Sanja Lazarova-Molnar

Protein-protein interaction based on pairwise similarity

BackgroundProtein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable. In this paper, we propose a simple yet effective method to detect PPI based on pairwise similarity and using only the primary structure of the protein. The PPI based on Pairwise Similarity (PPI-PS) method consists of a representation of each protein sequence by a vector of pairwise similarities against large subsequences of amino acids created by a shifting window which passes over concatenated protein training sequences. Each coordinate of this vector is typically the E-value of the Smith-Waterman score. These vectors are then used to compute the kernel matrix which will be exploited in conjunction with support vector machines.ResultsTo assess the ability of the proposed method to recognize the difference between interacted and non-interacted proteins pairs, we applied it on different datasets from the available yeast saccharomyces cerevisiae protein interaction. The proposed method achieved reasonable improvement over the existing state-of-the-art methods for PPI prediction.ConclusionPairwise similarity score provides a relevant measure of similarity between protein sequences. This similarity incorporates biological knowledge about proteins and it is extremely powerful when combined with support vector machine to predict PPI.

A fresh look at majority multiplexing when devices get into the picture

In this paper we present the first detailed analysis of von Neumann multiplexing (vN-MUX) using majority (MAJ) gates of small fan-ins Delta (MAJ-Delta) with respect to the probability of failure of the elementary (nano-)devices. Only gates with small fan-ins have been considered, as gates with large fan-ins do not seem practical (at least in the short term) in future technologies. The extensions from an exact counting algorithm (for gate defects and faults only) to device-level failures will allow us to estimate and characterize MAJ-Delta vN-MUX with respect to device-level malfunctions. The reported results depart significantly from all known gate-level analyses-either theoretical or based on simulations. These should be quite important as providing a detailed picture of the behavior of MAJ-Delta vN-MUX when considering the (unreliability of the elementary) (nano-)devices (as opposed to gate-level only analyses). The main conclusion is that small fan-in gates (and redundancy schemes relying on such gates) are quite promising-in spite of all previous results at gate-level showing the contrary.

A Service-Oriented Middleware for Building Collaborative UAVs

For a while, Unmanned Arial Vehicles (UAVs) use was limited to military applications, however recently UAVs are also used for a wide range of civilian applications. Some of these UAV applications may involve multiple UAVs that must cooperate to achieve a common goal. This kind of applications is termed collaborative UAV applications. This paper investigates the collaborative aspects and challenges of multiple UAV systems. One of the main issues for multiple UAV systems is developing an effective framework to enable the development of software systems for collaborative UAV operations. One possible approach is to rely on service-oriented computing and service-oriented middleware technologies to simplify the development and operations of such applications. This paper discusses how the service-oriented middleware approach can help resolve some of the challenges of developing collaborative UAVs. The paper also proposes a service-oriented middleware architecture that can satisfy the development and operations of such applications.

A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments

Transmembrane helix (TMH) topology prediction is becoming a focal problem in bioinformatics because the structure of TM proteins is difficult to determine using experimental methods. Therefore, methods that can computationally predict the topology of helical membrane proteins are highly desirable. In this paper we introduce TMHindex, a method for detecting TMH segments using only the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index, which is deduced from a combination of the difference in amino acid occurrences in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, a genetic algorithm was employed to find the optimal threshold value for the separation of TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in a dataset consisting of 70 test protein sequences. The sensitivity and specificity for classifying each amino acid in every protein sequence in the dataset was 0.901 and 0.865, respectively. To assess the generality of TMHindex, we also tested the approach on another standard 73-protein 3D helix dataset. TMHindex correctly predicted 91.8% of proteins based on TM segments. The level of the accuracy achieved using TMHindex in comparison to other recent approaches for predicting the topology of TM proteins is a strong argument in favor of our proposed method. Availability: The datasets, software together with supplementary materials are available at: http://faculty.uaeu.ac.ae/nzaki/TMHindex.htm.

Middleware requirements for collaborative unmanned aerial vehicles

With the recent advances in the aircraft technologies, software, sensors, and communications; unmanned aerial vehicles (UAVs) can offer a wide range of applications. Some of these applications may involve multiple UAVs that cooperate and collaborate to achieve a common goal. This kind of applications is termed collaborative UAVs applications. One of the main research topics for multiple UAVs is developing an effective framework to enable the development of software systems for collaborative UAVs operations. One possible approach is to rely on middleware technologies to simplify the development and operations of such applications. This paper discusses the challenges of developing collaborative UAVs applications and how middleware can help resolve some of these challenges. In addition, the paper studies the utilization of service-oriented middleware infrastructures for implementing and operating collaborative UAVs applications. Finally, the paper investigates the collaborative aspect of multiple UAVs and lists the functions needed for service-oriented middleware to satisfy the development and operations of such applications.

