A statement of research philosophy is available upon request.
Ph.D. Candidate, Computer Sciences
A detailed description of my research experience, including previous roles, is available in my Curriculum Vitae.
- Probabilistic Methods in machine learning, specifically learning and inference in complex graphical models such as in statistical relational learning
- Biomedical Applications including clinical diagnosis, protein-structure prediction, gene modeling, biomedical-image analysis, and information extraction from biomedical texts
Currently, I am working on ACMI (Automated Crystallographic Map Interpretation). The task of determining protein structures has been a central one to the biological community for several decades. The structure allows biologists to extract information about the underlying biology of a protein, and has implications for various applications such as disease treatment, drug design, and protein design. The most popular method for producing protein structures is by interpreting an electron-density map - a three-dimensional image of a molecule produced through X-ray crystallography. This process, however, remains a resource- intensive and time-consuming task, stunting basic biological research. Thus, the main objective of my thesis is:
Given the electron-density map (3D image) and a primary sequence of a target protein, produce a three- dimensional, physically-feasible, all-atom model of the target protein's structure.
The result of our group's research is ACMI, a probalistic technique for determining protein structures. Prior to ACMI, techniques failed when trying to interpret low-quality images. With ACMI, crystallographers can now obtain complete and accurate structures from these difficult proteins instead of scrapping the project or dedicating months of effort.
Ameet Soni, Craig Bingman, and Jude Shavlik (2010).
Guiding Belief Propagation using Domain Knowledge for Protein-Structure Determination.
Proceedings of the ACM International Conference on Bioinformatics and Computational Biology 2010 (ACM-BCB 2010), Niagara Falls, New York.
Frank DiMaio, Ameet Soni, George Phillips, and Jude Shavlik (2009).
Spherical-Harmonic Decomposition for Molecular Recognition in Electron-Density Maps.
International Journal of Data Mining and Bioinformatics, 3, pp. 205-227. doi: 10.1504/IJDMB.2009.024852 NIHMSID: NIHMS68171 PMCID: PMC2696052
(The paper is in pre-publication form. It is an extension to: DiMaio et al. (BIBM 2007))
Frank DiMaio, Ameet Soni, and Jude Shavlik (2008).
Machine Learning in Structural Biology: Interpreting 3D Protein Images.
In S. Mitra, S. Datta, T. Perkins & G. Michailidis, editors, Introduction to Machine Learning and Bioinformatics, pp. 237-276. Chapman & Hall/CRC Press
Frank DiMaio, Dmitry Kondrashov, Eduard Bitto, Ameet Soni, Craig Bingman, George Phillips, and Jude Shavlik (2007).
Creating Protein Models from Electron-Density Maps using Particle-Filtering Methods.
Bioinformatics, 23, pp. 2851-2858. doi: 10.1093/bioinformatics/btm480 PMCID: PMC2567142
Frank DiMaio, Ameet Soni, George Phillips, and Jude Shavlik (2007).
Improved Methods for Template-Matching in Electron-Density Maps Using Spherical Harmonics.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM'07), Fremont, CA.