecular sciences with advanced engineering sciences is moving the world towards a new generation of life science where physiological and pathological information from the living human body can be quantitatively described in silico via biocomputing across multiple scales of time and size and through diverse hierarchies of organization – from molecules to cells and organs to individuals. The development of the Virtual Patient will change conventional medicine, which has been based upon experience and expectation, into “predictive medicine” that will have the capacity to develop solutions based upon prior understanding of the dynamic mechanisms and the quantitative logic of human physiology. Drug discovery, medical and welfare apparatus, and clinical trials in silico will improve the development of products with higher efficiency, reliability and safety while reducing cost. They will also impact upon knowledge-intensive industries. Such program aims at playing a key role in this new area, by sharing and generating solutions as well as human resources contributing to establishment of “in silico medicine” as a basis of the predictive medicine within an international framework. In the long-term, computational physiological models will be refined, linked and validated until they are capable of providing essential predictions to clinicians when healthcare decisions need to be made. As the amount of data reinforcing the models grows, predictions will become more and more patient-centred, with models migrating from statistical, average models to physiological and mechanistic models informed by the unique characteristics of the patient. Systems Patientomics will propose new ways of combining this rich patient information space in a highly visual, coherent, meaningful way and of generating new clinical information by blending and fusing existing information, ultimately creating a “Patient Avatar” capable of supporting the medical professional by producing new clinical knowledge emerging from the integration of patient- and population-specific information. Focal points•BenchsideQuite a few experimental biologists, functional and statistical genomics researchers, involved in developing new measurement technology for biology, and even molecular systems biologists, feel that that computational methods are not relevant for their own research goals. For the lion’s share of those cases where these research goals are rationalized by their potential value for predictive, preventive and participatory medicine this is a misconception.•BedsideSystems computational approaches should be a routine part of the clinical arsenal for the diagnosis, planning and executing of therapeutic interventions. This must include the incorporation of relevant training in medical school curricula.•IndustryCollaborations to be developed across the breadth of the stakeholder groups involved in public health, from public health providers and patient groups, to researchers, scientists, funders and industry.•CommunityDevelopment of an ethical and legal framework (establish common rules and principles for data acquisition/sharing/integration/reduction to practice, define how to achieve patient consent, to respond to fears of misuse of provided data, define solutions for data protection/open innovation).•GovernmentsGovernments should faciliate European EHRs adoption for highets quality of patient care.•Regulatory agenciesA bypass fast track route for mobile medical apps can be adopted by the FDA authority by working in close relation with app developers, physicians and healthcare givers promote high standards.

Systems patientomics: the virtual in-silico patient

Abstract The integration of clinical and molecular sciences with advanced engineering sciences is moving the world towards a new generation of life science where physiological and pathological information from the living human body can be quantitatively described in silico via biocomputing across multiple scales of time and size and through diverse hierarchies of organization – from molecules to cells and organs to individuals. The development of the Virtual Patient will change conventional medicine, which has been based upon experience and expectation, into “predictive medicine” that will have the capacity to develop solutions based upon prior understanding of the dynamic mechanisms and the quantitative logic of human physiology. Drug discovery, medical and welfare apparatus, and clinical trials in silico will improve the development of products with higher efficiency, reliability and safety while reducing cost. They will also impact upon knowledge-intensive industries. Such program aims at playing a key role in this new area, by sharing and generating solutions as well as human resources contributing to establishment of “ in silico medicine” as a basis of the predictive medicine within an international framework. In the long-term, computational physiological models will be refined, linked and validated until they are capable of providing essential predictions to clinicians when healthcare decisions need to be made. As the amount of data reinforcing the models grows, predictions will become more and more patient-centred, with models migrating from statistical, average models to physiological and mechanistic models informed by the unique characteristics of the patient. Systems Patientomics will propose new ways of combining this rich patient information space in a highly visual, coherent, meaningful way and of generating new clinical information by blending and fusing existing information, ultimately creating a “Patient Avatar” capable of supporting the medical professional by producing new clinical knowledge emerging from the integration of patient- and population-specific information. Focal points • Benchside Quite a few experimental biologists, functional and statistical genomics researchers, involved in developing new measurement technology for biology, and even molecular systems biologists, feel that that computational methods are not relevant for their own research goals. For the lion’s share of those cases where these research goals are rationalized by their potential value for predictive, preventive and participatory medicine this is a misconception. • Bedside Systems computational approaches should be a routine part of the clinical arsenal for the diagnosis, planning and executing of therapeutic interventions. This must include the incorporation of relevant training in medical school curricula. • Industry Collaborations to be developed across the breadth of the stakeholder groups involved in public health, from public health providers and patient groups, to researchers, scientists, funders and industry. • Community Development of an ethical and legal framework (establish common rules and principles for data acquisition/sharing/integration/reduction to practice, define how to achieve patient consent, to respond to fears of misuse of provided data, define solutions for data protection/open innovation). • Governments Governments should faciliate European EHRs adoption for highets quality of patient care. • Regulatory agencies A bypass fast track route for mobile medical apps can be adopted by the FDA authority by working in close relation with app developers, physicians and healthcare givers promote high standards.

