Panayotis Tsanakas

DIANA-microT web server: elucidating microRNA functions through target prediction

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

Accurate microRNA target prediction correlates with protein repression levels.

BackgroundMicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease.ResultsDIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction.ConclusionRecently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at http://www.microrna.gr/microT

The autonomous mobile robot SENARIO: a sensor aided intelligent navigation system for powered wheelchairs

The SENARIO project is develoing a sensor-aided intelligent navigation system that provides high-level navigational aid to users of powered wheelchairs. The authors discuss new and improved technologies developed within SENARIO concerning task/path planning, sensing and positioning for indoor mobile robots as well as user interface issues. The autonomous mobile robot SENARIO, supports semi- or fully autonomous navigation. In semi-autonomous mode the system accepts typical motion commands through a voice-activated or standard joystick interface and supports robot motion with obstacle/collision avoidance features. Fully autonomous mode is a superset of semi-autonomous mode with the additional ability to execute autonomously high-level go-to-goal commands. At its current stage, the project has succeeded in fully supporting semi-autonomous navigation, while experiments on the fully autonomous mode are very encouraging.

Optimal Scheduling for UET/UET-UCT Generalizedn-Dimensional Grid Task Graphs

Then-dimensional grid is one of the most representative patterns of data flow in parallel computation. Many scientific algorithms, which require nearest neighbor communication in a lattice space, are modeled by a task graph with the properties of a simple or enhanced grid. The two most frequently used scheduling models for grids are the unit execution time-zero communication delay (UET) and the unit execution time?unit communication time (UET-UCT). In this paper we introduce an enhanced model of then-dimensional grid by adding extra diagonal edges and allowing unequal boundaries for each dimension. For this generalized grid topology we establish the optimal makespan for both cases of UET/UET-UCT grids. Then we give a closed formula that calculates the minimum number of processors required to achieve the optimal makespan. Finally, we propose a low-complexity optimal time and processor scheduling strategy for both cases.

Dynamic dramatization of multimedia story presentations

We describe a novel dynamic dramatization method for narrative presentations. This method accepts as input the original story material, along with a description of its plot written in a special-purpose language. It then analyzes the plot to iden~ interesting dramatic situations in the story. Based on this content analysis, a presentation manager organizes the presentation and enriches it with appropriate multimedia effects. These effects are associated with interesting dramatic situations, and serve to increase suspense and emphasize plot developments in the narrative. Our method can be used for the development of intelligent front-ends to story databases, for directing assistants in computer-based renditions of narrative works, or for real-time direction of interactive entertainment systems. We are integrating this system in an interactive storytelling environment for Greek mythology.

Mixture Density Estimation Based on Maximum Likelihood and Sequential Test Statistics

We address the problem of estimating an unknown probability density function from a sequence of input samples. We approximate the input density with a weighted mixture of a finite number of Gaussian kernels whose parameters and weights we estimate iteratively from the input samples using the Maximum Likelihood (ML) procedure. In order to decide on the correct total number of kernels we employ simple statistical tests involving the mean, variance, and the kurtosis, or fourth moment, of a particular kernel. We demonstrate the validity of our method in handling both pattern classification (stationary) and time series (nonstationary) problems.

Global path planning for autonomous qualitative navigation

We describe a novel global path planning method for autonomous qualitative navigation in indoor environments. Global path planning operates on top of a qualitative map of the environment that describes variations in sensor behavior between adjacent regions in space. The method takes into consideration the global topology of the environment and applies a set of criteria that can minimize the errors in the navigational accuracy of a robotic wheelchair. Our approach uses a modified version of the Dijkstras shortest path algorithm that takes into consideration the curvature of the trajectory and the off-wall distance of the map points. The algorithm computes in real-time a set of optimal paths for reaching the destination. We have tested our global path planning method in simulation in representative indoor environments with above average complexity. Based on these experiments we have determined empirically a set of values for the parameters of the algorithm that almost always lead to the selection of optimal paths in these environments.

Localized qualitative navigation for indoor environments

We describe a novel architecture for indoor navigation, based on qualitative representations of the variations in the interactions between the robot and its environment. We use these representations to localize and guide planning and reaction. The system accepts off-line as input a topological diagram of the environment. It then uses numerical simulation to generate a map, describing qualitative variations in the sensor behavior between adjacent regions in space. An off-line planner stores localized navigation information at each point in the map. During execution, an adaptive controller uses a short-term memory to improve its operation. The qualitative nature of our method, along with the localization performed by the topological planner result in a compact map representation and in linear-time performances for position estimation and path planning during execution. This architecture has been tested in simulation. Our results show that the proposed navigation method is tolerant of sensor inaccuracies, both in obstacle detection and orientation.

LOWER TIME AND PROCESSOR BOUNDS FOR EFFICIENT MAPPING OF UNIFORM DEPENDENCE ALGORITHMS INTO SYSTOLIC ARRAYS

One of the most promising areas of research is the area of automatic parallelization of sequential algorithms, where the primary objective is the execution of the algorithm in optimal parallel lime. For this purpose, methods of detecting and exploiting all inherent parallelism must be devised. Once optimal execution time is ensured, other prerequisites, e.g., the minimization of the number of processing elements (in the case of systolic arrays) or the minimization of the communication overhead (in the case of distributed memory architectures), should be accomplished too. In this paper we study the automatic parallelization of DO(FOR)-loops; we propose an algorithm that partitions the index space into distinct dependence chains and assigns them to different processing elements. We estimate that our method is always optimal in time and, for a specific subclass of nested DO(FOR)-loops, is also optimal in the number of systolic cells.

An attribute grammar approach to high-level automated hardware synthesis

Abstract Attribute grammars have been used extensively in every phase of traditional compiler construction. Since some of these phases have also been used in automated hardware synthesis (hardware compilation), attribute grammars can be effectively adopted to handle the two major tasks of high-level hardware synthesis, operation scheduling and hardware allocation, implementing various algorithms. This paper presents an attribute grammar driven scheduling system, as a more abstract way of handling the whole high-level hardware synthesis task, while maintaining the desired functionality by the utilization of existing and well-tested tools and techniques transferred from traditional compiler construction.