Kire Trivodaliev

Comparative Analysis of Three Efficient Approaches for Retrieving Protein 3D Structures

In this paper, comparative analysis is presented of our three 3D structure-based approaches for the efficient retrieval of protein molecules. All approaches rely on the 3D structure of the proteins. In the first approach, the Spherical Trace Transform is applied to protein 3D structures in order to produce geometry based descriptors. Additionally, some biological properties of the protein are taken, thus forming better integrated descriptor. In the second approach, some modification of the ray based descriptor is applied on the backbone of the protein molecule. In the third approach, wavelet transformation is applied on the distance matrix of the Calpha atoms which form the backbone of the protein. The SCOP database was used to evaluate the retrieval accuracy. We provide some experimental results of the retrieval accuracy of our three approaches. The results show that the ray based approach gives the best retrieval accuracy (97,5%), while it is simpler and faster than the other two approaches.

Internet of Things Framework for Home Care Systems

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency.

Protein Classification by Matching 3D Structures

In this paper, a 3D structure-based approach is presented for the efficient classification of protein molecules. The method relies on the geometric 3D structure of the proteins. After proper positioning of the 3D structures, the spherical trace transform is applied to them to produce geometry - based descriptors, which are completely rotation invariant. Additionally, some biological properties of the protein are taken, and added to the geometry-based descriptor, thus forming better integrated descriptor. We have used nearest neighbour classification on the previously extracted descriptors. A part of the FSSP/DALI database, was used to evaluate the classification accuracy of this system. The results show that this method achieves more than 92 percent classification accuracy while it is simpler and faster than the DALI method. We provide some experimental results of the implemented system.

Protein Function Prediction Based on Neighborhood Profiles

The recent advent of high throughput methods has generated large amounts of protein interaction network (PIN) data. A significant number of proteins in such networks remain uncharacterized and predicting their function remains a major challenge. A number of existing techniques assume that proteins with similar functions are topologically close in the network. Our hypothesis is that the simultaneous activity of sometimes functionally diverse functional agents comprises higher level processes in different regions of the PIN. We propose a two-phase approach. First we extract the neighborhood profile of a protein using Random Walks with Restarts. We then employ a “chi-square method”, which assigns k functions to an uncharacterized protein, with the k largest chi-square scores. We applied our method on protein physical interaction data and protein complex data, which showed the later perform better. We performed leave-one-out validation to measure the accuracy of the predictions, revealing significant improvements over previous techniques.

Protein Secondary Structure Prediction Method Based on Neural Networks

Protein secondary structure prediction remains an open and important problem in life sciences as a first step towards the crucial tertiary structure prediction. In [3], a protein secondary structure prediction algorithm called PSIPRED presents an innovative approach - feeding the neural network (NN) with a position specific scoring matrix as input data. Starting from this idea, in this paper we propose a method based on breaking down the single first level NN classifier, into three separate ones, for each of the secondary structure elements (SSE) types, in order to achieve greater generalization qualities of the first level classifying algorithm. We also introduce the use of sparsely connected feed-forward NNs, instead of the classic fully interconnected one. This network architecture gains considerable speed improvements (for both the training and the testing part of the algorithm) by omitting the most of the remote units that have the poorest influence on the selected amino acid. The prediction results are encouraging - the predictions are similar to the target PDB data and we achieve better accuracy, compared to the predictions obtained from the original PSIPRED algorithm.

Toby the explorer — An interactive educational game for primary school pupils

Technology enhanced education has been recently established as a new approach for all stages of education in developing countries, especially in Macedonia. Although computer games are often given little attention we believe that within the vast amount of technologies and instruments used to achieve the needed improvements it is computer games that are playing the central role in delivering the desired effects, in particular to children and teenagers. In this paper we present the “Toby the Explorer”, an interactive educational game for primary school students. We show in depth the engineering behind the game, its design and structure. We also give an insight and evaluation of the importance of the game in enhancing the educational process. Our results show that the learning process is regarded as more easy and fun by the students that learned by playing, but also it increased their interest in learning other subjects not included explicitly in the game.

Novel Gene Ontology Based Distance Metric for Function Prediction via Clustering in Protein Interaction Networks

The increased availability of large-scale protein-protein interaction (PPI) data has made it possible to have a network level understanding of the basic components and organization of the cell machinery. A significant number of proteins in protein interaction networks (PIN) remain uncharacterized and predicting their function remains a major challenge. We propose a novel distance metric for PIN clustering. First we augment the graph representing the PIN with weights derived from Gene Ontology (GO) semantic similarity and we use this augmented representation in a random walk with restarts (RWR) process. The distance between a pair of proteins is calculated from the steady state distribution of the RWR. We validate our approach by function prediction via clustering in a purified and reliable Saccharomyces cerevisiae PIN. We show that the rise of function prediction performance when using the novel distance metric is significant, as compared to traditional approaches.

SHOPIN: Semantic Homogeneity Optimization in Protein Interaction Networks

Protein interaction networks (PINs) are argued to be the richest source of hidden knowledge of the intrinsic physical and/or functional meanings of the involved proteins. We propose a novel method for computational protein function prediction based on semantic homogeneity optimization in PIN (SHOPIN). The SHOPIN method creates graph representations of the PIN augmented by inclusion of the semantics of the proteins and their interacting contexts. Network wide semantic relationships, modeled using random walks, are used to map the augmented PIN graphs in a new semantic metric space. The method produces a hierarchical partitioning of the PIN optimal in terms of semantic homogeneity by iterative optimization of the ratio of between clusters dissimilarities and within clusters similarities in the new semantic metric space. Function prediction is done using cluster wide-hierarchy high function enrichment. Results validate the rationale of the SHOPIN method placing it right next to state-of-the-art approaches performance wise.

Intelligent analysis of economic parameters of European countries

Engineers today are often bound to work with complex data, with valuable knowledge to be extracted and models to be built for predicting future behavior. With a given set of economic parameters for the European countries, the goal of this paper is to analyze if they can form meaningful community structure. To this end, we propose a network science approach to economic time series analysis. The first step is the creation of corresponding graphs as the most adequate data representation that can capture multiple relations and dependencies among entities of interest. The preprocessing steps for creating a graph include calculating correlation coefficients for the parameters, which are used for weighing the graph. The weighted graphs are then analyzed for their underlying structure using clustering algorithms which produce various communities depending on the settings employed. Experiments are performed using different correlation calculations for graph weighing and different settings for cluster extraction and the produced communities are analyzed in terms of their quality, both in view of modularity score and economic meaning. Results show that the proposed approach successfully captures meaningful economic relationships between European countries and can be a solid base on which future, more complex analysis can be build.

Deep Learning the Protein Function in Protein Interaction Networks

One of the essential challenges in proteomics is the computational function prediction. In Protein Interaction Networks (PINs) this problem is one of proper labeling of corresponding nodes. In this paper a novel three-step approach for supervised protein function learning in PINs is proposed. The first step derives continuous vector representation for the PIN nodes using semi-supervised learning. The vectors are constructed so that they maximize the likelihood of preservation of the graph topology locally and globally. The next step is to binarize the PIN graph nodes (proteins) i.e. for each protein function derived from Gene Ontology (GO) determine the positive and negative set of nodes. The challenge of determining the negative node sets is solved by random walking the GO acyclic graph weighted by a semantic similarity metric. A simple deep learning six-layer model is built for the protein function learning as the final step. Experiments are performed using a highly reliable human protein interaction network. Results indicate that the proposed approach can be very successful in determining protein function since the Area Under the Curve values are high (>0.79) even though the experimental setup is very simple, and its performance is comparable with state-of-the-art competing methods.