Personal Information Markets AND Privacy: A New Model to Solve the Controversy

From the early days of the information economy, personal data has been its most valuable asset. Despite data protection laws, companies trade per- sonal information and often intrude on the privacy of individuals. As a result, consumers feel out of control and lose trust in electronic environments. Tech- nologists and regulators are struggling to develop solutions that meet business- es' demand for more personal information while maintaining privacy. However, no promising proposals seem to be in sight. We propose a 3-tier personal in- formation market model with privacy. In our model, clear roles, rights and obli- gations for all actors re-establish trust. The 'relationship space' enables data subjects and visible business partners to build trusting relationships. The 'ser- vice space' supports customer relationships with distributed information pro- cessing. The 'rich information space' enables anonymized information ex- change. To transition to this model, we show how existing privacy-enhancing technologies and legal requirements can be integrated.

Personal Information Markets and Privacy: A New Model to Solve the Controversy

From the early days of the information economy, personal data has been its most valuable asset. Despite data protection laws, companies trade personal information and often intrude on the privacy of individuals. As a result, consumers become more and more irritated and concerned. They feel out of control and lose trust in electronic environments. Technologists, economists and regulators are struggling to develop solutions that meet businesses’ demand for more personal information while maintaining privacy. However, no promising proposals seem to be in sight. A vision of how internet economics can accommodate privacy is elusive. We would like to break this helpless cycle and propose a 3-tier market model for personal information markets with privacy. In our model, clear roles, rights and obligations for all actors re-establish trust. The 1st market tier, ‘relationship space’, enables data subjects and visible business partners to build trusting relationships. The 2nd market tier, ‘service space’, supports 1st tier relationships with distributed information processing. The 3rd tier is called ‘rich information space’. Here, anonymized personal information will drive innovative socio-inspired services, and companies can freely exchange non-identified data without restrictions. To transition to this model, we show how existing privacy-enhancing technologies and legal requirements can be integrated.

Resource recommendation in social annotation systems: A linear-weighted hybrid approach

Social annotation systems enable the organization of online resources with user-defined keywords. Collectively these annotations provide a rich information space in which users can discover resources, organize and share their finds, and connect to other users with similar interests. However, the size and complexity of these systems can lead to information overload and reduced utility for users. For these reasons, researchers have sought to apply the techniques of recommender systems to deliver personalized views of social annotation systems. To date, most efforts have concentrated on the problem of tag recommendation – personalized suggestions for possible annotations. Resource recommendation has not received the same systematic evaluation, in part because the task is inherently more complex. In this article, we provide a general formulation for the problem of resource recommendation in social annotation systems that captures these variants, and we evaluate two cases: basic resource recommendation and tag-specific resource recommendation. We also propose a linear-weighted hybrid framework for resource recommendation. Using six real-world datasets, we show that its integrative approach is essential for this recommendation task and provides the most adaptability given the varying data characteristics in different social annotation systems. We find that our algorithm is more effective than other more mathematically-complex techniques and has the additional advantages of flexibility and extensibility.

