Ameet Soni

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MOTIVATION One bottleneck in high-throughput protein crystallography is interpreting an electron-density map, that is, fitting a molecular model to the 3D picture crystallography produces. Previously, we developed ACMI (Automatic Crystallographic Map Interpreter), an algorithm that uses a probabilistic model to infer an accurate protein backbone layout.(More)
i For my parents, who nurtured my curiosity, and for Natalie, who now piques it. ii ABSTRACT Supervised machine learning is a branch of artificial intelligence concerned with automatically inducing predictive models from labeled data. Such learning approaches are useful for many interesting real-world applications, but particularly shine for tasks involving(More)
An important problem in high-throughput protein crystallography is constructing a protein model from an electron-density map. Previous work by some of this pa-per's authors [1] describes an automated approach to this otherwise time-consuming process. An important step in the previous method requires searching the density map for many small template(More)
In line with institutions across the United States, the Computer Science Department at Swarthmore College has faced the challenge of maintaining a demographic composition of students that matches the student body as a whole. To combat this trend, our department has made a concerted effort to revamp our introductory course sequence to both attract and retain(More)
Several methods for automatically constructing a protein model from an electron-density map require searching for many small protein-fragment templates in the density. We propose to use the spherical-harmonic decomposition of the template and the maps density to speed this matching. Unlike other template-matching approaches, this allows us to eliminate(More)
A major bottleneck in high-throughput protein crystallography is producing protein-structure models from an electron-density map. In previous work, we developed Acmi, a probabilistic framework for sampling all-atom protein-structure models. Acmi uses a fully connected, pairwise Markov random field to model the 3D location of each non-hydrogen atom in a(More)
i There are many people I would like to thank for helping me throughout my graduate career. I want to thank my advisor, David Page. David has been steadfast in his support for me throughout my tenure. His advice, mentorship, and feedback have improved the way I understand and approach my research. I am forever grateful to him for guiding me to this point(More)
We consider the task of KBP slot filling – extracting relation information from newswire documents for knowledge base construction. We present our pipeline, which employs Relational Dependency Networks (RDNs) to learn linguistic patterns for relation extraction. Additionally, we demonstrate how several components such as weak supervision, word2vec features(More)
We present a comparison of weak and distant supervision methods for producing proxy examples for supervised relation extraction. We find that knowledge-based weak supervision tends to outperform popular distance supervision techniques, providing a higher yield of positive examples and more accurate models.