My research interest involves applying the cutting-edge techniques of computer science to solve some computationally challenging biological problems. As part of my PhD research I have analyzed different probabilitic approaches to model the three-dimensional structures of protein-based assemblies formed by interactions among protein molecules as it would occur in living cells. Well-predicted structure help to understand the molecular basis of diseases, and assist in effective drug designing. My PhD dissertation can be found here . As a visiting fellow at NIH, I worked on advance machine learning techniques to classify human regulatory elements known as enhancers that are responsible for enhancing the functions of human genome. My works involves to decipher the structural mechanism of different class of enhancers. Here is my Google Scholar
Citations.
Publications
Journal
- Irina Hashmi and Amarda Shehu, idDock+: Integrating Machine Learning in Probabilistic Search for Protein–Protein Docking," Journal of Computational Biology 2015. (IF 2012: 1.564) Full-text
- Irina Hashmi and Amarda Shehu, HopDock: A Probabilistic Search Algorithm for Decoy Sampling
in Protein-protein Docking," Proteome Science 2013. (IF 2012: 2.33) Full-text
- Brian Olson, Irina Hashmi, Kevin Molloy, and Amarda Shehu, "Basin Hopping as a General and versatile optimization framework for the characterization of biological macromolecules," Journal of Artificial Intelligence Research, 2012.
Full-text
- Irina Hashmi, Bahar Akbal-Delibas, Nurit Haspel, and Amarda Shehu, "Guiding Protein Docking with Geometric and Evolutionary Information.," Journal of Bioinformatics and Computational Biology (JBCB) 2012, (IF 2011: 1.063).
Full-text
- Bahar Akbal-Delibas, Irina Hashmi, Amarda Shehu, and Nurit Haspel, "An Evolutionary Conservation Based Method for Refining and Reranking Protein Complex Structures," Journal of Bioinformatics and Computational Biology (JBCB) 2012, (IF 2011: 1.063). Full-text
Conference
- Irina Hashmi, Daniel Veltri, Nadine Kabbani and Amarda Shehu.
"Knowledge-based Search and Multiobjective Filters: Proposed Structural
Models of GPCR Dimerization," ACM BCB, Long Beach, CA, 2014, (Acceptance Rate: 25%). Full-text
- Irina Hashmi and Amarda Shehu, "A Basin Hopping Algorithm for Protein-protein Docking," The IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM), Philadelphia, PA, 2012. (Acceptance rate:20.73% ). Full-text
- Irina Hashmi, Hafiz Md. Hasan Babu, "An Efficient Design of a Reversible
Barrel Shifter," The 23rd IEEE International Conference on VLSI Design
(VLSID), pp.93-98, 2010, (Acceptance Rate: 21.87%). Full-text
Workshop
- Irina Hashmi and Amarda Shehu. "Informatics-driven Protein-protein Dokcing, " ACM BCBW - Comput Struct Biol Workshop (CSBW),
Washington, D.C., 2013. Full-text
- Irina Hashmi, Bahar Akbal-Delibas, Nurit Haspel, and Amarda Shehu.
"Protein Docking with Information on Evolutionary Conserved Interfaces."
In Computational Structural Biology Workshop (CSBW), IEEE International
Conference on Bioinformatics and Biomedicine (IEEE BIBM), Atlanta, GA,
2011, (Acceptance Rate: 40%). Full-text
- Bahar Akbal-Delibas, Irina Hashmi, Amarda Shehu and Nurit Haspel,
"Refinement of Docked Protein Complex Structures Using Evolutionary Traces",
In Computational Structural Biology Workshop (CSBW), IEEE International
Conference on Bioinformatics and Biomedicine (IEEE BIBM), Atlanta, GA,
2011, (Acceptance Rate: 40%). Full-text
Extended Abstract
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Irina Hashmi, and Amarda Shehu, "Protein-protein Docking using Information from Native Interaction Sites." ACM Conference on Bioinf and Comp Biol (BCB), Washington, D.C., September, 2013.
Abstract
- Irina Hashmi, Amarda Shehu, "Sampling Low-energy Protein-protein Configurations with Basin Hopping," Poster Presentation, IEEE BIBM,
Philadelphia, PA, 2012. (Best Poster Award) Abstract
- Irina Hashmi, Bahar Akbal-Delibas, Nurit Haspel, and Amarda Shehu.
"Protein Docking with Information on Evolutionary Conserved Interfaces,"
Poster Presentation, CSBW IEEE BIBM, Atlanta, GA, 2011. Abstract