Ahed Elmsallati

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About Me

I am a Ph.D. candidate at CU Colorado Springs's Department of Computer Science.I am affiliated with the Language, Information, and Computation Lab (LINC Lab), LINC Lab involved in research in artificial intelligence, natural language processing, information retrieval and bioinformatics. Now, I am working under the supervision of Dr. Jugal Kalita

Research Interests

Broadly, I am interested in data structure and algorithms, databases, and large graphs and their algorithms. Specifically, Data representation as graphs, graph indices, graph matching and alignments, graph mining, and graph queries are some examples of my research interest. My current research concentrates on the alignment of biological networks. To be more specific, I am now working on the problem of global pairwise alignment for Protein-Protein-Interaction (PPI) networks.

Current Research

My current research foucses on alignment of biological networks. To be more specific, I am working on global parwise alignment for Protein-Protein-Interaction (PPI) networks. Here is a brife introduction about the challenge that we are considering at our lab:
The challenge that I consider in my current research is the problem of global alignment of PPI networks. Global network alignment, in general, tries to uniquely find the best mapping for a node in one network to only one node in another network. The mapping is performed according to some matching criteria that depend on the nature of data.

In molecular biology, functional orthologs, protein complexes, and evolutionarily conserved pathways are some examples of information uncovered by global network alignment. Since most formulations of the network alignment problem are known to be NP-complete, which means that there is no exact solution can be used to obtain an optimal alignment in a tractable amount of time in the general case. Hence, researchers have to approximate the solution. All algorithms presented are usually evaluated on well-known networks that have been aligned manually or using synthetic datasets where the exact alignment is known.

Publications

Journal Papers

• [J1]

  Ahed Elmsallati, Connor Clark, and Jugal Kalita Global Alignment of Protein-Protein Interaction Networks: A Survey, IEEE, Transactions on Computational Biology and Bioinformatics (TCBB) ( Volume: 13, Issue: 4, July-Aug. 1 2016 ) Show Abstract Abstract In this paper, we survey algorithms that perform global alignment of networks or graphs. Global network alignment aligns two or more given networks to find the best mapping from nodes in one network to nodes in other networks. Since graphs are a common method of data representation, graph alignment has become important with many significant applications. Proteinprotein interactions can be modeled as networks and aligning these networks of protein interactions has many applications in biological research. In this survey, we review algorithms for global pairwise alignment highlighting various proposed approaches, and classify them based on their methodology. Evaluation metrics that are used to measure the quality of the resulting alignments are also surveyed. We discuss and present a comparison between selected aligners on the same datasets and evaluate using the same evaluation metrics. Finally, a quick overview of the most popular databases of protein interaction networks is presented focusing on datasets that have been used recently.

• [J2]

  Ahed Elmsallati, Abdulghani Msalati, and Jugal Kalita Index-Based Network Aligner of Protein-Protein Interaction Networks, IEEE, Transactions on Computational Biology and Bioinformatics (TCBB) ( Volume: preprint, Issue: XX, Sep 2016 ) (IBNAL's implementation) Show Abstract Abstract Network Alignment over graph-structured data has received considerable attention in many recent applications. Global network alignment tries to uniquely find the best mapping for a node in one network to only one node in another network. The mapping is performed according to some matching criteria that depend on the nature of data. In molecular biology, functional orthologs, protein complexes and evolutionary conserved pathways are some examples of information uncovered by global network alignment. Current techniques for global network alignment suffer from several drawbacks, e.g., poor performance and high memory requirements.We address these problems by proposing IBNAL, Indexes-Based Network ALigner, for better alignment quality and faster results. To accelerate the alignment step, IBNAL makes use of a novel clique-based index and is able to align large networks in seconds. IBNAL produces a higher topological quality alignment and comparable biological match in alignment relative to other state-of-the-art aligners even though topological fit is primarily used to match nodes. IBNAL’s results confirm and give another evidence that homology information is more likely to be encoded in network topology than sequence information.

Conference Papers

• [C1]

  Ahed Elmsallati, Swarup Roy, and Jugal Kalita Exploring Symmetric Substructures in Protein Interaction Networks for Pairwise Alignment, IWBBIO 2017(5th International Work-Conference on Bioinformatics and Biomedical Engineering). ( to appear ) Show Abstract Abstract In molecular biology, comparison of multiple Protein Protein Interaction (PPI) networks to extract subnetworks that are conserved during evolution across dierent species is helpful for studying complex cellular machinery. Most eorts produce promising results in creating alignments that show large regions of biological or topological similarity between the PPI networks of various species, but few do both. We present a new pairwise aligner SSAlign (Symmetric Substructure Alignment) that extracts maximal substructures from participating PPI networks and uses Gene Ontology Consistency (GOC) as the graph isomorphic function for aligning two subgraphs. We use PPI networks from Isobase data repository for experiments and comparisons. Our results show that in comparison to other contemporary aligners, SSAlign is better at aligning topologically and biologically similar subnetworks.