Kernel methods in genomics and computational biology

Support vector machines and kernel methods are increasingly popular in genomics and computational biology, due to their good performance in real-world applications and strong modularity that makes them suitable to a wide range of problems, from the classification of tumors to the automatic annotation of proteins. Their ability to work in high dimension, to process non-vectorial data, and the natural framework they provide to integrate heterogeneous data are particularly relevant to various problems arising in computational biology. In this chapter we survey some of the most prominent applications published so far, highlighting the particular developments in kernel methods triggered by problems in biology, and mention a few promising research directions likely to expand in the future

Evolutionary Computation and QSAR Research

[Abstract] The successful high throughput screening of molecule libraries for a specific biological property is one of the main improvements in drug discovery. The virtual molecular filtering and screening relies greatly on quantitative structure-activity relationship (QSAR) analysis, a mathematical model that correlates the activity of a molecule with molecular descriptors. QSAR models have the potential to reduce the costly failure of drug candidates in advanced (clinical) stages by filtering combinatorial libraries, eliminating candidates with a predicted toxic effect and poor pharmacokinetic profiles, and reducing the number of experiments. To obtain a predictive and reliable QSAR model, scientists use methods from various fields such as molecular modeling, pattern recognition, machine learning or artificial intelligence. QSAR modeling relies on three main steps: molecular structure codification into molecular descriptors, selection of relevant variables in the context of the analyzed activity, and search of the optimal mathematical model that correlates the molecular descriptors with a specific activity. Since a variety of techniques from statistics and artificial intelligence can aid variable selection and model building steps, this review focuses on the evolutionary computation methods supporting these tasks. Thus, this review explains the basic of the genetic algorithms and genetic programming as evolutionary computation approaches, the selection methods for high-dimensional data in QSAR, the methods to build QSAR models, the current evolutionary feature selection methods and applications in QSAR and the future trend on the joint or multi-task feature selection methods.Instituto de Salud Carlos III, PIO52048Instituto de Salud Carlos III, RD07/0067/0005Ministerio de Industria, Comercio y Turismo; TSI-020110-2009-53)Galicia. Consellería de Economía e Industria; 10SIN105004P

Investigation of mobile devices usage and mobile augmented reality applications among older people

Mobile devices such as tablets and smartphones have allow users to communicate, entertainment, access information and perform productivity. However, older people are having issues to utilise mobile devices that may affect their quality of life and wellbeing. There are some potentials of mobile Augmented Reality (AR) applications to increase older users mobile usage by enhancing their experience and learning. The study aims to investigate mobile devices potential barriers and influence factors in using mobile devices. It also seeks to understand older people issues in using AR applications

Promoting Academic Entrepreneurship in Europe and the United States: Creating an Intellectual Property Regime to Facilitate the Efficient Transfer of Knowledge from the Lab to the Patient

In 2014, the European Commission announced the launch of a study of knowledge transfer by public research organizations and other institutes of higher learning “to determine which additional measures might be needed to ensure an optimal flow of knowledge between the public research organisations and business thereby contributing to the development of the knowledge based economy.” As the European Commission has recognized, the European Union (“EU”) needs to take action to “unlock the potential of IPRs [intellectual property rights] that lie dormant in universities, research institutes and companies.” This article builds on our earlier work on structuring efficient pharmaceutical public-private partnerships (“PPPPs”), but focuses on the regulatory infrastructure necessary to support the efficient commercialization of publicly funded university medical research in both the European Union and the United States (“U.S.”). Our comparative analysis of the EU and U.S. approaches to translational medicine shows that there are lessons to be shared. The EU can apply the experiences from the U.S. Bayh-Dole Act and PPPPs in the United States, and the United States can emulate certain of the open innovation aspects of the European Innovative Medicines Initiative and the tighter patenting standards imposed by the European Patent Office. Thus, a secondary purpose of this article is suggesting amendments to the U.S. laws governing the patenting and licensing of government-funded technology to prevent undue burdens on the sharing of certain upstream medical discoveries and research tools

Renewables and Innovation - Empirical Assessment and Theoretical Considerations

This study is about structural change in the energy system. In a first step an econometric model is presented and in a second step diffusion of GTs is embedded theoretically. By focusing on different green technology industries (GT sector) in Germany, we analyze how policy induced demand stimulates innovation. Taking the size of the market as a proxy for demand and patent counts as a proxy for innovation, we find support that the presence of institutions enabling diffusion of GTs are correlated with innovative activity. Public R&D expenditures also play a significant role. We additionally control for a structural break by comparing the two institutional settings incorporated into the legal system in Germany, namely the Stromeinspeisegesetz (SEG) and the Erneuerbare Energiengesetz (EEG). We cannot find support for the supposition that innovative activity significantly differs for diffusion under the SEG and EEG. The empirical findings also show that electricity prices are not the driving force for innovative activity within the GT sector. The discussion at the end of the paper comes to the result that diffusion of GTs - under the EEG - is difficult to be justified theoretically.Renewable Energies, Demand Pull, Structural Change

Automated data integration for developmental biological research

In an era exploding with genome-scale data, a major challenge for developmental biologists is how to extract significant clues from these publicly available data to benefit our studies of individual genes, and how to use them to improve our understanding of development at a systems level. Several studies have successfully demonstrated new approaches to classic developmental questions by computationally integrating various genome-wide data sets. Such computational approaches have shown great potential for facilitating research: instead of testing 20,000 genes, researchers might test 200 to the same effect. We discuss the nature and state of this art as it applies to developmental research