What von Neumann did not say about multiplexing beyond gate failures: the gory details

This paper presents an exact reliability analysis of von Neumann multiplexing using majority gates of fan-in Δ = 3, 5, 7, 9, 11, and the corresponding minimum redundancy factors R = 6, 10, 14, 18, 22. Such results are extremely important for a deeper understanding of von Neumann multiplexing (and its variations), especially when considering the expected unreliable behavior of future nano-devices and interconnects. The analysis confirms and augments well-known theoretical results, and is exact as being obtained using exhaustive counting. The extension of the analysis to the device level will allow us to characterize von Neumann multiplexing with respect to device failures for the first time. The results are very timely and are also explaining a strange (non-linear) behavior of von Neuman multiplexing reported two years ago (based on extensive Monte Carlo simulations).

A genetic algorithm to enhance transmembrane helices prediction

A transmembrane helix (TMH) topology prediction is becoming a central problem in bioinformatics because the structure of TM proteins is difficult to determine by experimental means. Therefore, methods which could predict the TMHs topologies computationally are highly desired. In this paper we introduce TMHindex, a method for detecting TMH segments solely by the amino acid sequence information. Each amino acid in a protein sequence is represented by a Compositional Index deduced from a combination of the difference in amino acid appearances in TMH and non-TMH segments in training protein sequences and the amino acid composition information. Furthermore, genetic algorithm was employed to find the optimal threshold value to separate TMH segments from non-TMH segments. The method successfully predicted 376 out of the 378 TMH segments in 70 testing protein sequences. The level of accuracy achieved using TMHindex in comparison to recent methods for predicting the topology of TM proteins is a strong argument in favor of our method.

Modeling Human Decision Behaviors for Accurate Prediction of Project Schedule Duration

Simulation techniques have been widely applied in many disciplines to predict duration and cost of projects. However, as projects grew in size, they also grew in complexity making effective project planning a challenging task. Despite several attempts to achieve accurate predictions, simulation models in use are still considered to be oversimplified. They often fail to cope with uncertainty due to the complex modeling of the high number of interrelated factors. In this paper we propose a simulation model to cope with human resources uncertainty. We use the proxel-based simulation method to analyze and predict duration of project schedules exhibiting high uncertainty and typical human resources reallocation. The proxel-based simulation is an approximate simulation method that is proven to be more precise than discrete-event simulation. To model uncertainty, we introduce a new type of task, state-dependent (floating) task that supports and demonstrates a high degree of uncertainty in human resources allocation. In fact, it allows attributing different probability distributions to the same activity, depending on the team that may perform it. We use software development scheduling to illustrate our approach.

A simulation-based approach to enhancing project schedules by the inclusion of remedial action scenarios

Project schedules are typically defined in relatively strict terms and often rely on well-defined task ordering. Commonly, each task has either a pre-determined duration, or, a minimum, a maximum and a most-likely duration length. In real-life, however, projects are subject to numerous uncertainties. They often impact durations of tasks and may lead to project re-scheduling. In such cases managers need to decide about some remedial action scenario (RAS) to limit the impact of uncertainty on the overall project success. They are usually left clueless on what the most appropriate action to take is. To solve this problem, we propose a novel approach to enhance project schedules by the inclusion of an optimal RAS to be followed when uncertainties occur. This defines the enhanced project schedule model. The particular RAS, modeled by a set of fuzzy rules and selected using proxel-based simulation, becomes an integral part of the enhanced project schedule.

Floating Task: Introducing and Simulating a Higher Degree of Uncertainty in Project Schedules

Despite several attempts to accurately predict duration and cost of projects, simulation models in use are still over simplified and nonrealistic. They often fail to cope with real-life scenarios and uncertainty. In this paper we use the proxel-based simulation method to analyze and predict duration of project schedules exhibiting high uncertainty due to typical on-the-fly human decision behavior. The proxel-based simulation is an approximate simulation method that is more precise than discrete-event simulation. To model uncertainty, we introduce a new type of task, state-dependent (floating) task that supports and demonstrates a high degree of uncertainty and depends on various parameters in the schedule. For example, the probability distribution of the duration of a task can change depending on the team that performs it. Thus, this kind of task can be used to model the frequent re-scheduling in a project. We use software development process to illustrate our approach.