Discovery of interactive graphs for understanding and searching time-indexed corpora

Rich information spaces (like the Web or scientific publications) are full of “stories”: sets of statements that evolve over time, manifested as, for example, collections of news articles reporting events that relate to an evolving crime investigation, sets of news articles and blog posts accompanying the development of a political election campaign, or sequences of scientific papers on a topic. In this paper, we formulate the problem of discovering such stories as Evolutionary Theme Pattern Discovery, Summary and Exploration (ETP3). We propose a method and a visualisation tool for solving ETP3 by understanding, searching and interacting with such stories and their underlying documents. In contrast to existing approaches, our method concentrates on relational information and on local patterns rather than on the occurrence of individual concepts and global models. In addition, it relies on interactive graphs rather than natural language as the abstracted story representations. Furthermore, we present an evaluation framework. Two real-life case studies are used to illustrate and evaluate the method and tool.

BRAIN GENE ONTOLOGY AND SIMULATION SYSTEM (BGOS) FOR A BETTER UNDERSTANDING OF THE BRAIN

This article presents some preliminary results on a brain-gene ontology project that is concerned with the collection and the presentation, in a form of ontology, of various concepts, facts, data, software simulators, graphs, videos, animations, and other information forms, related to brain functions, brain diseases, their genetic basis and the relationship between all of them. The first version of the brain-gene ontology (BGO) has been completed as a structure and as an initial implementation in the Protégé ontology-building environment. The BGO allows users to: navigate through the rich information space of brain functions and brain diseases, brain related genes and their activities in certain parts of the brain and their relation to brain diseases; to run simulations; to download data that can be used in a software machine learning environment such as WEKA and NeuCom to train prediction or classification models; to visualize relationship information; to add some new information as the BGO has an evolving structure. The BGO is designed to facilitate active learning and research in the areas of bioinformatics, neuroinformatics, information engineering, and knowledge management. Different parts of it can be used by different users, from a school level to postgraduate and PhD student level. A further development of the BGO is discussed, where more data and information will be added, that will include both a higher level information on cognitive functions and consciousness, and a lower level quantum information.

Navigating on handheld displays

Handheld displays leave little space for the visualization and navigation of spatial layouts representing rich information spaces. The most common navigation method for handheld displays is static peephole navigation : The peephole is static and we move the spatial layout behind it (scrolling). A more natural method is dynamic peephole navigation: here, the spatial layout is static and we move the peephole across it. In the experiment reported here, we compared dynamic and static peephole navigation in otherwise similar conditions. Subjects viewed a spatial layout containing two lines on a static display screen. Only a part of the screen---the peephole---was visible. Subjects had to discriminate line length by either moving a dynamic peephole across a static layout of the lines or by moving a dynamic layout behind a static peephole. In both conditions, they used mouse-cursor control to move either the peephole or the lines.Results show significant differences in discrimination performance between conditions when lines are larger than the size of the peephole. Discrimination thresholds for static peephole navigation were 50--75% higher than for dynamic peephole navigation. Furthermore, static peephole navigation took 24% more time than dynamic peephole navigation.

Multimodal Information Exploration

Exploration is a fundamental aspect of learning. In computer-based systems, advanced interfaces to a rich information space can become a key element in this connection. Tools adaptable to different modes of exploring information and to the characteristics of different users are needed. In this article, the integration of hypermedia and natural language dialogue are discussed and reference is made to ALFRESCO, a natural language-centered multimodal system developed at IRST. The discussion is mainly focused on the role and the structure of the communicative context.