Machine learning on normalized protein sequences

<p>Abstract</p> <p>Background</p> <p>Machine learning techniques have been widely applied to biological sequences, e.g. to predict drug resistance in HIV-1 from sequences of drug target proteins and protein functional classes. As deletions and insertions are frequent in biological sequences, a major limitation of current methods is the inability to handle varying sequence lengths.</p> <p>Findings</p> <p>We propose to normalize sequences to uniform length. To this end, we tested one linear and four different non-linear interpolation methods for the normalization of sequence lengths of 19 classification datasets. Classification tasks included prediction of HIV-1 drug resistance from drug target sequences and sequence-based prediction of protein function. We applied random forests to the classification of sequences into "positive" and "negative" samples. Statistical tests showed that the linear interpolation outperforms the non-linear interpolation methods in most of the analyzed datasets, while in a few cases non-linear methods had a small but significant advantage. Compared to other published methods, our prediction scheme leads to an improvement in prediction accuracy by up to 14%.</p> <p>Conclusions</p> <p>We found that machine learning on sequences normalized by simple linear interpolation gave better or at least competitive results compared to state-of-the-art procedures, and thus, is a promising alternative to existing methods, especially for protein sequences of variable length.</p

Applications of Haptic Systems in Virtual Environments: A Brief Review

Haptic systems and virtual environments represent two innovative technologies that have been attractive for the development of applications where the immersion of the user is the main concern. This chapter presents a brief review about applications of haptic systems in virtual environments. Virtual environments will be considered either virtual reality (VR) or augmented reality (AR) by their virtual nature. Even if AR is usually considered an extension of VR, since most of the augmentations of reality are computer graphics, the nature of AR is also virtual and will be taken as a virtual environment. The applications are divided in two main categories, training and assistance. Each category has subsections for the use of haptic systems in virtual environments in education, medicine, and industry. Finally, an alternative category of entertainment is also discussed. Some representative research on each area of application is described to analyze and to discuss which are the trends and challenges related to the applications of haptic systems in virtual environments